scholarly journals Data Association Techniques for Non-Gaussian Measurements

Author(s):  
Stephen C. ◽  
Kathleen A.
Author(s):  
Tao Yang ◽  
Prashant G. Mehta

This paper is concerned with the problem of tracking single or multiple targets with multiple nontarget-specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization—to the nonlinear non-Gaussian case—of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking (MTT) applications.


2012 ◽  
Vol 71 (17) ◽  
pp. 1541-1555
Author(s):  
V. A. Baranov ◽  
S. V. Baranov ◽  
A. V. Nozdrachev ◽  
A. A. Rogov

2013 ◽  
Vol 72 (11) ◽  
pp. 1029-1038
Author(s):  
M. Yu. Konyshev ◽  
S. V. Shinakov ◽  
A. V. Pankratov ◽  
S. V. Baranov

2010 ◽  
Vol 69 (8) ◽  
pp. 669-680 ◽  
Author(s):  
D. A. Kurkin ◽  
A. A. Roenko ◽  
V. V. Lukin ◽  
I. Djurovic
Keyword(s):  

2007 ◽  
Vol 66 (18) ◽  
pp. 1703-1710
Author(s):  
V. A. Tikhonov ◽  
K. V. Netrebenko
Keyword(s):  

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