Method for determining number of coherent signals in the presence of colored noise

The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colored noise. An approach combining the combined information theoretic criteria and eigen- value correction, is presented to determine number of signals. The method uses maximum likelihood （ML） and information theoretic criteria to estimate coherent signals alternately, then eliminate the inequality of the eigenvalues caused by colored noise by correcting the noise eigenvalues. The computer simulation results prove the effective performance of the method.

Detection of Number of Wideband Signals Based on Support Vector Machine

In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm for detecting number of wideband signals is provided.First,technique of focusing is used for transforming signals into a same focusing subspace.Then the support vector machine(SVM)can be deduced by the information of eigenvalues and corresponding eigenvectors.At last,the signal number can be determined with the obtained decision function.Several simulations have been carried on verifying the proposed algorithm.

An investigation of several typical model selection criteria for detecting the number of signals

Based on the problem of detecting the number of signals,this paper provides a systematic empirical investigation on model selection performances of several classical criteria and recently developed methods(including Akaike’s information criterion(AIC),Schwarz’s Bayesian information criterion,Bozdogan’s consistent AIC,Hannan-Quinn information criterion,Minka’s(MK)principal component analysis(PCA)criterion,Kritchman&Nadler’s hypothesis tests(KN),Perry&Wolfe’s minimax rank estimation thresholding algorithm(MM),and Bayesian Ying-Yang(BYY)harmony learning),by varying signal-to-noise ratio(SNR)and training sample size N.A family of model selection indifference curves is defined by the contour lines of model selection accuracies,such that we can examine the joint effect of N and SNR rather than merely the effect of either of SNR and N with the other fixed as usually done in the literature.The indifference curves visually reveal that all methods demonstrate relative advantages obviously within a region of moderate N and SNR.Moreover,the importance of studying this region is also confirmed by an alternative reference criterion by maximizing the testing likelihood.It has been shown via extensive simulations that AIC and BYY harmony learning,as well as MK,KN,and MM,are relatively more robust than the others against decreasing N and SNR,and BYY is superior for a small sample size.