Multi-target particle filter based track before detect algorithms for spawning targets /

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2014
Eyili, Mehmet
In this work, a Track Before Detect (TBD) approach is proposed for tracking and detection of the spawning targets on the basis of raw radar measurements. The principle of this approach is mainly constructed by multi-model particle filter method. In contrast to the related works in the literature, a novel reduced order dynamic model is introduced and the information about bearing angle derived from the radar measurements is not used in this model to improve the efficiency of the particle filter. Moreover, a new process noise identification method [1] proposed for the classical target tracking is adapted to the TBD framework. The process noise identification is used for the state estimation of the highly maneuvering spawned targets in the presence of non-stationary process noise with unknown parameters. It is shown that this method deals with the sample impoverishment problem which is serious for tracking of the highly maneuvering targets by particle filters. Two different multi-target particle filter based TBD algorithms are developed. These algorithms are confirmed by simulations. Their performances are analyzed on the basis of the probability of target existences and Root-Mean-Square (RMS) estimation accuracies.
Citation Formats
M. Eyili, “Multi-target particle filter based track before detect algorithms for spawning targets /,” M.S. - Master of Science, Middle East Technical University, 2014.