基于MMSE估计的自适应脉冲压缩

摘要在复杂的环境中，雷达信号的脉冲压缩一直存在小目标的主瓣会被大目标的旁瓣掩盖的问题，传统的脉冲压缩方法如匹配滤波虽然能够获得最大的输出信噪比，但是无法很好的解决掩盖问题，这就需要自适应的脉冲压缩方法来解决临近目标的干扰带来的掩盖问题。本次毕业设计研究了各种典型的旁瓣抑制算法的基本原理，讨论了迭代的最小均方误差算法提高计算效率和鲁棒性的办法，仿真并比较了匹配滤波、失配滤波、最小均方误差滤波这三种算法在不同目标运动特性下的输出结果，分析了多普勒效应对目标分辨的影响，利用RMMSE算法实现了P3码回波信号脉冲压缩结果的高主旁瓣比性能，分析了RMMSE算法对噪声调频脉冲回波信号的旁瓣抑制性能。42904

关键词自适应脉冲压缩 MMSE 匹配滤波失配滤波

毕业论文设计说明书外文摘要

Title Adaptive Pulse Compression via MMSE Estimation

Abstract

In a complex environment, a problem inherent to pulse compression of Radar signals is that the main lobe of small targets will be masked by sidelobes of large targets nearby. Traditional ways of pulse compression, such as match filtering, can achieve maximum output SNR, while the masking problem cannot be solved effectively. So an adaptive pulse compression is needed to solve the masking problem due to the influence of close target. Basic theories of several typical sidelobe-suppression algorithm are studied in this graduation project, and the approach to to improve computational efficiency and robustness of reiterative minimum-square error estimation is discussed. I also simulated and compared the output of the matched filter, the mismatched filter , the minimum mean-square error filter about targets of different motion characteristics, make analysis of the influence of the Doppler effect on the target distinction. At last, high peak-to-sidelobe ratio of P3 code signal’s and noise frequency modulation signal’s pulse compression result is realized by RMMSE algorithm.

Keywords adaptive pulse compression MMSE match filtering mismatch filtering

1 绪论 1

2 回波信号分析与仿真 3

2.1 引言 3

2.2 匹配滤波 4

2.3 失配滤波 5

3 最小均方误差滤波器 7

3.1 自适应算法简介 7

3.2 迭代的最小均方误差算法推导 8

3.3 迭代的最小均方误差算法讨论 13

3.3.1 单独点目标回波的MMSE滤波 13

3.3.2 大、小目标的MMSE滤波 14

4 算法实现 15

4.1 计算效率 15

4.2 鲁棒性 16

5 P3码回波信号处理仿真结果 18

5.1 引言 18

5.2 点目标回波信号处理 19

5.2.1 低SNR点目标 19

5.2.2 高SNR点目标 20

5.3 有高SNR点目标存在于处理窗口外的回波处理 21

5.4 速度5马赫的点目标的回波处理基于MMSE估计的自适应脉冲压缩:http://www.youerw.com/zidonghua/lunwen_43523.html