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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03045">arXiv:2408.03045</a> <span> [<a href="https://arxiv.org/pdf/2408.03045">pdf</a>, <a href="https://arxiv.org/format/2408.03045">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Coherent FDA Radar: Transmitter and Receiver Design and Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yan Sun</a>, <a href="/search/eess?searchtype=author&query=Jia%2C+M">Ming-jie Jia</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+W">Wen-qin Wang</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria Sabrina Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shunsheng Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.03045v1-abstract-short" style="display: inline;"> The combination of frequency diverse array (FDA) radar technology with the multiple input multiple output (MIMO) radar architecture and waveform diversity techniques potentially promises a high integration gain with respect to conventional phased array (PA) radars. In this paper, we propose an approach to the design of the transmitter and the receiver of a coherent FDA (C-FDA) radar, that enables… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03045v1-abstract-full').style.display = 'inline'; document.getElementById('2408.03045v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03045v1-abstract-full" style="display: none;"> The combination of frequency diverse array (FDA) radar technology with the multiple input multiple output (MIMO) radar architecture and waveform diversity techniques potentially promises a high integration gain with respect to conventional phased array (PA) radars. In this paper, we propose an approach to the design of the transmitter and the receiver of a coherent FDA (C-FDA) radar, that enables it to perform the demodulation with spectral overlapping, due to the small frequency offset. To this purpose, we derive the generalized space-time-range signal model and we prove that the proposed C-FDA radar has a higher coherent array gain than a PA radar, and at the same time, it effectively resolves the secondary range-ambiguous (SRA) problem of FDA-MIMO radar, allowing for mainlobe interference suppression and range-ambiguous clutter suppression. Numerical analysis results prove the effectiveness of the proposed C-FDA radar in terms on anti-interference and anti-clutter capabilities over conventional radars. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03045v1-abstract-full').style.display = 'none'; document.getElementById('2408.03045v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.16675">arXiv:2402.16675</a> <span> [<a href="https://arxiv.org/pdf/2402.16675">pdf</a>, <a href="https://arxiv.org/ps/2402.16675">ps</a>, <a href="https://arxiv.org/format/2402.16675">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Integrated MIMO Passive Radar Target Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zaimbashi%2C+A">Amir Zaimbashi</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria Sabrina Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.16675v1-abstract-short" style="display: inline;"> Integrated passive radar (IPR) can be regarded as next generation passive radar technology, which aims to integrate communication and radar systems. Unlike conventional passive radar, which does not prioritize communication-centric radar technology, IPR technology places a higher priority on incorporating specific radar constraints to develop waveforms that are better suited for radar applications… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16675v1-abstract-full').style.display = 'inline'; document.getElementById('2402.16675v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.16675v1-abstract-full" style="display: none;"> Integrated passive radar (IPR) can be regarded as next generation passive radar technology, which aims to integrate communication and radar systems. Unlike conventional passive radar, which does not prioritize communication-centric radar technology, IPR technology places a higher priority on incorporating specific radar constraints to develop waveforms that are better suited for radar applications. This paper deals with the problem of distributed MIMO IPR target detection under uncalibrated surveillance/reference receivers. We focus on a communication-centric radar system consisting of several opportunity transmitters non-overlapping in frequency with the same bandwidth and several spatially separated receivers. Five new detectors are devised according to the likelihood ratio test (LRT), Rao, Wald, Gradient and Durbin criteria. Although, it is shown that these detectors are asymptotically equivalent, they provide different performance in the presence of noisy reference channels. The invariance principle is applied in this paper to show that all uncertainties affecting threshold setting can be unified in the direct-path signal power-to-noise power ratio (DNR) of the reference channels. To ensure effective detection threshold setting regardless of the DNR values in the reference channels, we introduce a novel strategy to adjust the level of the proposed detectors rather than their sizes. Then, we examine false alarm regulations and detection performance of the fixed-level proposed detectors to demonstrate their effectiveness compared to several existing detectors. Thus, we create a unified framework for uncalibrated MIMO IPR target detection in the presence of noisy reference channels. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16675v1-abstract-full').style.display = 'none'; document.getElementById('2402.16675v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.02628">arXiv:2112.02628</a> <span> [<a href="https://arxiv.org/pdf/2112.02628">pdf</a>, <a href="https://arxiv.org/ps/2112.02628">ps</a>, <a href="https://arxiv.org/format/2112.02628">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TAES.2022.3168033">10.1109/TAES.2022.3168033 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lisi%2C+F">Francesco Lisi</a>, <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria Sabrina Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.02628v2-abstract-short" style="display: inline;"> In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a colocated massive MIMO radar is presented. Under the massive MIMO regime, a robust Wald type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.02628v2-abstract-full').style.display = 'inline'; document.getElementById('2112.02628v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.02628v2-abstract-full" style="display: none;"> In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a colocated massive MIMO radar is presented. Under the massive MIMO regime, a robust Wald type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and data driven scheme for the selection of the hyper-parameters involved in the RL algorithm. Such an adaptive selection makes the Wald RL based detector independent of any ad hoc, and potentially suboptimal, manual tuning of the hyper-parameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.02628v2-abstract-full').style.display = 'none'; document.getElementById('2112.02628v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04708">arXiv:2005.04708</a> <span> [<a href="https://arxiv.org/pdf/2005.04708">pdf</a>, <a href="https://arxiv.org/format/2005.04708">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ahmed%2C+A+M">Aya Mostafa Ahmed</a>, <a href="/search/eess?searchtype=author&query=Ahmad%2C+A+A">Alaa Alameer Ahmad</a>, <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Sezgin%2C+A">Aydin Sezgin</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.04708v4-abstract-short" style="display: inline;"> This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not avail… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04708v4-abstract-full').style.display = 'inline'; document.getElementById('2005.04708v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04708v4-abstract-full" style="display: none;"> This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection ($P_\mathsf{D}$) by focusing the energy in specific range-angle cells (i.e., beamforming). Furthermore, we propose a solution to the beamforming optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL based beamforming is compared to the conventional omnidirectional approach with equal power allocation and to adaptive beamforming with no RL. As highlighted by the proposed numerical results, our RL-based beamformer outperforms both approaches in terms of target detection performance. The performance improvement is even particularly remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance and rapidly changing scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04708v4-abstract-full').style.display = 'none'; document.getElementById('2005.04708v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.06191">arXiv:1906.06191</a> <span> [<a href="https://arxiv.org/pdf/1906.06191">pdf</a>, <a href="https://arxiv.org/format/1906.06191">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Massive MIMO Radar for Target Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Sanguinetti%2C+L">Luca Sanguinetti</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a>, <a href="/search/eess?searchtype=author&query=Himed%2C+B">Braham Himed</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.06191v4-abstract-short" style="display: inline;"> Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in communication systems has changed from being a theoretical scaled-up version of MIMO, with an infinite number of antennas, to a practical technology. Its key concepts have been adopted in the 5G new radio standard and base stations, where $64$ fully-digital transceivers have been commercially deployed. Motivated by these r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.06191v4-abstract-full').style.display = 'inline'; document.getElementById('1906.06191v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.06191v4-abstract-full" style="display: none;"> Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in communication systems has changed from being a theoretical scaled-up version of MIMO, with an infinite number of antennas, to a practical technology. Its key concepts have been adopted in the 5G new radio standard and base stations, where $64$ fully-digital transceivers have been commercially deployed. Motivated by these recent developments, this paper considers a co-located MIMO radar with $M_T$ transmitting and $M_R$ receiving antennas and explores the potential benefits of having a large number of virtual spatial antenna channels $N=M_TM_R$. Particularly, we focus on the target detection problem and develop a \textit{robust} Wald-type test that guarantees certain detection performance, regardless of the unknown statistical characterization of the clutter disturbance. Closed-form expressions for the probabilities of false alarm and detection are derived for the asymptotic regime $N\to \infty$. Numerical results are used to validate the asymptotic analysis in the finite system regime with different disturbance models. Our results imply that there always exists a sufficient number of antennas for which the performance requirements are satisfied, without any a-priori knowledge of the clutter statistics. This is referred to as the Massive MIMO regime of the radar system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.06191v4-abstract-full').style.display = 'none'; document.getElementById('1906.06191v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 6 figures, accepted for publication in IEEE Transactions on Signal Processing. A related work has been presented at ICASSP19, Brighton, UK, and is available at arXiv:1904.04184</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.04184">arXiv:1904.04184</a> <span> [<a href="https://arxiv.org/pdf/1904.04184">pdf</a>, <a href="https://arxiv.org/ps/1904.04184">ps</a>, <a href="https://arxiv.org/format/1904.04184">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Scaling up MIMO radar for target detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Sanguinetti%2C+L">Luca Sanguinetti</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria Sabrina Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.04184v1-abstract-short" style="display: inline;"> This work focuses on target detection in a colocated MIMO radar system. Instead of exploiting the classical temporal domain, we propose to explore the spatial dimension (i.e., number of antennas $M$) to derive asymptotic results for the detector. Specifically, we assume no a priori knowledge of the statistics of the autoregressive data generating process and propose to use a mispecified Wald-type… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.04184v1-abstract-full').style.display = 'inline'; document.getElementById('1904.04184v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.04184v1-abstract-full" style="display: none;"> This work focuses on target detection in a colocated MIMO radar system. Instead of exploiting the classical temporal domain, we propose to explore the spatial dimension (i.e., number of antennas $M$) to derive asymptotic results for the detector. Specifically, we assume no a priori knowledge of the statistics of the autoregressive data generating process and propose to use a mispecified Wald-type detector, which is shown to have an asymptotic $蠂$-squared distribution as $M\to\infty$. Closed-form expressions for the probabilities of false alarm and detection are derived. Numerical results are used to validate the asymptotic analysis in the finite system regime. It turns out that, for the considered scenario, the asymptotic performance is closely matched already for $M\ge 50$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.04184v1-abstract-full').style.display = 'none'; document.getElementById('1904.04184v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 2 figures. This is the extended version (with all the proofs in Appendices) of a paper that will be presented at ICASSP 2019, 12-17 May, 2019, Brighton, UK</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.00403">arXiv:1903.00403</a> <span> [<a href="https://arxiv.org/pdf/1903.00403">pdf</a>, <a href="https://arxiv.org/format/1903.00403">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Semiparametric Stochastic CRB for DOA Estimation in Elliptical Data Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1903.00403v1-abstract-short" style="display: inline;"> This paper aims at presenting a numerical investigation of the statistical efficiency of the MUSIC (with different covariance matrix estimates) and the IAA-APES Direction of Arrivals (DOAs) estimation algorithms under a general Complex Elliptically Symmetric (CES) distributed measurement model. Specifically, the density generator of the CES-distributed data snapshots is considered as an additional… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.00403v1-abstract-full').style.display = 'inline'; document.getElementById('1903.00403v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.00403v1-abstract-full" style="display: none;"> This paper aims at presenting a numerical investigation of the statistical efficiency of the MUSIC (with different covariance matrix estimates) and the IAA-APES Direction of Arrivals (DOAs) estimation algorithms under a general Complex Elliptically Symmetric (CES) distributed measurement model. Specifically, the density generator of the CES-distributed data snapshots is considered as an additional, infinite-dimensional, nuisance parameter. To assess the efficiency in the considered semiparametric setting, the Semiparametric Stochastic Cram茅r-Rao Bound (SSCRB) is adopted as lower bound for the Mean Square Error (MSE) of the DOA estimators. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.00403v1-abstract-full').style.display = 'none'; document.getElementById('1903.00403v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to 27th European Signal Processing Conference (EUSIPCO 2019)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.02359">arXiv:1811.02359</a> <span> [<a href="https://arxiv.org/pdf/1811.02359">pdf</a>, <a href="https://arxiv.org/ps/1811.02359">ps</a>, <a href="https://arxiv.org/format/1811.02359">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Reinforcement learning-based waveform optimization for MIMO multi-target detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+L">Li Wang</a>, <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria Sabrina Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1811.02359v1-abstract-short" style="display: inline;"> A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the \textit{agent}, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.02359v1-abstract-full').style.display = 'inline'; document.getElementById('1811.02359v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.02359v1-abstract-full" style="display: none;"> A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the \textit{agent}, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection ($P_D$). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.02359v1-abstract-full').style.display = 'none'; document.getElementById('1811.02359v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Presented at ASILOMAR 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.08936">arXiv:1807.08936</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Semiparametric Slepian-Bangs Formula for Complex Elliptically Symmetric Distributions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Zoubir%2C+A+M">Abdelhak M. Zoubir</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1807.08936v2-abstract-short" style="display: inline;"> This letter aims at deriving a Semiparametric Slepian-Bangs (SSB) formula for Complex Elliptically Symmetric (CES) distributed data vectors. The Semiparametric Cram茅r-Rao Bound (SCRB), related to the proposed SSB formula, provides a lower bound on the Mean Square Error (MSE) of \textit{any} robust estimator of a parameter vector parameterizing the mean vector and the scatter matrix of the given CE… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.08936v2-abstract-full').style.display = 'inline'; document.getElementById('1807.08936v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.08936v2-abstract-full" style="display: none;"> This letter aims at deriving a Semiparametric Slepian-Bangs (SSB) formula for Complex Elliptically Symmetric (CES) distributed data vectors. The Semiparametric Cram茅r-Rao Bound (SCRB), related to the proposed SSB formula, provides a lower bound on the Mean Square Error (MSE) of \textit{any} robust estimator of a parameter vector parameterizing the mean vector and the scatter matrix of the given CES-distributed vector in the presence of an unknown, nuisance, density generator. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.08936v2-abstract-full').style.display = 'none'; document.getElementById('1807.08936v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This letter has been merged with another letter and resubmitted as a full IEEE TSP paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.08505">arXiv:1807.08505</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> The Semiparametric Cram茅r-Rao Bound for Complex Elliptically Symmetric Distributions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a>, <a href="/search/eess?searchtype=author&query=Zoubir%2C+A+M">Abdelhak M. Zoubir</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1807.08505v2-abstract-short" style="display: inline;"> This letter aims at extending the Constrained Semiparametric Cramer-Rao Bound (CSCRB) for the joint estimation of mean vector and scatter matrix of Real Elliptically Symmetric (RES) distributions to Complex Elliptically Symmetric (CES) distributions. A closed form expression for the complex CSCRB (CCSCRB) is derived by exploiting the so-called \textit{Wirtinger} or $\mathbb{C}\mathbb{R}$-\textit{c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.08505v2-abstract-full').style.display = 'inline'; document.getElementById('1807.08505v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.08505v2-abstract-full" style="display: none;"> This letter aims at extending the Constrained Semiparametric Cramer-Rao Bound (CSCRB) for the joint estimation of mean vector and scatter matrix of Real Elliptically Symmetric (RES) distributions to Complex Elliptically Symmetric (CES) distributions. A closed form expression for the complex CSCRB (CCSCRB) is derived by exploiting the so-called \textit{Wirtinger} or $\mathbb{C}\mathbb{R}$-\textit{calculus}. Finally, the CCSCRB for the estimation of the complex mean vector and scatter matrix of a set of complex $t$-distributed random vectors is provided as an example of application. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.08505v2-abstract-full').style.display = 'none'; document.getElementById('1807.08505v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This letter has been merged with another letter and resubmitted as a full IEEE TSP paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.07811">arXiv:1807.07811</a> <span> [<a href="https://arxiv.org/pdf/1807.07811">pdf</a>, <a href="https://arxiv.org/format/1807.07811">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSP.2018.2880724">10.1109/TSP.2018.2880724 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Semiparametric Inference and Lower Bounds for Real Elliptically Symmetric Distributions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a>, <a href="/search/eess?searchtype=author&query=Zoubir%2C+A+M">Abdelhak M. Zoubir</a>, <a href="/search/eess?searchtype=author&query=Rangaswamy%2C+M">Muralidhar Rangaswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1807.07811v2-abstract-short" style="display: inline;"> This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the Real Elliptically Symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametric lower bound for the estimation of the parametric component of the model. The RES distributions represent a semiparametric model where the parametric… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.07811v2-abstract-full').style.display = 'inline'; document.getElementById('1807.07811v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.07811v2-abstract-full" style="display: none;"> This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the Real Elliptically Symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametric lower bound for the estimation of the parametric component of the model. The RES distributions represent a semiparametric model where the parametric part is given by the mean vector and by the scatter matrix while the non-parametric, infinite-dimensional, part is represented by the density generator. Since, in practical applications, we are often interested only in the estimation of the parametric component, the density generator can be considered as nuisance. The first part of the paper is dedicated to conveniently place the RES distributions in the framework of the semiparametric group models. The second part of the paper, building on the mathematical tools previously introduced, the Constrained Semiparametric Cram茅r-Rao Bound (CSCRB) for the estimation of the mean vector and of the constrained scatter matrix of a RES distributed random vector is introduced. The CSCRB provides a lower bound on the Mean Squared Error (MSE) of any robust $M$-estimator of mean vector and scatter matrix when no a-priori information on the density generator is available. A closed form expression for the CSCRB is derived. Finally, in simulations, we assess the statistical efficiency of the Tyler's and Huber's scatter matrix $M$-estimators with respect to the CSCRB. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.07811v2-abstract-full').style.display = 'none'; document.getElementById('1807.07811v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted for publication in IEEE Transactions on Signal Processing</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.01000">arXiv:1803.01000</a> <span> [<a href="https://arxiv.org/pdf/1803.01000">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Cognitive Radars: A Reality? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Stinco%2C+P">Pietro Stinco</a>, <a href="/search/eess?searchtype=author&query=Bell%2C+K">Kristine Bell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.01000v1-abstract-short" style="display: inline;"> This paper describes some key ideas and applications of cognitive radars, highlighting the limits and the path forward. Cognitive radars are systems based on the perception-action cycle of cognition that sense the environment, learn from it relevant information about the target and the background, then adapt the radar sensor to optimally satisfy the needs of their mission according to a desired go… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.01000v1-abstract-full').style.display = 'inline'; document.getElementById('1803.01000v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.01000v1-abstract-full" style="display: none;"> This paper describes some key ideas and applications of cognitive radars, highlighting the limits and the path forward. Cognitive radars are systems based on the perception-action cycle of cognition that sense the environment, learn from it relevant information about the target and the background, then adapt the radar sensor to optimally satisfy the needs of their mission according to a desired goal. The concept of cognitive radar was introduced originally for active radar only. In this paper we describe how this paradigm can be applied also to passive radar. In particular, we describe (i) cognitive active radars that work in a spectrally dense environment and change the transmitted waveform on-the-fly to avoid interference with the primary user of the channel, such as broadcast or communication systems, (ii) cognitive active radars that adjust transmit waveform parameters to achieve a specified level of target tracking performance, and (iii) cognitive passive radars, that contrary to the active radars cannot directly change the transmitted waveforms, but can instead select the best source of opportunity to improve detection and tracking performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.01000v1-abstract-full').style.display = 'none'; document.getElementById('1803.01000v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.00267">arXiv:1803.00267</a> <span> [<a href="https://arxiv.org/pdf/1803.00267">pdf</a>, <a href="https://arxiv.org/ps/1803.00267">ps</a>, <a href="https://arxiv.org/format/1803.00267">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A fresh look at the Semiparametric Cram茅r-Rao Bound </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">Stefano Fortunati</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">Fulvio Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">Maria S. Greco</a>, <a href="/search/eess?searchtype=author&query=Zoubir%2C+A+M">Abdelhak M. Zoubir</a>, <a href="/search/eess?searchtype=author&query=Rangaswamy%2C+M">Muralidhar Rangaswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.00267v1-abstract-short" style="display: inline;"> This paper aims at providing a fresh look at semiparametric estimation theory and, in particular, at the Semiparametric Cram茅r-Rao Bound (SCRB). Semiparametric models are characterized by a finite-dimensional parameter vector of interest and by an infinite-dimensional nuisance function that is often related to an unspecified functional form of the density of the noise underlying the observations.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.00267v1-abstract-full').style.display = 'inline'; document.getElementById('1803.00267v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.00267v1-abstract-full" style="display: none;"> This paper aims at providing a fresh look at semiparametric estimation theory and, in particular, at the Semiparametric Cram茅r-Rao Bound (SCRB). Semiparametric models are characterized by a finite-dimensional parameter vector of interest and by an infinite-dimensional nuisance function that is often related to an unspecified functional form of the density of the noise underlying the observations. We summarize the main motivations and the intuitive concepts about semiparametric models. Then we provide a new look at the classical estimation theory based on a geometrical Hilbert space-based approach. Finally, the semiparametric version of the Cram茅r-Rao Bound for the estimation of the finite-dimensional vector of the parameters of interest is provided. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.00267v1-abstract-full').style.display = 'none'; document.getElementById('1803.00267v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to EUSIPCO 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1709.08667">arXiv:1709.08667</a> <span> [<a href="https://arxiv.org/pdf/1709.08667">pdf</a>, <a href="https://arxiv.org/format/1709.08667">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Asymptotic robustness of Kelly's GLRT and Adaptive Matched Filter detector under model misspecification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">S. Fortunati</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">M. S. Greco</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">F. Gini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1709.08667v1-abstract-short" style="display: inline;"> A fundamental assumption underling any Hypothesis Testing (HT) problem is that the available data follow the parametric model assumed to derive the test statistic. Nevertheless, a perfect match between the true and the assumed data models cannot be achieved in many practical applications. In all these cases, it is advisable to use a robust decision test, i.e. a test whose statistic preserves (at l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1709.08667v1-abstract-full').style.display = 'inline'; document.getElementById('1709.08667v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1709.08667v1-abstract-full" style="display: none;"> A fundamental assumption underling any Hypothesis Testing (HT) problem is that the available data follow the parametric model assumed to derive the test statistic. Nevertheless, a perfect match between the true and the assumed data models cannot be achieved in many practical applications. In all these cases, it is advisable to use a robust decision test, i.e. a test whose statistic preserves (at least asymptotically) the same probability density function (pdf) for a suitable set of possible input data models under the null hypothesis. Building upon the seminal work of Kent (1982), in this paper we investigate the impact of the model mismatch in a recurring HT problem in radar signal processing applications: testing the mean of a set of Complex Elliptically Symmetric (CES) distributed random vectors under a possible misspecified, Gaussian data model. In particular, by using this general misspecified framework, a new look to two popular detectors, the Kelly's Generalized Likelihood Ration Test (GLRT) and the Adaptive Matched Filter (AMF), is provided and their robustness properties investigated. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1709.08667v1-abstract-full').style.display = 'none'; document.getElementById('1709.08667v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 September, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ISI World Statistics Congress 2017 (ISI2017), Marrakech, Morocco, 16-21 July 2017</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1709.08210">arXiv:1709.08210</a> <span> [<a href="https://arxiv.org/pdf/1709.08210">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MSP.2017.2738017">10.1109/MSP.2017.2738017 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental findings and applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fortunati%2C+S">S. Fortunati</a>, <a href="/search/eess?searchtype=author&query=Gini%2C+F">F. Gini</a>, <a href="/search/eess?searchtype=author&query=Greco%2C+M+S">M. S. Greco</a>, <a href="/search/eess?searchtype=author&query=Richmond%2C+C+D">C. D. Richmond</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1709.08210v1-abstract-short" style="display: inline;"> Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. In particular, the common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in the last decades; for example, wireless communications, radar and sonar, biomedicine, image processi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1709.08210v1-abstract-full').style.display = 'inline'; document.getElementById('1709.08210v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1709.08210v1-abstract-full" style="display: none;"> Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. In particular, the common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in the last decades; for example, wireless communications, radar and sonar, biomedicine, image processing, and seismology, just to name a few. Developing an estimation algorithm often begins by assuming a statistical model for the measured data, i.e. a probability density function (pdf) which if correct, fully characterizes the behaviour of the collected data/measurements. Experience with real data, however, often exposes the limitations of any assumed data model since modelling errors at some level are always present. Consequently, the true data model and the model assumed to derive the estimation algorithm could differ. When this happens, the model is said to be mismatched or misspecified. Therefore, understanding the possible performance loss or regret that an estimation algorithm could experience under model misspecification is of crucial importance for any SP practitioner. Further, understanding the limits on the performance of any estimator subject to model misspecification is of practical interest. Motivated by the widespread and practical need to assess the performance of a mismatched estimator, the goal of this paper is to help to bring attention to the main theoretical findings on estimation theory, and in particular on lower bounds under model misspecification, that have been published in the statistical and econometrical literature in the last fifty years. Secondly, some applications are discussed to illustrate the broad range of areas and problems to which this framework extends, and consequently the numerous opportunities available for SP researchers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1709.08210v1-abstract-full').style.display = 'none'; document.getElementById('1709.08210v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in the IEEE Signal Processing Magazine</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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