Distance Velocity Fusion Algorithm Based on Sequential Monte Carlo Probability Hypothesis Density Filter in Low-Zero Power Scenario

In low-zero power scenarios, certain sensors exhibit measurement inaccuracies, and the disparity in measurement precision among networked sensors leads to data inequality. This results in poor fusion accuracy in the multisensor fusion process, particularly when prior weights are unknown. To address the aforementioned problem, a distance velocity (DV) fusion algorithm was designed based on the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) algorithm. Specifically, after the state extraction step of the SMC-PHD filter algorithm, the final estimated target was obtained in two steps: judgment and weighted summation. The simulation results demonstrate the effectiveness of the proposed algorithm in improving fusion accuracy and robustnes.