A new method for detecting the number of signals incident upon an array of sensors is described. This new method is based on finding upper thresholds for the observed eigenvalues of the covariance matrix of the array output. Theoretical analysis shows that the performance of the new method is flexible and can be controlled by a parameter ta. By using a suitable value of t, the performance of the new method can be made superior to MDL, in that the threshold occurs at a lower value of SNR. Also, it can be made superior to the AIC in that a lower error rate can be achieved at high SNR. Simulation results are included to confirm the analysis. ABSTRACTA new method for detecting the number of signals incident upon an array of sensors is described. This new method is based on finding upper thresholds for the observed eigenvalues of the covariance matrix of the array output. Theoretical analysis shows that the performance of the new method is flexible and can be controlled by a parameter t . By using a suitable value of t , the performance of the new method can be made superior to MDL, in that the threshold (X occurs at a lower value of SNR. Also, it can be made superior to the AIC in that a lower error rate can be achieved at high SNR.Simulation results are included to confirm the analysis.

The recently developed generalized linear least squares (GLLS) algorithm has been found v e v useful in non-uniformly sampled biomedical signal processing and parameter estimation. In this paper, the algorithm is used for the identification of a compartmental model with a pair of repeated eigenvalues bused on the nonuniformly sampled noisy data. A ca,re study is presented, which demonstrates that the algorithm is able to select the most suitable model for the system from the non-uniformly sampled noisy signals.