CAJ | 학술논문

针对阵列通道不一致性引起的幅相误差校正问题，基于多级维纳滤波器(MSWF)，该文提出幅相误差快速校正的简化的多级维纳滤波器(SMSWF)算法。SMSWF算法利用校正源的方位和波形信息对阵列幅相参数进行估计，无需估计协方差矩阵和进行特征值分解，大大地减小了计算量，且具有与特征分解方法相同的幅相参数估计性能。研究发现，单个信源入射到阵列且信源波形已知时，SMSWF算法获得的信号子空间等价于特征分解法得到的信号子空间，这表明SMSWF算法能够替代特征分解法，从而极大减小基于特征分解法的信号处理方法的计算量。大量计算机仿真和消声水池试验验证了SMSWF算法的优越性能。
침대진렬통도불일치성인기적폭상오차교정문제，기우다급유납려파기(MSWF)，해문제출폭상오차쾌속교정적간화적다급유납려파기(SMSWF)산법。SMSWF산법이용교정원적방위화파형신식대진렬폭상삼수진행고계，무수고계협방차구진화진행특정치분해，대대지감소료계산량，차구유여특정분해방법상동적폭상삼수고계성능。연구발현，단개신원입사도진렬차신원파형이지시，SMSWF산법획득적신호자공간등개우특정분해법득도적신호자공간，저표명SMSWF산법능구체대특정분해법，종이겁대감소기우특정분해법적신호처리방법적계산량。대량계산궤방진화소성수지시험험증료SMSWF산법적우월성능。
Aiming the error calibration for the array channel uncertainty, a new fast algorithm named Simplified Multi-Stage Wiener Filter (SMSWF) based on the Multi-Stage Wiener Filter (MSWF) is proposed. The SMSWF takes the advantages of the DOA and the waveform of the cooperative source to estimate the gain and the phase factors, and it does not need to estimate the covariance matrix and the eigendecomposition operations. Compared with the eigendecomposition algorithm, the SMSWF has the same performance for estimating gain and phase factors while greatly reduce the complexity. The researches show that if a single source with a known waveform incidence on the array, the signal subspaces obtained by the SMSWF and one obtained by the eigendecomposition are equipollent, which demonstrate that the SMSWF is able to replace the eigendecomposition. The complexity of signal processing methods based on the eigendecomposition can greatly be reduced by replacing the eigendecomposition with the SMSWF. The extensive computer simulations and experiment in anechoice water tank show the superiori performance of the proposed algorithm.