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A Comparison of Bistatic Bearings-Only Tracking Methods

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Porównanie bistatycznych metod wyznaczania namiaru kątowego
Języki publikacji
EN
Abstrakty
EN
Target tracking using bistatic bearings-only measurements has obtained distinct interest recently. It is a nonlinear problem that traditional Kalman filter (KF) can not be applied directly. In this paper, the triangular ranging formula has been derived first for bistatic bearings-only tracking. The ranging error is then proved to be Gaussian noises, which enable the traditional KF applicable. The recently developed unscented Kalman filter (UKF) is also applied to the nonlinear measuring equation directly. To further improve the tracking accuracy especially in case of maneuvering target tracking, interactive multiple model (IMM) is adopted. Simulation results for both constant velocity moving target and maneuvering target are included to compare the performance of the aforementioned methods. The triangular ranging method, triangular-ranging-based Kalman filtering (TRKF), UKF, TR-IMMKF, and IMM-UKF are compared extensively using the criterion of root of the mean squared error (RMSE) and computational burden, as well as the robustness.
PL
W artykule zaproponowano formułę o zasięgu trójkątnym w zastosowaniu do bistatycznego wyznaczania namiaru (pelengu). W rozwiązaniu wykorzystano m. In. filtr Kalman’a do pomiaru wielkości nieliniowych. Wyniki badań symulacyjnych, dla obiektów w ruchu jednostajnym lub zmiennym, pozwalają na porównanie działania metod. Porównano także metody TRKF, UKF, TR-IMMKF, IMM-UKF, pod względem błędów średniokwadratowych, odporności, złożoności obliczeń.
Rocznik
Strony
179--184
Opis fizyczny
Bibliogr. 15 poz., tab., wykr.
Twórcy
autor
  • College of Information Engineering,Xiangtan University, Yuhu District, Xiangtan, Hunan, China, 411105
Bibliografia
  • [1] Musicki D., Bearings only single-sensor target tracking using Gaussian mixtures, Automatica, 45 (2009), No. 9, 2088-2092
  • [2] Jauffret C., Pillon D., Pignol A.C., Bearings-Only Maneuvering Target Motion Analysis from a Nonmaneuvering Platform, IEEE Trans. Aeros. & Electr. Syst., 46 (2010), No. 4, 1934-1949
  • [3] Lindgren A.G., Gong K. F., Position and velocity estimation via bearing observations, IEEE Trans. Aerosp. Electron. Syst., 14 (1978), No. 4, 564–577
  • [4] Gharehshiran O.N., Krishnamurthy V., Coalition Formation for Bearings-Only Localization in Sensor Networks-A Cooperative Game Approach, IEEE Trans. Signal Process., 58 (1010), No. 8, 4322-4338
  • [5] Musicki D., Bearings only multi-sensor maneuvering target tracking, Systems & Cotrol Lett., 57 (2008), No. 3, 216-221
  • [6] Song T. L., Speyer J. L., A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements, IEEE Trans. Automatic Control, 30 (1985), No. 10, 940-949
  • [7] Julier S.J., Uhlmann J.K., Durrant-Whyte H.F., A new method for the nonlinear transformations of means and covariances in filters and estimators, IEEE Trans. Autom. Control, vol. 45 (2000), No. 3, 477-482
  • [8] Ito K., Xiong K., Gaussian filters for nonlinear filtering problems, IEEE Trans. Autom. Control, 45 (2000), No. 5, 910-927
  • [9] Ristic B., Arulampalam S., Gordon N., Beyond the Kalman Filter: Particle Filters for Tracking Applications. Norwood, MA: Artech House, 2004
  • [10] Lin X., Kirubaranjan T., Bar-Shalom Y., Comparison of EKF, pseudo-measurement, and particle filer for a bearing-only target tracking problem, in Proc. SPIE Conf. Signal Data Processing of Small Targets, Orlando, FL, 2002
  • [11] Le Cadre J. P., Bearings-only tracking for maneuvering sources, IEEE Trans. Aerospace & Elect. System, 34 (1998), No. 1, 179- 191
  • [12] Trémois O., Le Cadre J.P., Target motion analysis with multiple arrays: performance analysis, IEEE Trans. Aerospace Electron. Systems, 32 (1996), No. 3, 1030–1046
  • [13] Kalman R. E., A new approach to linear filtering and prediction problems, J. basic Eng. - T. ASME, 82 (1960), 35-45
  • [14] Zhang Y., Li X. R., Detection and diagnosis of sensor and actuator failures using IMM estimator, IEEE Trans. Aeros. & Elect. System, 34 (1998), No. 4, 1293-1311
  • [15] Johnstone A. L., Krishnamurthy V., An improvement to the interacting multiple model (IMM) algorithm, IEEE Trans. on Signal Proces., 49 (2011), No. 12, 2909-2923
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0e3f8aa9-03ef-4311-a067-e81ad662873a
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