Track Processing Approach for Bearing-Only Target Tracking

Track Processing Approach for Bearing-Only Target Tracking

Автор: Hui Chen, Chen Li

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 1 vol.1, 2009 года.

Бесплатный доступ

This paper mainly studies angle-measurement based track processing approach to overcome the existing problems in the applications of traditional approaches for bearing-only target locating and tracking system. First, this paper gives suited data association algorithms including track initiation and point-track association. Moreover, a new tracking filtering association gate method is presented through analysis of the target motion characteristics in polar coordinates for improving bearing-only measurement confirming efficiency of real target and limiting false track overextension with the dense clutter. Then, by analyzing the feasibility of using multi-model technology, the IMM is adopt as filtering algorithm to solve existing problem in bearing-only tracking for complicated target motion in two dimensional angle plane. As the results, the two dimensional bearing-only tracking accuracy of real target is improved and false tracking is greatly limited. Moreover, computation cost of IMM is analyzed in view of the real-time demand of bearing-only tracking. Finally, this paper gives some concrete summary of multi-model choosing principle. The application of the proposed approach in a simulation system proves its effectiveness and practicability.

Еще

Data association, multi-model filter, bearing-only tracking, passive sensor, targets

Короткий адрес: https://sciup.org/15010084

IDR: 15010084

Список литературы Track Processing Approach for Bearing-Only Target Tracking

  • Popp R L, Pattipati K R, Bar-Shalom Y. m-Best S-D assignment algorithm with application to multitarget tracking[J]. IEEE Trans. on AC, 2001, 37 (1):22 - 38.
  • Eli Fogel, Motti Gavish. Nth-order Dynamics Target Observability from Angle Measurements[J] . IEEE Trans. on AES, 1998, 3(24):305 - 307.
  • Mangzuo S. Range Information Extraction from Tracking Data Using Object Kinematic Parameters[J] . SPIE, 1995, 2561: 484-488.
  • Kronhamn T R. Bearings-only Target Motion Analysis Based on A Multi-hypothesis Kalman Filter and Adaptive Oweship Motion Control [R]. IEE Proc. Radar, Sonar Navigation, 1998, 145 (4): 247 - 252.
  • Pattipati K R, Deb S, Bar-Shalom Y, et al. A new relaxation algorithm and passive sensor data association [J]. IEEE Trans. on AC, 1992, 37(2): 98-213.
  • Somnath Deb, Murali Yeddanapudi, Krishna Pattipati, et al. A generalized S-D assignment approach for multisensor-multitarget state estimation [J]. IEEE Trans. on AES, 1997, 33(2): 523–538.
  • WANG Ming-hui, YOU Zhi-sheng, ZHAO Rong-chun, et al. An fast data association approach of passive sensors [J]. Electronic Journal, 2000, 28(12): 45-47.
  • Ito M, Tsujimichi S, Kosuge Y. Sensor-to-sensor target association in a network of passive sensors[J]. 23rd International Conference on Industrial Electronics, Control and Instrumentation, 1997, (3): 1260–1264.
  • PAN Li-na. A track initiation and filtering approach of shipborne infrared surveillance system in multi-target tracking [J].Electro-Optic Warfare & Radar Passive Countermeasures, 1998, (2): 19-23.
  • Grevera G J, Udupa J K. An objective comparision of 3-D image interpolation methods[J]. IEEE Transactions on Medical Imaging, 1998, 17(4): 642-652.
  • Grevera G J, Udupa J K. Shape-based interpolation of multidimensional grey-level images[J]. IEEE Transactions on Medical Imaging, 1996, 15(6): 881-892.
  • Hu Z J, Leung H. Statistical performance analysis of track initiation techniques[J]. IEEE Transactions on AES, 1997, 45(2): 445-456.
  • A Goshtasby, D A Turner, L V Acekerman. Matching of tomographic slices for interpolation[J]. IEEE Transactions on Medical Imaging, 1992, 11(4): 507-516.
  • Bar-shalom Y, Fortmann T E. Tracking and data association[M]. New York, 1988. 252 - 263.
  • Singer R A, Sea R G. A new filter for optimal tracking in dense multi-target environment[C]. Proceedings of the ninth Allerton Conference Circuit and System Theory. Urbana-Champaign, USA: Univ. of Illinois, 1971. 201~211.
  • Singer R A,Stein J J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance system[C]. Proceedings of the tenth IEEE Conference on Decision and Control. USA: Institute of Electrical and Electronics Engineers, 1971. 171~175.
  • Roecker J A, Phillis G L. Suboptimal joint probabilistic data association[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(2): 510~517.
  • Roecker J A. A class of near optimal JPDA algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems,1994, 30(2): 504~510
  • Fisher J L,Casasent D P. Fast JPDA multi-target tracking algorithm[J]. Applied Optics, 1989, 28(2): 371~376.
  • Bar-Shalom Y, Tse E. Tracking in a cluttered environment with probabilistic data association[J]. Automatica, 1975, 11(9):451~460.
  • Metropolis N, Rosenbluth A W, M N Rosenbiuth, etal. Equations of state calculations by fast computing machines. Journal of chemical phyisics, 1953, 21(6): 1087~1091.
  • Kong A, Liu J S, Wong W H. Sequential imputations and Bayesian missing data problem.. Jounal of American Statistical Association, 1994, 89(425): 278~288.
  • Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear-non-gaussian Bayesian state estimation. IEE-Proceeding-F, 1993, 140(2):107~113.
  • Johnston L A, Vikram Krishnamurthy. An Improvement to the Interacting Multiple Model (IMM) Algorithm. IEEE Trans. on Signal Processing, 2001, 49: 2909-2923.
  • ZHU Hong-yan, HAN Chong-zhao, HAN Hong, et al. Study on Approaches for Track Initiation[J]. Acta Aeronautica et Astronautica Sinica, 2004, 25(3)：284-288.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp