CAJ | 학술논문

针对经典高分辨波达方位(DOA)估计方法在低信噪比下分辨性能较差的问题，该文提出一种适用于主动探测系统的基于互相关矩阵的改进多重信号分类(MUSIC)高分辨方位估计方法(I-MUSIC)。该方法首先利用主动声呐发射信号已知的特性，将发射信号与阵元接收信号进行互相关，利用互相关序列形成新的空域协方差矩阵，再进行特征分解。理论分析表明，互相关处理在抑制噪声的同时保留了阵元之间的相位信息，可以得到比MUSIC方法更准确的子空间划分，进而提高低信噪比方位估计性能。在此基础上，提出一种基于相关时间门限的改进MUSIC高分辨方位估计(T-MUSIC)方法，通过对互相关序列设置时间门限进一步提高方位估计信噪比。仿真结果表明，与MUSIC方法相比，I-MUSIC与T-MUSIC可以分别使低信噪比时的估计性能提高3 dB和6 dB，相应平均估计误差分别为原方法的77%和53%。在阵元间接收噪声存在相关性时，T-MUSIC与I-MUSIC方法相比可获得8 dB的估计增益，估计性能更优。I-MUSIC 与 T-MUSIC 应用于多目标主动探测，可大幅提高探测系统在低信噪比下的方位估计性能。
침대경전고분변파체방위(DOA)고계방법재저신조비하분변성능교차적문제，해문제출일충괄용우주동탐측계통적기우호상관구진적개진다중신호분류(MUSIC)고분변방위고계방법(I-MUSIC)。해방법수선이용주동성눌발사신호이지적특성，장발사신호여진원접수신호진행호상관，이용호상관서렬형성신적공역협방차구진，재진행특정분해。이론분석표명，호상관처리재억제조성적동시보류료진원지간적상위신식，가이득도비MUSIC방법경준학적자공간화분，진이제고저신조비방위고계성능。재차기출상，제출일충기우상관시간문한적개진MUSIC고분변방위고계(T-MUSIC)방법，통과대호상관서렬설치시간문한진일보제고방위고계신조비。방진결과표명，여MUSIC방법상비，I-MUSIC여T-MUSIC가이분별사저신조비시적고계성능제고3 dB화6 dB，상응평균고계오차분별위원방법적77%화53%。재진원간접수조성존재상관성시，T-MUSIC여I-MUSIC방법상비가획득8 dB적고계증익，고계성능경우。I-MUSIC 여 T-MUSIC 응용우다목표주동탐측，가대폭제고탐측계통재저신조비하적방위고계성능。
In view of the poor performance of traditional Direction of Arrival (DOA) methods at low signal-to-noise ratios, an improved MUltiple SIgnal Classification (MUSIC) algorithm for DOA estimation applied to active detection system based on covariance matrix decomposition of cross-correlation (I-MUSIC) is proposed. Exploiting the transmission feature of active sonar, cross-correlation sequence between the transmitted signal and the array output is formulated. The spatial covariance matrix is then constructed from the sequence. Then matrix decomposition is implemented over the new spatial covariance matrix to estimate the DOA. It is proved that cross-correlation can suppress noise while preserving the phase information between array elements, which facilitate the subspace separation at low SNRs. Furthermore, another novel method based on correlation Time threshold (T-MUSIC) is proposed to further improve the DOA performance. Simulation results indicate that I-MUSIC and T-MUSIC can obtain a performance gain of 3 dB and 6 dB, with the estimate error being 77% and 53% of the original method respectively. Due to data selection via time threshold, T-MUSIC is not appreciably affected by noise, and thus outperforms IM-MUISC for 8 dB at low SNRs. I-MUSIC and T-MUSIC can improve the DOA performance at low SNRs significantly if applied to active multi-target detection system.