Background agnostic CPHD tracking of dim targets in heavy clutter
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Detection and tracking of dim targets in heavy clutter environments is a daunting theoretical and practical problem. Application of the recently developed Background Agnostic Cardinalized Probability Hypothesis Density (BA-CPHD) filter provides a very promising approach that adequately addresses all the complexities and the nonlinear nature of this problem. In this paper, we present analysis, derivation, development, and application of a BA-CPHD implementation for tracking dim ballistic targets in environments with a range of unknown clutter rates, unknown clutter distribution, and unknown target probability of detection. The effectiveness and accuracy of the implemented algorithms are assessed and evaluated. Results that evaluate and also demonstrate the specific merits of the proposed approach are presented. © 2013 SPIE.
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Mahler, Ronald (2012)This paper describes a general approach for deriving PHD/CPHD filters that must estimate the background clutter process, rather than being provided with it a priori. I first derive general time- and measurementupdate ...
Mahler, Ronald (2015)© 2015 SPIE. Previous research has produced CPHD filters that can detect and track multiple targets in unknown, dynamically changing clutter. The.first such filters employed Poisson clutter generators and, as a result, ...
Mahler, Ronald (2014)© 2014 IEEE. In previous publications the author introduced CPHD filters designed to detect and track multiple targets in unknown, dynamically changing clutter backgrounds. The first such filters employed Poisson clutter ...