Abstract
A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.
Highlights
Multi-target tracking based on multi-target Bayes filter makes an important contribution to capturing and maintaining the awareness of the environment [1,2]
With the development of the emerging random finite set (RFS) theory, tremendous efforts have been devoted to investigating various approximate multi-target Bayes filters [5,6,7,8], including probability hypothesis density (PHD) filter, cardinalized PHD (CPHD) filter and multi-Bernoulli (MB) filter, and their various revised versions
To improve the computational efficiency of the S-labeled multi-Bernoulli (LMB)-generic observation model (GOM) filter, we propose an approximate method to calculate the point spread function according to its property analysis
Summary
Multi-target tracking based on multi-target Bayes filter makes an important contribution to capturing and maintaining the awareness of the environment [1,2]. In order to integrate track management into the filtering scheme, the so-called generalized labeled multi-Bernoulli (GLMB). Filter and labeled multi-Bernoulli (LMB) filter were proposed [9,10,11]. Most of these filters are based on the standard multi-target observation model which assumes that one target generates at most one measurement and so any measurement corresponds to at most one target [12]. In many scenarios, the observation models are nonstandard and cannot be modeled by standard multi-target likelihood function, for example, track before detect (TBD) in video [13,14], superpositional sensors [15,16,17], unresolved target tracking [18] and extended target tracking [19]
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