Gaussian Mixture Reduction for Tracking Multiple Maneuvering Targets in Clutter

Abstract : The problem of tracking multiple maneuvering targets in clutter naturally leads to a Gaussian mixture representation of the Provability Density Function (PDF) of the target state vector. State-of-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses.

[1]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

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

[3]  Keith D. Kastella,et al.  Comparison of mean-field tracker and joint probabilistic data association tracker in high-clutter environments , 1995, Optics & Photonics.

[4]  W. Koch.  Experimental results on Bayesian MHT for maneuvering closely-spaced objects in a densely cluttered environment , 1997 .

[5]  J.S. Goldstein,et al.  Introduction to radar systems third edition [Book Review] , 2001, IEEE Aerospace and Electronic Systems Magazine.

[6]  Henk A. P. Blom,et al.  Joint probabilistic data association methods avoiding track coalescence , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[7]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[8]  D. Salmond.  Mixture reduction algorithms for target tracking , 1989 .

[9]  R. Streit,et al.  Probabilistic Multi-Hypothesis Tracking , 1995 .

[10]  P. S. Dwyer.  Some Applications of Matrix Derivatives in Multivariate Analysis , 1967 .

[11]  Samuel S. Blackman,et al.  Evaluation of IMM filtering for an air defense system application , 1995, Optics & Photonics.

[12]  J. A. Roecker.  Multiple scan joint probabilistic data association , 1995 .

[13]  Lucy Y. Pao,et al.  Multisensor multitarget mixture reduction algorithms for tracking , 1994 .

[14]  Lang Hong,et al.  Bias phenomenon and compensation for PDA/JPDA algorithms , 1998 .

[15]  J. Bather,et al.  Mixture Reduction Algorithms for Uncertain Tracking , 1988 .

[16]  Henk A. P. Blom,et al.  Joint Probabilistic Data Association Avoiding Track Coalescence , 1995 .

[17]  Kim B. Housewright,et al.  Derivation and evaluation of improved tracking filter for use in dense multitarget environments , 1974, IEEE Trans. Inf. Theory.

[18]  Cong Shan,et al.  Bias phenomenon and compensation in multiple target tracking algorithms , 2000 .

[19]  Aubrey B. Poore,et al.  A New Lagrangian Relaxation Based Algorithm for a Class of Multidimensional Assignment Problems , 1997, Comput. Optim. Appl..

[20]  W. Koch,et al.  Multiple hypothesis track maintenance with possibly unresolved measurements , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Henk A. P. Blom,et al.  Probabilistic data association avoiding track coalescence , 2000, IEEE Trans. Autom. Control..

[22]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[23]  D. Alspach.  A gaussian sum approach to the multi-target identification-tracking problem , 1975, Autom..

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

[25]  Dale R. Billetter.  Multifunction Array Radar , 1989 .

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

[27]  Robert J. Dempster,et al.  Combining IMM filtering and MHT data association for multitarget tracking , 1997, Proceedings The Twenty-Ninth Southeastern Symposium on System Theory.

[28]  David J. Salmond.  Mixture reduction algorithms for target tracking in clutter , 1990 .

[29]  S. Leigh,et al.  Probability and Random Processes for Electrical Engineering , 1989 .

[30]  Hugh Porteous.  Linear Algebra and its Applications (Third edition)Title: Linear Algebra and its Applications ( Third edition ) Author: David C. Lay Addison Wesley 2003 , ISBN: 0-201-70970-8 , 2003 .

[31]  Branko Ristic,et al.  Multitarget mixture reduction algorithm with incorporated target existence recursions , 2000, SPIE Defense + Commercial Sensing.

[32]  D. Lainiotis,et al.  On joint detection, estimation and system identification: discrete data case† , 1973 .

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

[34]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[35]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[36]  Tim Wark,et al.  Multi-modal speech processing for automatic speaker recognition , 2001 .

[37]  George W. Stimson,et al.  Introduction to Airborne Radar , 1983 .

[38]  D. Salmond.  Tracking in Uncertain Environments , 1989 .

[39]  Keith D. Kastella.  Maximum likelihood estimator for report-to-track association , 1993, Defense, Security, and Sensing.

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