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《Technical Acoustics》 2004-04
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Algorithm for DOA tracking based on adaptive subspace estimation

GAN Hua-dong, LI Zhi-shun, LI Le, SU Wei(College of Marine Engineering, Northwestern Polytechnical University, Xi’an 710072, China)  
The key problem of eigen-subspace high-resolution methods is estimation of signal or noise subspace. In practice, statistic characteristics of signals always change with time. To obtain real time estimates of the time varying signal parameters, it is necessary to constantly update the signal or noise subspace to adapt the newly sampled array output. In this paper, an algorithm termed MALASE(maximum likelihood adaptive subspace estimation)is presented to address the problem of adaptive estimation of the subspace of the data covariance matrix. It is based on optimization of a likelihood criterion. The parameters of the likelihood to be estimated are the expected eigenvectors and eigenvalues, obtained with a stochastic algorithm that requires little computation cost. Furthermore, the particular structure of the algorithm ensures the orthonormality of the estimated eigenvectors. Thus, with combination of the above-mentioned subspace tracking algorithm and a mini-norm high-resolution algorithm, and a zero-tracking technique, an adaptive mini-norm algorithm is proposed to track the time-varying DOAs (directions of arrival) of multiple targets. Computer simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
【CateGory Index】: TN911
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