Abstract:

To address the key problem in multi-target tracking under complex interference environments-where the time-varying number of targets， complex motion modes， and prominent “false target” interference lead to significant degradation of tracking accuracy-this paper proposes a Confidence-Driven Adaptive Gaussian Mixture Probability Hypothesis Density （CA-GMPHD） filtering algorithm， which optimizes the core links of traditional GMPHD filtering through the innovative design of four types of coordinated adaptive mechanisms： specifically， the likelihood layer dynamically adjusts the measurement covariance based on measurement confidence to improve the matching accuracy between high-confidence measurements and targets， the prior layer dynamically modifies the detection probability and clutter intensity model to reduce the risk of low-confidence measurements being misjudged as real targets， the fusion layer embeds a confidence power weight factor in the weight update process to enhance the fusion efficiency of prior confidence information and geometric consistency constraints， and the structure layer adaptively optimizes the pruning and merging thresholds based on the global average confidence to effectively suppress the expansion of the number of filter components in strong clutter scenarios. To verify the algorithm performance， simulation scenarios including multi-sensor， multi-target， and two types of typical strong interference sources （chaff clouds and corner reflectors） are constructed， where the measurement confidence is generated by mapping the detector output probability through a mapping function， and experimental results show that compared with the standard GMPHD algorithm， the proposed CA-GMPHD algorithm significantly reduces both the global Root Mean Square Error （RMSE） and Optimal Subpattern Assignment （OSPA）-two core evaluation metrics-while maintaining computational efficiency， remarkably improving the accuracy and robustness of multi-target tracking under complex interference environments and possessing important theoretical significance and engineering application value.

Key words: multi-target tracking, GMPHD filtering, confidence-driven, adaptive measurement covariance, high clutter scenario