An Optimized Diffuse Kalman Filter for Frequency and Phase Synchronization in Distributed Radar Networks

Distributed radar networks have emerged as a key technology in remote sensing and surveillance due to their high transmission power and robustness against node failures. When performing coherent beamforming with multiple radars, frequency and phase deviations introduced by independent oscillators lead to a decrease in transmission power. This paper proposes an optimized diffuse Kalman filter (ODKF) for the frequency and phase synchronization. Specifically, each radar locally estimates its frequency and phase, then shares this information with neighboring nodes, which are used for incremental update and diffusion update to adjust local estimates. To further reduce synchronization errors, we incorporate a self-feedback strategy in the diffusion step, in which each node balances its own estimate with neighbor information by optimizing the diagonal weights in the diffusion matrix. Numerical simulations demonstrate the superior performance of the proposed method in terms of mean squared deviation (MSD) and convergence speed.