Bayesian integrated tracking and communication with random finite set observations

This paper proposes a novel Bayesian integrated tracking and communication (ITAC) framework. By consolidating the time of arrival (ToA) and angle of departure (AoD) measurement models, a robust Bayesian filtering framework is introduced for single-target tracking in dynamic and cluttered wireless environments. By incorporating random finite set(RFS) theory, the method jointly estimates the target’s existence probability and its kinematic state within a unified Bayesian recursion, effectively overcoming the limitations of conventional filters, which assume that the target can always be detected. Furthermore, the paper derives the signal-to-interference-plus noise ratio (SINR) for both communication and sensing links. The proposed RFS-based Bayesian filter is implemented using a sequential Monte Carlo (SMC) approach. Numerical simulations demonstrate the framework’s superior performance in maintaining tracking consistency and accuracy even under challenging propagation conditions. Moreover, the results reveal how different network parameters affect the overall communication and tracking performance.