文章摘要
何光进,程锦房,李楠,张炜.单矢量水听器方位频率联合估计新方法[J].声学技术,2013,32(3):238~242
单矢量水听器方位频率联合估计新方法
A new method of DOA and frequency estimation based on a single vector hydrophone
投稿时间:2012-02-08  修订日期:2012-05-23
DOI:10.3969/j.issn1000-3630.2013.02.013
中文关键词: 矢量水听器  方位频率估计  四阶累积量  三线性分解
英文关键词: vector hydrophone  DOA and frequency estimation  fourth-order cumulant  PARAFAC
基金项目:国家部委基金资助项目(4010709010201)
作者单位E-mail
何光进 海军工程大学兵器工程系, 武汉 430033
海军驻昆明地区军事代表办事处, 昆明 650031 
gjhe2008@163.com 
程锦房 海军工程大学兵器工程系, 武汉 430033  
李楠 海军工程大学兵器工程系, 武汉 430033  
张炜 海军工程大学兵器工程系, 武汉 430033  
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中文摘要:
      旋转不变子空间法和多重信号分类法需假设背景噪声为独立的高斯白噪声或自相关矩阵已知, 当条件不满足时算法的性能急剧下降。针对这一问题, 根据矢量水听器多通道输出的特点, 提出了一种基于平行因子模型的单矢量水听器方位频率联合估计方法。首先利用矢量水听器各个通道t时刻和t+1时刻的输出数据, 计算声压和各振速不同组合时的四阶累积量, 并构建三阶平行因子模型;然后分析了PARAFAC模型低秩分解的唯一性条件并利用三线性交替最小二乘算法得到了单矢量水听器阵列流形和相位延迟估计, 进而得到目标的方位和频率估计。与旋转不变子空间法和多重信号分类法相比, 该方法不需要子空间估计和谱峰搜索, 在高斯噪声和拉普拉斯噪声背景下对多目标的分辨能力好于ESPRIT算法。仿真和实测数据的分析结果证明了算法的有效性。
英文摘要:
      ESPRIT (Estimation of Signal Parameter via Rotational Invariance Techniques) and MUSIC (Multiple SIgnal Classification) DOA finding algorithms are based on the hypothesis that the additive noise is independent, identically distributed white Gaussian noise or the noise’s auto correlation matrix is known. When the condition is not satisfied, the performance of the algorithms would drop dramatically. Based on this, according to the multi-output characteristics of a single vector hydrophone, a multi-source DOA (Direction-Of-Arrival) and frequency estimation algorithm based on PARAFAC (PARAllel FACtor) model is proposed. By using the hydrophone’s output data and time-delays between adjacent snapshots, the fourth-order cumulants of sound pressure and velocity data are calculated. And a third-order PARAFAC model is constructed. The uniqueness of high-dimension matrix’s low rank decomposition is analyzed and TALS (Tri-linear Alternating Least Square) method is used to estimate the manifold of the vector hydrophone and phase delays between adjacent outputs. Finally, the sources’ DOA’s and frequencies are obtained. Compared with ESPRIT and MUSIC algorithms, the proposed one doesn’t need subspace estimation or peak searching, and has a better resolution under Gaussian and Laplace noises. The simulation and experiment results verify the efficiency of the algorithm.
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