基于Chirp-z变换的低复杂度双基前视SAR扩展NLCS成像算法

Low Complexity Bistatic Forward-looking SAR Extended NLCS Algorithm Based on Chirp-z Transform

双基合成孔径雷达(SAR)由于收发分置的特殊几何构型,具备对前视区域高分辨成像的优势,在精确制导、自主着陆、对敌打击等领域有着重要应用价值。但是双基前视SAR具有强回波二维耦合特性,成像算法需要的处理流程更为复杂。扩展非线性变标(ENLCS)算法虽然能够有效解决这种构型下空变的距离徙动(RCM)和多普勒参数问题,但在将雷达数据从回波域映射至图像域的过程中,会带来一定的数据量冗余,而且ENLCS的方位向处理流程过长,导致算法的数据量和存储成本大大增加,很难满足相关领域实时性的要求。为了降低ENLCS算法的运算复杂度与数据冗余度,提高算法实时成像处理的效率,文中通过在距离徙动校正(RCMC)过程中采用Chirp-z变换(CZT)结构实现无插值的Keystone变换(KT)降低运算复杂度,并在ENLCS均衡过程中加入线性因子显著降低数据映射过程中的冗余量,进一步对算法方位向处理过程加速。与原算法相比,总体运算量及耗时更低。仿真和实测数据的处理结果验证了算法的有效性和高时效性。

Due to the special geometric configuration of transmitter and receiver, bistatic synthetic aperture radar(SAR) can image the forward-looking area with high resolution, and has important application value in the fields of precision guidance, autonomous landing, and enemy strike. However, bistatic forward-looking SAR has severe 2-D coupling in echo, and the imaging algorithm requires more complex processing flow. Although the extend nonlinear chirp scaling(ENLCS) algorithm can effectively solve the problem of space-variant migration and Doppler parameters under this configuration, it brings a certain amount of data redundancy in the process of mapping radar data from the echo domain to the image domain. Moreover, the azimuth processing of ENLCS is too long, causing a great increase in computational load and storage costs, and it is difficult to meet the real-time requirements of related fields. In order to reduce the computational complexity and data redundancy of the ENLCS algorithm and improve its efficiency of the real-time processing, this paper utilizes Chirp-z transform(CZT) structure to realize the Keystone transform(KT) without interpolation in the process of range cell migration correction(RCMC) to reduce the computational complexity. In addition, linear factor is added to the ENLCS equalization process, which significantly reduces the amount of redundancy in the data mapping process, and further accelerates the azimuth processing process of the algorithm, which is lower in overall operation and time-consuming than that of the original algorithm. The simulation experiment results verify the effectiveness and timeliness of the proposed algorithm.