基于最小熵的双基前视 SAR二阶差分运动补偿方法研究
Research on BFSAR Motion Compensation with Second-order Difference Method via Minimum Entropy
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摘要: 相较于传统的单基合成孔径雷达(Synthetic Aperture Radar,SAR)成像系统,双基前视合成孔径雷达(Bistatic forward-looking SAR,BFSAR)系统由于其特殊的构型与成像优势逐渐受到国内外科研工作者的关注。BFSAR系统通过将发射天线和接收天线分置在两个平台上,可以完成单基SAR不可实现的前视成像功能,因此在对海监视、目标探测和军事制导等领域具有十分重要的应用价值。然而,由于BFSAR系统收发分置,两个雷达平台在飞行过程中均存在运动误差,使得系统录取的回波数据产生比单基SAR更加复杂的相位误差,最终导致目标图像散焦。针对这一问题,该文提出一种基于最小熵准则的二阶差分运动补偿方法。首先将目标一维距离压缩后的回波数据作为处理对象,通过二阶差分法估计目标所在各个距离单元回波信号的相位误差,采用该相位误差对回波信号进行补偿;然后结合距离-多普勒(Range Doppler,RD)算法对运动补偿后的回波信号进行方位向压缩获得目标图像,并通过计算图像熵找出使熵值达到最小的最优相位误差;最后基于该相位误差对回波信号进行补偿,采用RD算法对补偿后的回波信号进行方位向压缩,便可以得到清晰聚焦的目标图像。该方法无须惯导信息,可从回波数据中直接估计相位误差完成运动补偿。仿真结果表明,该方法可以有效补偿收发平台运动误差对回波信号相位与多普勒参数造成的影响,补偿后目标图像的聚焦质量接近于理想图像。Abstract: Compared with traditional monostatic synthetic aperture radar imaging systems, bistatic forward-looking synthetic aperture radar (BFSAR) systems have increasingly attracted the attention of researchers both domestically and internationally, owing to their unique configuration and imaging advantages. By placing the transmitting and receiving antennas on separate platforms, the BFSAR system enables forward-looking imaging capabilities that are not achievable with monostatic SAR systems. Therefore, the BFSAR system holds significant application value in areas such as maritime surveillance, target detection, and military guidance. However, due to the separation of the transmitter and receiver of the BFSAR system, each radar platform is subjected to motion-induced errors during flight. Compared with monostatic SAR systems, these motion errors introduce more complex phase distortions in the received echo data captured by the system, ultimately resulting in image defocusing of the target. To address this issue, a second-order difference motion compensation algorithm based on the minimum entropy criterion is proposed in this study. First, the echo data of the target, after one-dimensional range compression, is used as the processing object. The second-order difference method is then applied to estimate the phase errors of the echo signals corresponding to each range cell occupied by the target. These estimated phase errors are subsequently used to compensate for the echo signals. Next, the compensated echo signal is compressed in the azimuth direction using the Range Doppler (RD) algorithm to generate the target image. The image entropy is then calculated, and the optimal phase error that minimizes the entropy is selected. Finally, the phase error is used to further compensate the echo signal, and the RD algorithm is applied once more in the azimuth domain to produce a well-focused target image. This algorithm does not rely on inertial navigation data and can directly estimate phase errors from the echo data to perform motion compensation. Simulation results demonstrate that the proposed algorithm effectively mitigates the impact of the platform motion errors on the phase and Doppler characteristics of the echo signal. Consequently, the resulting image exhibits a focusing quality comparable to that of an ideal reference image.