基于壓縮感知理論的信源定位算法研究
發(fā)布時(shí)間：2019-06-03 13:59
【摘要】：陣列信號(hào)處理作為現(xiàn)代信號(hào)處理領(lǐng)域當(dāng)中的一個(gè)重要分支,廣泛應(yīng)用于軍用和民用領(lǐng)域,本文著重研究其中的信源定位算法。根據(jù)信源與陣列天線間的距離,信源分為遠(yuǎn)場(chǎng)信源和近場(chǎng)信源,遠(yuǎn)場(chǎng)信源僅由波達(dá)方向(Direction of Arrival,DOA)表征而近場(chǎng)信源同時(shí)需要DOA和距離參數(shù)表征。基于壓縮感知理論的信源定位算法能夠降低所需快拍數(shù)并提高定位精度,為解決信源定位問題提供了新的思路。論文主要工作如下:(1)研究遠(yuǎn)近場(chǎng)信源定位模型及經(jīng)典方法。研究遠(yuǎn)場(chǎng)、近場(chǎng)和遠(yuǎn)近混合場(chǎng)信源的陣列接收數(shù)據(jù)模型,仿真驗(yàn)證經(jīng)典的遠(yuǎn)場(chǎng)DOA估計(jì)算法和混合場(chǎng)信源定位算法的性能;仿真三種信源定位場(chǎng)景下參數(shù)估計(jì)的克拉美羅界。(2)研究基于壓縮感知理論的信源定位模型及算法。研究將壓縮感知理論用于信源定位問題的稀疏模型,驗(yàn)證凸優(yōu)化算法和貪婪算法用于DOA估計(jì)的性能以及仿真驗(yàn)證一種結(jié)合壓縮感知和四階累積量的混合場(chǎng)定位算法。本文創(chuàng)新點(diǎn)如下:(1)改進(jìn)一種基于OMP(Orthogonal Matching Pursuit)算法的DOA估計(jì)方法。通過研究導(dǎo)向矢量間的相關(guān)性,分析壓縮感知理論用于信源定位的不確定性,以及通過對(duì)接收數(shù)據(jù)進(jìn)行向量化,研究協(xié)方差域的操作對(duì)重構(gòu)算法的性能影響。改進(jìn)一種基于OMP算法的改進(jìn)DOA估計(jì)算法,該算法能夠自適應(yīng)利用先驗(yàn)信息,在低信噪比和少快拍情況下的估計(jì)性能優(yōu)勢(shì)明顯。(2)改善兩種傳統(tǒng)定位算法的空間譜并提出一種新的混合場(chǎng)信源定位算法。對(duì)兩種傳統(tǒng)混合場(chǎng)信源定位算法進(jìn)行改進(jìn),用稀疏重構(gòu)算法改善其空間譜。運(yùn)用壓縮感知理論提出一種適應(yīng)混合場(chǎng)定位算法,該算法只適用于多頻點(diǎn)信號(hào),通過對(duì)接收數(shù)據(jù)的不同頻率分量聯(lián)合稀疏表示,可以提高定位精度并且能夠定位與陣元數(shù)相同的信源。該算法對(duì)于純近場(chǎng)信源定位的情況同樣適用。
[Abstract]:Array signal processing, as an important branch of modern signal processing, is widely used in military and civil fields. This paper focuses on the source location algorithm. According to the distance between the source and the array antenna, the source is divided into far field source and near field source. The far field source is only represented by the direction of arrival (Direction of Arrival,DOA), while the near field source needs DOA and distance parameter representation at the same time. The source location algorithm based on compressed perception theory can reduce the number of fast beats and improve the positioning accuracy, which provides a new idea for solving the problem of source location. The main work of this paper is as follows: (1) the far and near field source location model and classical methods are studied. The array receiving data model of far-field, near-field and far-near mixed field sources is studied, and the performance of classical far-field DOA estimation algorithm and hybrid field source location algorithm is verified by simulation. The Keramero bound of parameter estimation in three source localization scenarios is simulated. (2) the source location model and algorithm based on compressed perception theory are studied. In this paper, the sparse model of compressed sensing theory for source location is studied, the performance of convex optimization algorithm and greedy algorithm for DOA estimation is verified, and a hybrid field location algorithm combining compressed perception and fourth-order cumulant is verified by simulation. The innovations of this paper are as follows: (1) an improved DOA estimation method based on OMP (Orthogonal Matching Pursuit) algorithm. By studying the correlation between guidance vectors, the uncertainty of compressed perception theory used in source location is analyzed, and the effect of covariance domain operation on the performance of reconstruction algorithm is studied by vector quantification of received data. An improved DOA estimation algorithm based on OMP algorithm is improved, which can adaptively make use of prior information. In the case of low signal-to-noise ratio (SNR) and low fast beat, the estimation performance has obvious advantages. (2) the spatial spectrum of the two traditional localization algorithms is improved and a new hybrid field source location algorithm is proposed. Two traditional hybrid field source localization algorithms are improved, and their spatial spectrum is improved by sparse reconstruction algorithm. Based on the compressed sensing theory, an adaptive hybrid field location algorithm is proposed, which is only suitable for multi-frequency point signals, and is represented by sparse representation of different frequency components of the received data. It can improve the positioning accuracy and locate the same number of sources as the array elements. The algorithm is also suitable for the location of pure near-field sources.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7
本文編號(hào)：2491984
[Abstract]:Array signal processing, as an important branch of modern signal processing, is widely used in military and civil fields. This paper focuses on the source location algorithm. According to the distance between the source and the array antenna, the source is divided into far field source and near field source. The far field source is only represented by the direction of arrival (Direction of Arrival,DOA), while the near field source needs DOA and distance parameter representation at the same time. The source location algorithm based on compressed perception theory can reduce the number of fast beats and improve the positioning accuracy, which provides a new idea for solving the problem of source location. The main work of this paper is as follows: (1) the far and near field source location model and classical methods are studied. The array receiving data model of far-field, near-field and far-near mixed field sources is studied, and the performance of classical far-field DOA estimation algorithm and hybrid field source location algorithm is verified by simulation. The Keramero bound of parameter estimation in three source localization scenarios is simulated. (2) the source location model and algorithm based on compressed perception theory are studied. In this paper, the sparse model of compressed sensing theory for source location is studied, the performance of convex optimization algorithm and greedy algorithm for DOA estimation is verified, and a hybrid field location algorithm combining compressed perception and fourth-order cumulant is verified by simulation. The innovations of this paper are as follows: (1) an improved DOA estimation method based on OMP (Orthogonal Matching Pursuit) algorithm. By studying the correlation between guidance vectors, the uncertainty of compressed perception theory used in source location is analyzed, and the effect of covariance domain operation on the performance of reconstruction algorithm is studied by vector quantification of received data. An improved DOA estimation algorithm based on OMP algorithm is improved, which can adaptively make use of prior information. In the case of low signal-to-noise ratio (SNR) and low fast beat, the estimation performance has obvious advantages. (2) the spatial spectrum of the two traditional localization algorithms is improved and a new hybrid field source location algorithm is proposed. Two traditional hybrid field source localization algorithms are improved, and their spatial spectrum is improved by sparse reconstruction algorithm. Based on the compressed sensing theory, an adaptive hybrid field location algorithm is proposed, which is only suitable for multi-frequency point signals, and is represented by sparse representation of different frequency components of the received data. It can improve the positioning accuracy and locate the same number of sources as the array elements. The algorithm is also suitable for the location of pure near-field sources.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 梁國(guó)龍;韓博;林旺生;王丹;;基于稀疏信號(hào)重構(gòu)的近場(chǎng)源定位[J];電子學(xué)報(bào);2014年06期
2 蔣佳佳;段發(fā)階;陳勁;常宗杰;;一種高精度的近場(chǎng)與遠(yuǎn)場(chǎng)混合源定位算法[J];天津大學(xué)學(xué)報(bào)(自然科學(xué)與工程技術(shù)版);2013年12期
，本文編號(hào)：2491984
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