An adaptive mini-norm algorithm
Can Hua · Applied Acoustics · 2005
The key problem of eigen subspace methods is the estimation of signal or noise subspace. In practical situations, there exist signals whose statistic characteristics always change over time. To obtain the real time estimates of the signal parameters which are time varying, it is necessary to update the signal or noise subspace according to newly received array sampled output. In this paper, an algorithm MALASE (Maximum Likelihood Adaptive Subspace Estimation) is first analysed to address the problem of adaptive estimating the subspace of the data covariance matrix. Then, with the combination of the above subspace tracking algorithm with the Mini-norm high resolution algorithm, and using the Zero-tracking technology, an adaptive Mini-norm algorithm is proposed to track the time-varying DOAs (directions of arrival). Computer simulation results are provided to demonstrate the effectiveness of the proposed algorithm.