基于迭代线性约束最小方差的稳健自适应脉冲压缩方法

Robust Adaptive Pulse Compression Algorithm Based on Reiterative Linearly Constrained Minimum Variance

针对常规自适应脉冲压缩方法在目标散射点与采样中心失配时旁瓣抑制性能下降的问题,该文提出一种基于迭代线性约束最小方差(RLCMV)的自适应脉冲压缩方法。该方法首先将自适应波束形成器算法引入到自适应脉冲压缩滤波器设计中。其次对目标及干扰单元进行线性约束,并用对角加载技术避免矩阵出现病态。最后构造了迭代运算方法,依次抑制不同大小目标的距离旁瓣。仿真结果表明,该算法可以有效抑制散射点随机分布目标的距离旁瓣,对散射点与采样中心失配情况具有较好的稳健性,在多目标及距离扩展目标场景中达到较好的旁瓣抑制性能,并在一定程度上提高了多普勒容性。

In order to solve the problem of range side-lobes suppression performance degradation due to error between scatter and sample center of tradition methods, a new adaptive pulse compression algorithm based on Reiterative Linearly Constrained Minimum Variance（RLCMV） is presented in this paper. Firstly, adaptive beamformer is introduced into adaptive pulse compression. Then, linearly constraint is forced on range bins of target and interference, and diagonal loading techniques are applied. Finally, reiterative method is presented to suppress the side-lobe of target in range of different Radar Cross Section（RCS）. Simulation results show that this algorithm can effectively suppress side-lobes of scatters random distributed in range bin, and it is robust to error between scatters and sample center. It keeps excellent performance even in multi-targets and rangeextended target scenario, and improves performance of high Doppler target to a certain extend.