Adaptive Frequency and Phase Synchronization for Distributed Coherent Aperture Radar Under Heterogeneous Noise

To achieve the coherence gain of a Distributed Coherent Aperture Radar (DCAR) network, it is essential to ensure frequency and phase synchronization among all radar nodes, which is challenging especially when each node operates in a distinctly noisy environment. This paper aims to develop an adaptive distributed processing approach for frequency and phase synchronization in a DCAR network under heterogeneous noise conditions. We formulate the synchronization problem as a weighted consensus optimization that accounts for spatially varying noise characteristics across distributed radar nodes. The proposed Weighted Least Mean Squared Deviation (W-LMSD) framework derives optimal weight matrices by minimizing the steady-state mean squared deviation of synchronization errors, establishing an explicit relationship between mixing matrix design and synchronization accuracy. For unknown noise statistics, we develop an Adaptive Distributed Frequency and Phase Consensus (A-DFPC) algorithm that integrates variational Bayesian Kalman filtering to simultaneously estimate node-specific noise covariances and compute optimal weights. Simulation results demonstrate significant improvements over existing methods, with A-DFPC achieving near-optimal synchronization performance and ensuring the operation of the DCAR network.