Robust calibration method for distributed ISAR time-varying frequency errors based on the contrast maximisation principle

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition, this method uses enumeration algorithm to solve the optimization problem, which has a heavy computation burden. Therefore, a robust calibration method based on the contrast maximization principle is proposed. Compared with image entropy, image contrast has better anti-noise ability because it has better sensitivity property, namely, the change of image contrast is sharper than the change of image entropy. In the proposed method, the estimation of frequency error coefficient is modelled as an unconstrained optimization problem with image contrast as cost function, and the particle swarm optimization (PSO) algorithm is used to search the global optimal solution. Then, the LTFE can be calibrated by the estimated frequency error coefficient. The proposed method has better robustness, which can work well under low SNR conditions. Besides, it has higher computational efficiency. Simulations are carried out to verify the effectiveness and robustness of the proposed method.