Data association probability and measurement density function of tracking in clutter with strongest-neighbor measurements

When tracking a target in clutter, a measurement may have originated from either the target, clutter, or some other source. The measurement with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement is known as the 'strongest neighbor' (SN) measurement. A simple and commonly used method for tracking in clutter is the so-called strongest neighbor filter (SNF), which uses the SN measurement at each time as if it were the true one. This paper presents analytic results, along with discussions, for the SN measurement, including the a priori and a posteriori probabilities of data association events and the conditional probability density functions. These results provide theoretical foundation for performance prediction and development of improved tracking filters.

[1]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[2]  R. Singer,et al.  New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments , 1973 .

[3]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

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

[5]  X. R. Li,et al.  Performance Prediction of the Interacting Multiple Model Algorithm , 1992 .

[6]  Y. Bar-Shalom,et al.  Stability evaluation and track life of the PDAF for tracking in clutter , 1990, 29th IEEE Conference on Decision and Control.

[7]  Yaakov Bar-Shalom,et al.  Detection threshold selection for tracking performance optimization , 1994 .

[8]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[9]  Yaakov Bar-Shalom,et al.  Automated Tracking with Target Amplitude Information , 1990, 1990 American Control Conference.

[10]  Y. Bar-Shalom,et al.  Detection thresholds for tracking in clutter--A connection between estimation and signal processing , 1985 .

[11]  Kuo-Chu Chang,et al.  Prediction of track purity and track accuracy in dense target environments , 1995, IEEE Trans. Autom. Control..

[12]  D. Lerro,et al.  Comparison of Tracking/association Methods For Low SNR Targets , 1992, OCEANS 92 Proceedings@m_Mastering the Oceans Through Technology.

[13]  S. R. Rogers,et al.  Diffusion analysis of track loss in clutter , 1991 .

[14]  Oliver E. Drummond,et al.  Performance evaluation methods for multiple-target-tracking algorithms , 1991, Defense, Security, and Sensing.

[15]  Y. Bar-Shalom,et al.  Tracking in clutter with nearest neighbor filters: analysis and performance , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Albert H Nuttall,et al.  Signal-to-noise ratio requirements for detection of multiple pulses subject to partially correlated fading with chi-squared statistics of various degrees of freedom , 1986 .