On the sensor order in sequential integrated probability data association filter

The processing order of sensors with different detection probabilities and in different clutter densities in a multi-sensor system is investigated in this paper. A sequential implementation of the integrated probability data association (IPDA) algorithm under random set framework is derived. Under the assumptions of different detection probabilities and different clutter densities of individual sensor in a multi-sensor system, we reach the conclusion that the sequential IPDA filter depends on the order analyzing the target existence probability of varying sensor orders. Moreover, we obtain the optimal order of sensors for the sequential IPDA filter in terms of maximizing the target existence probability. The conclusions are demonstrated by simulation results.

[1]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[2]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[3]  Ba-Ngu Vo,et al.  Bayesian Filtering With Random Finite Set Observations , 2008, IEEE Transactions on Signal Processing.

[4]  R. Mahler,et al.  PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Arnaud Doucet,et al.  Sequential Monte Carlo Methods , 2006, Handbook of Graphical Models.

[6]  Ronald Mahler,et al.  PHD filters of second order in target number , 2006, SPIE Defense + Commercial Sensing.

[7]  R. Mahler Multitarget Bayes filtering via first-order multitarget moments , 2003 .

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

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

[10]  Lucy Y. Pao,et al.  A comparison of parallel and sequential implementations of a multisensor multitarget tracking algorithm , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[11]  Samuel S. Blackman,et al.  Design and Analysis of Modern Tracking Systems , 1999 .

[12]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[13]  I. R. Goodman,et al.  Mathematics of Data Fusion , 1997 .

[14]  Lucy Y. Pao,et al.  The optimal order of processing sensor information in sequential multisensor fusion algorithms , 2000, IEEE Trans. Autom. Control..

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

[16]  Ba-Ngu Vo,et al.  Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.

[17]  Ba-Ngu Vo,et al.  Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach , 2006, IEEE Transactions on Signal Processing.

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

[19]  B. Vo,et al.  Bayesian approaches to track existence - IPDA and random sets , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).