DECENTRALIZED ARRAY-PROCESSING USING THE MODE ALGORITHM

Centralized methods for source location using sensor arrays have compu tational and communication burdens that increase significantly with th e number of sensors in the array. Therefore, these methods may not be usable in the applications involving very large arrays. In such applic ations, the data processing may need to be decentralized. This paper i ntroduces two methods for decentralized array processing, based on the recently proposed MODE algorithm. For prescribed nonoverlapping subar rays, both methods are shown to be statistically optimal in the sense that asymptotically they provide the most accurate decentralized estim ates of source location parameters. The problem of subarray selection to further optimize the estimation accuracy is only briefly addressed. The two methods are intended for different types of applications: the first should be preferred when there exist significant possibilities for local processing or for parallel computation in the central proces sor; otherwise the second method should be preferred. The accuracy of the two decentralized methods is compared to the centralized Cramer-Ra o bound, both analytically and numerically, in order to provide indica tions about the loss of accuracy associated with decentralized process ing.