Calibration of Linear Time-Varying Frequency Errors for Distributed ISAR Imaging Based on the Entropy Minimization Principle

Abstract

The inevitable frequency errors owing to the frequency mismatch of a transmitter and receiver oscillators could seriously deteriorate the imaging performance in distributed inverse synthetic aperture radar (ISAR) system. In this paper, for this issue, a novel method is proposed to calibrate the linear time-varying frequency errors (LTFE) between the transmitting node and the receiving node. The cost function is constructed based on the entropy minimization principle and the problem of LTFE calibration is transformed into cost function optimization. The frequency error coefficient, which minimizes the image entropy, is obtained by searching optimum solution in the solution space of cost function. Then, the original signal is calibrated by the frequency error coefficient. Finally, the effectiveness of the proposed method is demonstrated by simulation and real-data experiments.

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

“…The performance of the proposed method is better than that of the method in [23] because image entropy is more sensitive to noise than image contrast. When the optimal value of is searched by the method in [23], the minimum entropy must be found accurately at first. However, in the case of low SNR, noise has a great influence on image entropy, which may result in finding a wrong value of minimum entropy.…”

“…However, the entropy of image 1 may be smaller than that of image 2 under low SNR conditions. According to the method in [23], it can be seen that the estimation value of is not accurate when the calculated minimum entropy is wrong. As a result of that, the LTFE cannot be effectively calibrated.…”

“…2. The method in [23] is based on the relationship between image entropy and QPC, which is shown in Fig. 4.…”

“…These methods usually increase the system complexity and hardware cost. In our previous work [23], an LTFE calibration method based on entropy minimisation principle is proposed, which gets rid of the hardware complexity compared with [21, 22]. However, this method is not robust enough, and its performance deteriorates sharply under low signal‐to‐noise ratio (SNR) conditions.…”

“…The particle swarm optimisation (PSO) algorithm [26] is used to search the global optimal solution of frequency error coefficient, which can avoid falling into the local optimum because of its strong ability of global optimisation. Compared with the method in [23], the proposed method can improve the computational efficiency by using the PSO algorithm to search the optimal solution.…”