A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief Propagation

In this paper, we propose the specific recursion formula for generalized labeled multi-Bernoulli filter based on track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, algorithm improves tracking accuracy without pruning operation preserve relevant association information. performance in is validated simulated scenarios OSPA(2) metric. More importantly, simulation results demonstrate that outputs both version computational cost due linear complex number Bernoulli components measurements.