Abstract
The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.
Highlights
Radar, with the capability of all-weather monitoring day and night, has been widely used in civil and military applications [1,2,3,4,5,6]
We propose a time-matching random finite set (RFS)-based multi-target tracking (MTT) framework in which the sampling time diversity of the radar MTT is considered
This makes it possible to use RFS-based filters to deal with the complex radar MTT problems
Summary
With the capability of all-weather monitoring day and night, has been widely used in civil and military applications [1,2,3,4,5,6]. In order to improve the performance of the RFS-based approaches in radar applications, we propose a time-matching filtering framework in this paper to deal with the problem induced by the sampling time diversity. When the surveillance area is very large and the target is far from the radar, it is necessary for the antenna to scan slowly, so that its beam can illuminate in each direction for a long time to obtain a relatively good signal-to-noise ratio (SNR) This leads to a wide difference in sampling time between different targets, so much so that we have to treat them differently. We divide the measurement space into several small areas according to the sampling times and assume that the different regions are independent of each other Based on this assumption, we model both the multi-target state and the measurements at each scan as special RFSs, whose elements are RFSs, each corresponding to a specific sampling time.
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