Track Processing Approach for Bearing-Only Target Tracking

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.

[1]  T. R. Kronhamn,et al.  Bearings-only target motion analysis based on a multihypothesis Kalman filter and adaptive ownship motion control , 1998 .

[2]  D P Casasent,et al.  Fast JPDA multitarget tracking algorithm. , 1989, Applied optics.

[3]  Y. Kosuge,et al.  Sensor-to-sensor target association in a network of passive sensors , 1997, Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066).

[4]  Henry Leung,et al.  Statistical performance analysis of track initiation techniques , 1997, IEEE Trans. Signal Process..

[5]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[6]  Y. Bar-Shalom,et al.  A generalized S-D assignment algorithm for multisensor-multitarget state estimation , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[8]  Jayaram K. Udupa,et al.  Shape-based interpolation of multidimensional grey-level images , 1994, Medical Imaging.

[9]  A. Ardeshir Goshtasby,et al.  Matching of tomographic slices for interpolation , 1992, IEEE Trans. Medical Imaging.

[10]  Y. Bar-Shalom,et al.  m-best S-D assignment algorithm with application to multitarget tracking , 2001 .

[11]  Y. Bar-Shalom,et al.  A new relaxation algorithm and passive sensor data association , 1992 .

[12]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[13]  John Stein,et al.  An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance systems , 1971, CDC 1971.

[14]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[15]  Vikram Krishnamurthy,et al.  An improvement to the interacting multiple model (IMM) algorithm , 2001, IEEE Trans. Signal Process..

[16]  J. Udupa,et al.  An objective comparison of 3-D image interpolation methods , 1998, IEEE Transactions on Medical Imaging.

[17]  Mangzuo Shen.  Range information extraction from tracking data using object kinematic parameters , 1995, Optics & Photonics.

[18]  Zhu Hong-yan.  Study on Approaches for Track Initiation , 2004 .

[19]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[20]  M. Gavish,et al.  Nth-order dynamics target observability from angle measurements , 1988 .

[21]  J. A. Roecker.  A class of near optimal JPDA algorithms , 1994 .

[22]  J. A. Roecker,et al.  Suboptimal joint probabilistic data association , 1993 .