本文为土耳其比尔肯特大学（作者：Rasim Akın Sevimli）的硕士论文，共112页。

无源双基地雷达（PBR）系统近年来在许多研究领域和国家得到了越来越广泛的应用。与PBR系统相关的论文在研究中越来越受到重视，目前国内外对PBR系统的目标检测方法很多。本文假设了一个基于立体声调频信号作为机会发射机的系统方案，模糊函数（AF）是一种在实际应用中确定距离-多普勒二维谱中目标位置的函数。这可能导致低信噪比下的目标检测问题，为了解决这一问题，采用压缩传感（CS）和投影到l1球（PES-l1）上的图集对距离-多普勒图进行去噪。将CS方法应用于该系统的场景，包括基跟踪（BP）、正交匹配跟踪（OMP）、压缩采样匹配跟踪（CoSaMP）、迭代硬阈值（IHT）。此外，AF通常用于确定两个信号之间的相似性。因此，也可以使用不同的相关方法来比较参考信号与其延时频移复制信号之间的关系。采用最大信息系数（MIC）、皮尔逊相关系数、斯皮尔曼秩相关系数进行目标检测。本文提出了一种基于最小二乘（LS）的方法，该方法在PSNR和SNR方面优于其他相关算法；利用调制后的参考信号预测监视信号的实部和虚部，可以得到两个LS系数。LS系数的范数将在目标所在位置呈现峰值，该方法比普通的AF方法能更好地检测近距离目标，并减少了PBR系统中多个调频信道的旁瓣数目。

Passive Bistatic Radar (PBR) systems have become more popular in recent years in many research communities and countries. Papers related to PBR systems have increasingly received significant attention in research. There are many target detection methods for PBR system in the literature. This thesis assumes a system scenario based on stereo FM signals as transmitters of opportunity. Ambiguity function (AF) is a function that determines the locations of targets in range-Doppler map turns out to be noisy in practice. This can cause a problem with low SNR-valued targets because they cannot be visible. To solve this problem, compressive sensing (CS) and projection onto the epigraph set of the ‘1 ball (PES-‘1) are used to denoise the range-Doppler map. Some CS methods are applied to the system scenario, which are Basis Pursuit (BP), Orthogonal Matching Pursuit (OMP), Compressed Sampling Matching Pursuit (CoSaMP), Iterative Hard Thresholding (IHT). In addition, AF is generally used to determine the similarities between two signals. Therefore, different correlation methods can be also used to compare the surveillance and time delayed frequency shifted replica of the reference signal. Maximal Information Coefficient (MIC), Pearson correlation coefficient, Spearman's rank correlation coefficient are used for the target detection. This thesis proposes a least squares (LS) based method which outperforms other correlation algorithms in terms of PSNR and SNR. Two LS coefficients are obtained from the real and imaginary parts of predicting the surveillance signal using the modulated reference signal. Norm of LS coefficients exhibit a peak at target locations. The proposed method detects close targets better than the ordinary AF method and decreases the number of sidelobes on multiple FM channels based the PBR system.

1 引言

1.1 本文概述及相关贡献

2 无源双基地雷达的特点

2.1 双基地的几何形式

2.2 双基地雷达方程

2.3 系统场景

2.4 模糊函数

2.5 杂波消除与检测目标的CFAR算法

2.6 小结

3 基于压缩感知和PES-l1的距离-多普勒目标检测的降噪算法

3.1 压缩感知

3.2 重建算法

3.3 PES-l1

3.4 仿真结果

3.5 小结

4 无源雷达目标检测的新相关算法

4.1 比较两个信号相关性的算法

4.2 仿真结果

4.3 小结

5 结论与未来工作展望

5.1 未来工作展望

附录A 立体声调频信号 

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