本文為土耳其比爾肯特大學（作者：Rasim Akın Sevimli）的碩士論文，共112頁。
無源雙基地雷達（PBR）系統近年來在許多研究領域和國家得到了越來越廣泛的應用。與PBR系統相關的論文在研究中越來越受到重視，目前國内外對PBR系統的目标檢測方法很多。本文假設了一個基于立體聲調頻信号作為機會發射機的系統方案，模糊函數（AF）是一種在實際應用中确定距離-多普勒二維譜中目标位置的函數。這可能導緻低信噪比下的目标檢測問題，為了解決這一問題，采用壓縮傳感（CS）和投影到l1球（PES-l1）上的圖集對距離-多普勒圖進行去噪。将CS方法應用于該系統的場景，包括基跟蹤（BP）、正交比對跟蹤（OMP）、壓縮采樣比對跟蹤（CoSaMP）、疊代硬門檻值（IHT）。此外，AF通常用于确定兩個信号之間的相似性。是以，也可以使用不同的相關方法來比較參考信号與其延時頻移複制信号之間的關系。采用最大資訊系數（MIC）、皮爾遜相關系數、斯皮爾曼秩相關系數進行目标檢測。本文提出了一種基于最小二乘（LS）的方法，該方法在PSNR和SNR方面優于其他相關算法；利用調制後的參考信号預測監視信号的實部和虛部，可以得到兩個LS系數。LS系數的範數将在目标所在位置呈現峰值，該方法比普通的AF方法能更好地檢測近距離目标，并減少了PBR系統中多個調頻信道的旁瓣數目。
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 本文概述及相關貢獻
2 無源雙基地雷達的特點
2.1 雙基地的幾何形式
2.2 雙基地雷達方程
2.3 系統場景
2.4 模糊函數
2.5 雜波消除與檢測目标的CFAR算法
2.6 小結
3 基于壓縮感覺和PES-l1的距離-多普勒目标檢測的降噪算法
3.1 壓縮感覺
3.2 重建算法
3.3 PES-l1
3.4 仿真結果
3.5 小結
4 無源雷達目标檢測的新相關算法
4.1 比較兩個信号相關性的算法
4.2 仿真結果
4.3 小結
5 結論與未來工作展望
5.1 未來工作展望
附錄A 立體聲調頻信号
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