Centralized Multi-Sensor GLMB Smoother for Multi-Target Tracking

Aiming to improve the accuracy of multi-target tracking in multi-sensor scenarios, this paper proposes a centralized multi-sensor (MS) generalized labeled multi-Bernoulli (GLMB) smoother, abbreviated as MS-GLMB-S. The developed smoother is built on the multi-target forward–backward Bayesian smoothing framework, which uses an MS-GLMB filter for forward recursion and is subsequently followed by backward propagation via the multi-sensor backward corrector to obtain the GLMB smoothing density. In the backward smoothing process, expressions for the multi-sensor backward corrector and the multi-target smoothing density are detailed. By deriving the time-decoupled form of the smoothing weight, a suboptimal Gibbs sampling method is introduced to achieve efficient implementation of the proposed smoother, enabling independent sampling across each sensor at different time steps within the lag interval during the backward smoothing process. Additionally, a Gaussian mixture implementation of MS-GLMB-S is formulated. Simulations conducted in both linear and nonlinear scenarios demonstrate the effectiveness and real-time performance of MS-GLMB-S.