Abstract
This paper presents a description of recent research and the multi-target tracking in experimental passive bistatic radar (PBR) system taking advantage of numerous non-cooperative AM radio signals via multi-static doppler shifts. However, it raises challenges for use by multiple spatially distributed AM radio illuminators for multi-target tracking in PBR system due to complex data association hypotheses and no directly used tracking algorithm in the practical scenario. To solve these problems, after a series of key array signal processing techniques in the self-developed system, by constructing a nonlinear measurement model, the novel method is proposed to accommodate nonlinear model by using the unscented transformation (UT) in Gaussian mixture (GM) implementation of iterated-corrector cardinality-balanced multi-target multi-Bernoulli (CBMeMBer). Simulation and experimental results analysis verify the feasibility of this approach used in a practical PBR system for moving multi-target tracking.
Highlights
IntroductionSince the Passive bistatic radar (PBR) system can typically collect bistatic range, time-of-arrival (TOA), direction-of-arrival (DOA), and Doppler shift from the received signals, an alternative choice is to use multi-static Doppler shift as measurements for multi-target tracking
We extended Gaussian mixture (GM) implementation of the CBMeMBer filter to update the procedure by using unscented transformation (UT)
We developed the Passive bistatic radar (PBR) system in Huazhong University of Science and Technology by tracking a close-in civilian airplane whose working frequency band is 6–30 MHz
Summary
Since the PBR system can typically collect bistatic range, time-of-arrival (TOA), direction-of-arrival (DOA), and Doppler shift from the received signals, an alternative choice is to use multi-static Doppler shift as measurements for multi-target tracking. DOA of in direct wave can be to traditional passive radar, surveillance and reference channels arefrom needed receive target obtained by the multiple signal classification (MUSIC).
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