| Summary: | Aiming at the problem of missed targets that easily occur when there are large differences in signal-to-noise ratio, this paper proposes an improved multi-target dual-layer particle filter track-before-detect algorithm(IM-PF-TBD). The algorithm uses a two-layer particle filter structure. In the target detection layer, the method of tournament selection is used to resample the detected particle group, select multiple particles with large weight differences, and simultaneously detect multiple targets through clustering. The existence probability of weak target in early detection is improved. In addition, the algorithm proposes a particle swarm fusion method for verification of newly discovered targets, which facilitates the removal of false targets after target detection. Simulation results show that the proposed algorithm can effectively improve the detection probability of targets with small signal-to-noise ratio and reduce the target RMSE.
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