Efficient Multiplatform Motion Error Calibration Using Strong Scatterers With Position Uncertainty in Asynchronous Airborne Distributed Radars

ABSTRACT This work addresses the problem of multiplatform motion error calibration in an asynchronous airborne radar network with the employment of ground strong scatterers (SSs) at inaccurate locations. The multiradar time‐frequency synchronisation errors as well as motion errors, including position and velocity errors, would significantly impair signal coherence of airborne radar networks, provided that a potential target can share the same complex scattering coefficient to all airborne radars distributed within hundreds of yards. Hence, the network configuration calibration under time and frequency asynchronisation is required prior to coherent beamforming. Starting from the nonlinear time of arrival (TOA) and frequency of arrival (FOA) measurement equations, we develop an iterative reweighted least squares (IRLS) algorithm to obtain the deviations in positions, velocities, instants and frequencies of multiple radars during each iteration. By adding the deviations obtained from all iterations, a final error estimation is achieved and the evaluation of multiradar parameters is more refined. During algorithm development, we apply Taylor series expansion to eliminate nuisance parameters, followed by reweighted iterations to manage the remaining nonlinearity. This approach allows us to form linear equations for estimating multiradar parameter errors. Besides, we conduct the performance analysis of our method in comparison with the theoretical Cramér‐Rao lower bound (CRLB). Both theoretical derivations and simulation results confirm the effectiveness of our algorithm.