發(fā)布時(shí)間：2018-06-06 14:24

  本文選題：波達(dá)角度估計(jì) + 貝葉斯壓縮感知�。� 參考：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：作為陣列信號處理理論的重要研究方向之一，信號波達(dá)角度（Direction ofArrival，DOA）估計(jì)廣泛應(yīng)用于軍事及國民經(jīng)濟(jì)各個(gè)領(lǐng)域。經(jīng)典DOA估計(jì)算法需要信號源數(shù)目的先驗(yàn)知識和多快拍數(shù)的采樣數(shù)據(jù)，，這在實(shí)際應(yīng)用環(huán)境中較難實(shí)現(xiàn)，且當(dāng)輻射信號源具有相關(guān)性時(shí)，經(jīng)典算法的估計(jì)性能急劇下降甚至完全失效。貝葉斯壓縮感知（Bayesian Compressive Sensing, BCS）為陣列信號DOA估計(jì)提供了一個(gè)新的解決思路，該理論突破了Nyquist采樣定理的瓶頸，以基于統(tǒng)計(jì)模型的思想實(shí)現(xiàn)信號重構(gòu)。本文主要研究基于BCS的DOA估計(jì)，以克服傳統(tǒng)經(jīng)典算法的不足。 首先，結(jié)合窄帶遠(yuǎn)場陣列信號DOA估計(jì)模型，分別構(gòu)建了兩類基于BCS的DOA估計(jì)：一是實(shí)現(xiàn)實(shí)時(shí)估計(jì)的單快拍模型；二是獲取高分辨的多快拍模型，在此基礎(chǔ)上為了降低計(jì)算復(fù)雜度研究討論了對應(yīng)的快速算法。由于噪聲參數(shù)的辨識性作用顯著影響算法性能，為了改善這一缺陷，提高算法的魯棒性，進(jìn)一步研究了修正的貝葉斯壓縮感知理論�；谝陨瞎烙�(jì)模型，分別從信噪比、信號源角度間隔、陣元數(shù)、快拍數(shù)、相干信號源場景和計(jì)算復(fù)雜度六個(gè)方面仿真分析比較了經(jīng)典MUSIC算法、正交匹配追蹤（Orthogonal Matching Pursuit，OMP）算法以及BCS算法的估計(jì)性能。 最后，從信號源稀疏表示和超參數(shù)先驗(yàn)分布兩個(gè)方面對BCS算法進(jìn)行改進(jìn)，分別得到兩類改進(jìn)算法：（1）基于等正弦網(wǎng)格劃分的改進(jìn)算法，理論證明了同傳統(tǒng)等角度劃分方式相比，基于等正弦網(wǎng)格劃分對應(yīng)的陣列流形矩陣具有更優(yōu)的不一致性(Mutual Incoherence Property, MIP)。（2）基于Laplace先驗(yàn)的BCS-DOA估計(jì)，分別構(gòu)建了基于Laplace先驗(yàn)的單快拍和多快拍條件下的DOA估計(jì)模型。在此基礎(chǔ)上，從仿真分析的角度驗(yàn)證了兩種改進(jìn)算法的DOA估計(jì)性能優(yōu)于原始BCS算法，表明了兩類改進(jìn)算法的可行性。
[Abstract]:As one of the important research directions of array signal processing theory, signal DOA estimation is widely used in various fields of military and national economy. The classical DOA estimation algorithm requires prior knowledge of the number of signal sources and sampling data of multiple beats, which is difficult to implement in practical applications, and when the radiation signal source is correlated, The estimation performance of the classical algorithm drops sharply and even fails completely. Bayesian Compressive Sensing, BCS) provides a new solution for array signal DOA estimation. The theory breaks through the bottleneck of Nyquist sampling theorem and realizes signal reconstruction based on statistical model. In this paper, DOA estimation based on BCS is studied to overcome the shortcomings of traditional classical algorithms. Firstly, two kinds of DOA estimation models based on BCS are constructed by combining with the DOA estimation model of narrowband far-field array signals: one is a single-shot model to realize real-time estimation, the other is to obtain a high-resolution multi-shot model. In order to reduce the computational complexity, the corresponding fast algorithm is discussed. In order to improve the robustness and improve the robustness of the algorithm, the modified Bayesian compression sensing theory is further studied in order to improve the performance of the algorithm because of the effect of the identification of the noise parameters on the performance of the algorithm. Based on the above estimation model, the classical MUSIC algorithm is simulated and compared from six aspects, such as signal-to-noise ratio (SNR), angle interval of signal source, array element number, rapid-beat number, scene of coherent signal source and computational complexity. The estimation performance of orthogonal Matching pursuit (OMP) algorithm and BCS algorithm. Finally, the BCS algorithm is improved from two aspects of signal source sparse representation and super-parameter prior distribution, and two improved algorithms, one of which is based on equal-sinusoidal mesh generation, are obtained, respectively. The theoretical proof is compared with the traditional equal-angle partition method. Based on the BCS-DOA estimation of Laplace priori, the DOA estimation models based on Laplace priori single rapid-beat and multi-rapid-beat are constructed, respectively, based on the array manifold matrix corresponding to the equal-sinusoidal mesh division with more inconsistency and mutual Incoherence property, and based on the BCS-DOA estimator of Laplace priori, respectively. On this basis, from the point of view of simulation analysis, the DOA estimation performance of the two improved algorithms is better than that of the original BCS algorithm, which shows the feasibility of the two improved algorithms.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.23

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本文編號：1986920