A Multiple Targets Direct Tracking Algorithm Based on Generalized Labeled Multi-Bernoulli Filter

Multi-target tracking (MTT) is essential in various applications, including autonomous driving, surveillance, and electronic warfare. However, traditional two-stage tracking methods often suffer from performance degradation in low signal-to-noise ratio (SNR) cases due to information loss during parameter estimation in the first stage. This paper presents a direct tracking algorithm based on the Generalized Labeled Multi-Bernoulli (GLMB) filter, which avoids intermediate parameter estimation by directly constructing the cost function from the received signals. By leveraging the GLMB filter to jointly estimate the number and states of multiple targets, the proposed method can achieve robust target tracking in complex and dynamic environments. Simulation results demonstrate that the proposed algorithm exhibits superior tracking accuracy compared to traditional multi-target tracking methods.