A Sequential Implementation of Optimal Array Processors

A sequential implementation of the likelihood ratio for doubly composite hypothesis array processing problems is presented in which the likelihood ratio is realized by an appropriate combination ion of single frequency components. The array processos decomposed in such a way that the actual a priori parameter knowledge dge need not be introduced until after a significant portion of the data processing has taken place.     

Array Processor Performance Under Directional Uncertainty

Detection performance of four candidate receiver structures for the signal known except for direction (SKED) array problem is investigated. Included are the Bayes optimal detector, two estimate and plug structures, and a fixed estimate structure. Estimators considered are the maximum likelihood (ML) and maximum a posteriori (MAP). Performance degradation from optimal for the estimate and plug structures considered is shown to be significantly more severe the larger the array size.     

Optimal Array Processor Performance Trade-offs under Directional Uncertainty

Detection performance in terms of the receiver operating characteristic (ROC) curve is obtained for three specific problems involving either signal or noise source location uncertainty. The array processors are optimum in the sense that they form the likelihood ratio of the outputs of the individual array elements. Performance trade-offs due to signal uncertainty, signal and noise source location uncertainty, and array size are presented.