Patent ID: 8224627

Claim:
A technique for determination of the signal subspace dimension K in direction of arrival (DOA) estimation and in exponentially damped sinusoids (EDS) modeling, two tasks where said signal subspace dimension K represents respectively the number of detected objects and the model order, and where the samples obtained by analog-to-digital conversion (ADC) of the sensors signals are put into a data matrix that is numerically processed through a computer needed to achieve the desired results of the DOA estimation or the EDS modeling in real-time, said technique comprising: (a) storing the samples obtained by said ADC of the sensors signals respectively into a complex-valued L×M data matrix X with L≧2 and M>>L in the DOA estimation, or into a complex or real-valued data vector x=[x 0 , x 1 , . . . , x n-1 ] with n≧100 and then storing x into a Hankel L×M data matrix X with L=M−1 or L=M and L+M−1=n in the EDS modeling; (b) generating random numbers nu having real or complex normal distribution with zero mean and standard deviation σ=1 and storing them into a noise matrix N u or a noise vector nu=[nu 0 , nu 1 , . . . , nu n-1 ] that respectively fit in sizes and kind (complex-valued or real) said data matrix X in the DOA estimation and said data vector x in the EDS modeling (single N u or nu is necessary when series of similar data matrices X are under processing in the DOA estimation or in the EDS modeling); (c) computing the squared singular values σ X,1 2 ≧σ X,2 2 ≧ . . . ≧σ X,L 2 ≧0 of said data matrix X and estimating the standard deviation σ W of its noise by computing square root of (σ X,Km+1 2 + . . . +σ X,Km+D 2 )/(DM), where K m and D are tuning parameters; (d) determining a standard deviation σ N =k N s W with a lower limit σ 0 , where k N and σ 0 are tuning parameters, and forming a matrix Y=X+N respectively by scaling said noise matrix N u into an auxiliary matrix N=σ N N u and adding to it said data matrix X in the DOA estimation, or only by scaling said noise vector nu to σ N nu and adding to it said data vector x into a vector y=x+σ N nu and then storing y into a Hankel L×M matrix Y that fits in sizes said data matrix X in the EDS modeling; (e) computing the squared singular values σ Y,1 2 ≧σ Y,2 2 ≧ . . . ≧σ Y,L 2 ≧0 of said matrix Y and the ratios r k =σ X,k 2 /σ Y,k 2 with k=1, . . . , K m +D between said squared singular values σ X,k 2 and σ Y,k 2 of Y, where said data matrix X is added with noise having real or complex normal distribution with zero mean and standard deviation σ N .