Real-Time Correlation Estimation for Distributed Radar Systems Using Bayesian Updating

This paper addresses the real-time estimation of correlation coefficients between received signals in distributed radar systems, where correlation estimates must be updated sequentially with each newly acquired sample group to support immediate processing decisions. To this end, a recursive Bayesian updating framework is proposed and compared with traditional Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) methods. A class of Quasi-Swerling target models is constructed to simulate echo characteristics, and the estimation performance is evaluated under varying sample sizes. Unlike batch estimators, the proposed method supports online inference with minimal latency, enabling real-time adaptability in cluttered and dynamic environments. Simulation results show that while MLE and MAP converge faster with sufficient data, the Bayesian updating method demonstrates superior stability and lower estimation error under small-sample conditions, making it more suitable for real-time applications.