Distributed adaptive direct position determination based on diffusion framework

The conventional direct position determination (DPD) algorithm processes all received signals on a single sensor. When sensors have limited computational capabilities or energy storage, it is desirable to distribute the computation among other sensors. A distributed adaptive DPD (DADPD) algorithm based on diffusion framework is proposed for emitter localization. Unlike the corresponding centralized adaptive DPD (CADPD) algorithm, all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position, respectively. The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner. Exactly the same iterative localization algorithm is carried out in each computing sensor, respectively, and the algorithm in each computing sensor exhibits quite similar convergence behavior. The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.