{"title":"Instant Location Detection of Objects Moving at High-Speedin C-OTDR Monitoring Systems","authors":"Andrey V. Timofeev","volume":106,"journal":"International Journal of Computer and Information Engineering","pagesStart":2177,"pagesEnd":2182,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10002523","abstract":"<p>The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data of the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as &laquo;signaling parameters&raquo; (SP). There are several the SP&rsquo;s which carry information about dynamic objects instant localization for each of COTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources, but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as rule. This report contains describing of the method for SP&rsquo;s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.<\/p>\r\n","references":"[1] K. N. Choi, J. C. Juarez, H. F. Taylor, \u201cDistributed fiber optic\r\npressure\/seismic sensor for low-cost monitoring of long perimeters\u201d,\r\nProc. SPIE 5090, Unattended Ground Sensor Technologies and\r\nApplications, 2003, pp. 134-141.\r\n[2] J. C. Juarez, E. W. Maier, K. N. Choi, and H. F. Taylor, \u201cDistributed\r\nFiber-Optic Intrusion Sensor System\u201d, Journal of Lightwave\r\nTechnology, Vol. 23, Issue 6, 2005, pp. 2081-2087.\r\n[3] S. S. Mahmoud, Y. Visagathilagar, J. Katsifolis., \u201cReal-time distributed\r\nfiber optic sensor for security systems: Performance, event classification\r\nand nuisance mitigation\". Photonic Sensors, Vol.2, Issue 3, 2012, pp.\r\n225-236.\r\n[4] V. Korotaev, V. M. Denisov, A. V. Timofeev, and M. G. Serikova,\r\n\"Analysis of seismoacoustic activity based on using optical fiber\r\nclassifier,\" in Latin America Optics and Photonics Conference, OSA\r\nTechnical Digest (online) (Optical Society of America, 2014), paper\r\nLM4A.22.\r\n[5] Y. Mei, \u201cSequential change-point detection when unknown parameters\r\nare present in the pre-change distribution\u201d, The Annals of Statistics, Vol.\r\n34, 2006, pp. 92-122.\r\n[6] T.L. Lai, \u201cSequential Change point Detection in Quality Control and\r\nDynamical Systems\u201d, Journal of Royal Statistical Society, Series B, Vol.\r\n57, 1995, pp. 613-658.\r\n[7] A.V. Timofeev, \"Monitoring the Railways by Means of C-OTDR\r\nTechnology\", International Journal of Mechanical, Aerospace,\r\nIndustrial and Mechatronics Engineering, 9(5), 2015, 620-623.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 106, 2015"}