Target perceivability and its applications

A concept of target perceivability is introduced, which is related to such concepts as target existence and target observability. Its probability provides a basis for an integrated approach to track initiation, confirmation, termination, and refinement of track maintenance algorithms. This paper proposes the concepts of target perceivability and presents a recursion of its probability based on hidden Markov models (HMMs) and their applications to tracker analysis, development, and design, in particular, in the context of the probabilistic data association (PDA) method. Specifically, several important quantities and track life are analyzed; a perceivability-based probabilistic approach to track confirmation and termination is proposed; two versions of perceivability-based PDA trackers are presented. Simulation results are provided to demonstrate their performance.

[1]  Ning Li,et al.  Integrated real-time estimation of clutter density for tracking , 2000, IEEE Trans. Signal Process..

[2]  S. Stankovic,et al.  Integrated probabilistic data association (IPDA) , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[3]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[4]  Robin J. Evans,et al.  Integrated probabilistic data association , 1994, IEEE Trans. Autom. Control..

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

[6]  X. R. Li,et al.  Tracker design based on target perceivability , 2001 .

[7]  X. R. Li,et al.  Chapter 10 Engineer ’ s Guide to Variable-Structure Multiple-Model Estimation for Tracking , 2022 .

[8]  X. R. Li,et al.  Tracking in clutter with strongest neighbor measurements. I. Theoretical analysis , 1998, IEEE Trans. Autom. Control..

[9]  Robert W. Sittler,et al.  An Optimal Data Association Problem in Surveillance Theory , 1964, IEEE Transactions on Military Electronics.

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

[11]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[12]  Ning Li,et al.  Intelligent PDAF: refinement of IPDAF for tracking in clutter , 1997, Proceedings The Twenty-Ninth Southeastern Symposium on System Theory.

[13]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Applications and Advances , 1992 .

[14]  Chen He,et al.  2M-PDAF: an integrated two-model probabilistic data association filter , 1999, Optics & Photonics.

[15]  C. Morefield.  Application of 0-1 integer programming to multitarget tracking problems , 1977 .

[16]  Corrections to "A Method for the Synthesis of Controllers to Handle Safety, Liveness, and Real-Time Constraints" and "Tracking in Clutter with Strongest Neighbor Measurements - Part I: Theoretical Analysis" , 1999, IEEE Trans. Autom. Control..

[17]  Jean Dezert,et al.  A new formulation of IPDAF for tracking in clutter , 1999, 1999 European Control Conference (ECC).

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

[19]  X. R. Li,et al.  Adaptation of Transition Probability Matrix for Multiple Model Estimators , 2001 .

[20]  Y. Bar-Shalom,et al.  Automatic track formation in clutter with a recursive algorithm , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.