| Summary: | The Fields of Views (FoVs) of radars in a distributed network partially overlap due to detecting capability, waveform design, and antenna orientation constraints, resulting in observed discrepancies between radars and a significant obstacle to future information fusion. In this paper, we propose a distributed multitarget tracking method under the scene of partially overlapping radar FoVs, based on the Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) filter. First, we employ the product of the multitarget densities to split the PHD functions and find the part that characterizes the information of the targets commonly observed by multiple radars. Then, a standard distributed fusion (arithmetic average or geometric average fusion) acts on the splitting information to improve tracking performance, and a compensation fusion acts on the remaining information to expand the observation FoV. The proposed method does not require prior knowledge of the radar’s FoV and may adapt to the scene of distributed multitarget tracking while the FoVs are unknown. Simulations are provided to verify the effectiveness of the proposed approach under the scene of unknown and time-varying radar FoVs, and show that the proposed method has better performance than that of the cluster method based on Gaussian matching.
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