An Improved Signal Number Estimation Method Based on Information Theoretic Criteria in Array Processing
Jiang Bin, LU An-nan, Jie Xu · 2019
In order to extract the signal subspace and noise subspace accurately, the subspace direction finding algorithm needs to know the number of signal sources in advance. The algorithm based on information theoretic criteria can effectively estimate the number of signals. In this paper, an improved algorithm is proposed to estimate the number of signals in space based on information theoretic criteria. The influence of noise eigenvalue divergence on the likelihood function term is compensated by modifying the penalty function term in the information theory criterion. Simulation results show that the estimation performance of the proposed method is better than the minimum description length method (MDL) under low SNR. At the same time, compared with the Akaike information theoretic criterion (AIC), it has higher probability of correct estimation under high SNR.