The Study Of Singular Value Decomposition-based Signal Estimation Algorithm Of Spatial Array Covariance Hankel Matrix

Signal estimation algorithm has been an important prerequisite of all kinds of subspace estimation in array signal processing. For most of the estimation algorithm in array signal processing, if the estimated number of the signal is not consistent with the actual number, the validity of the algorithm will fell or even fail in estimation.As a result, over the years, signal estimation algorithm in array signal processing has been one of the hot topics in the study of many scholars.Based on the above, this paper puts forward a SVD-based signal estimation algorithm of spatial array covariance Hankel matrix. We also optimize the new algorithm and get its optimization algorithm which named SVD-based signal estimation algorithm of combine Hankel matrix.First of all, this paper introduces the traditional signal estimation algorithm: the algorithm based on MDL criterion, the algorithm based on the AIC criterion and spatial smoothing algorithm.Can be found that the algorithm based on MDL criterion and AIC criterion have the validity only in the case of independent signal, and in the case of the coherent/related signal, these two kinds of methods will fail in estimation.On the other hand, spatial smoothing algorithm is suitable in the case of the coherent/related signal, it sacrifices to the array aperture to reach the purpose, and it reduces the estimation efficiency of the array. In view of the above algorithms, we introduced the Hankel matrice for studying the signal estimation algorithm.It has been found that Hankel matrice can be used in uniform linear array.But there are still differences in how to struct Hankel matrice in uniform linear array.We studied the factorization form, rank properties and rank invariance of Hankel matrix,and struct a kind of array covariance Hankel matrice which is suitable for uniform linear array.The SVD of the array covariance Hankel matrix will obtain both non-zero singular values and zero, and by the number of non-zero singular values we can determine the number of the signal.The factorization form of Hankel matrix shows the array covariance Hankel matrice that has removed the noise can be used to estimate both in the case of independent and coherent signal.On this basis, to further strengthen the estimation ability of the sensor array,we found an important prerequisite that the linear equations of array covarianceHankel matrice in different baseline have the same constant solution through the study of the linear equations of Hankel matrice.Based on this conclusion, we will struct a combined Hankel matrice by combining array covariance Hankel matrices in different reference signals through some certain rules.the combined Hankel matrice raise the estimation ability to int[2 ]3M in the case of partially coherent and independent signal and reduce to int[( 1) ]2M +only in the case of completely coherent signal.Here M means the number of the sensor array.Finally, in this paper, we carried on the simulation programming and analyzing of new algorithm. Simulation experiments show that the estimation has good gradual consistency with the growth of the signal-to-noise ratio, and compared with the traditional algorithm based on AIC and MDL criterion, the method has a better performance in the condition of low signal-to-noise ratio.