舰船电子工程
艦船電子工程
함선전자공정
Ship Electronic Engineering
2015年
9期
63-67,143
,共6页
波达方向估计%正则化 FOCUSS 算法%稀疏重构%协方差矩阵
波達方嚮估計%正則化 FOCUSS 算法%稀疏重構%協方差矩陣
파체방향고계%정칙화 FOCUSS 산법%희소중구%협방차구진
direction of arrival estimation%regularized FOCUSS algorithm%sparse reconstruction%covariance matrix
传统的波达方向（DOA）估计方法往往受到 Nyquist 采样定理与“瑞利限”的限制，对快拍数、阵元数及信噪比等条件的要求较高，并且不能准确估计信号源的幅度信息。基于目标在空域的稀疏性，针对多维观测向量模型，提出一种正则化的 FOCUSS 稀疏重构算法，可以有效提高低信噪比条件下的估计性能。阵列接收矩阵快拍数大，含噪声信息多，对高分辨的 DOA 估计影响较大，而通过对阵列的协方差矩阵求高阶次幂的方法可以有效逼近信号子空间，减小噪声子空间的影响。以阵列接收数据的协方差矩阵作为待分解的数据向量构造稀疏模型，能够使重构信号具有较高的分辨率，对快拍数、阵元数及信噪比等条件的要求更低，对旁瓣抑制效果更好，能够较为准确地估计出信源的幅度信息，且不需要对信源数目进行预估计，体现出明显的优势。
傳統的波達方嚮（DOA）估計方法往往受到 Nyquist 採樣定理與“瑞利限”的限製，對快拍數、陣元數及信譟比等條件的要求較高，併且不能準確估計信號源的幅度信息。基于目標在空域的稀疏性，針對多維觀測嚮量模型，提齣一種正則化的 FOCUSS 稀疏重構算法，可以有效提高低信譟比條件下的估計性能。陣列接收矩陣快拍數大，含譟聲信息多，對高分辨的 DOA 估計影響較大，而通過對陣列的協方差矩陣求高階次冪的方法可以有效逼近信號子空間，減小譟聲子空間的影響。以陣列接收數據的協方差矩陣作為待分解的數據嚮量構造稀疏模型，能夠使重構信號具有較高的分辨率，對快拍數、陣元數及信譟比等條件的要求更低，對徬瓣抑製效果更好，能夠較為準確地估計齣信源的幅度信息，且不需要對信源數目進行預估計，體現齣明顯的優勢。
전통적파체방향（DOA）고계방법왕왕수도 Nyquist 채양정리여“서리한”적한제，대쾌박수、진원수급신조비등조건적요구교고，병차불능준학고계신호원적폭도신식。기우목표재공역적희소성，침대다유관측향량모형，제출일충정칙화적 FOCUSS 희소중구산법，가이유효제고저신조비조건하적고계성능。진렬접수구진쾌박수대，함조성신식다，대고분변적 DOA 고계영향교대，이통과대진렬적협방차구진구고계차멱적방법가이유효핍근신호자공간，감소조성자공간적영향。이진렬접수수거적협방차구진작위대분해적수거향량구조희소모형，능구사중구신호구유교고적분변솔，대쾌박수、진원수급신조비등조건적요구경저，대방판억제효과경호，능구교위준학지고계출신원적폭도신식，차불수요대신원수목진행예고계，체현출명현적우세。
Traditional direction of arrival estimation method is always limited with Nyquist sampling theorem and Ray‐leigh limit ,requires better condition such as snapshot number ,sensor number and SNR .It also can’t estimate amplifier in‐formation of source accurately yet .Based on spatial sparsity of targets ,aiming at multiple‐dimension vectors model ,regular‐ized FOCUSS sparse reconstruction algorithm can improve performance of DOA estimation in the condition of low SNR effec‐tively .Array received matrix has large snapshot number and lots of noise information and it may affect higher resolution DOA estimation deeply .High power of the covariance matrix can approach signal subspace effectively ,decrease the influence of noise subspace .Taking the covariance matrix of array received data as data vector to be resolved construct sparse model can improve resolution of reconstructed signal ,and requires less snapshot number ,less sensor number and lower SNR .This method can also restrain sidelobe better and estimate amplifier information of source accurately .Moreover ,it doesn’t need to pre‐estimate the number of sources ,reflects obvious advantage .
