LAUSR

The direct position determination (DPD) algorithms have been shown to outperform conventional two-step methods in localization accuracy under poor observation conditions (e.g., low signal-to-noise ratios or short signal observation durations). In this paper, we consider the distributed DPD of a static source in large-scale sensor networks. Different from the centralized DPD (CDPD) algorithms where all the raw signal samples are transmitted globally to a fusion center to solve for the source position, in our method the signal samples are only exchanged locally between neighboring sensors, and the source position is estimated using the proposed DPD algorithm distributively. The problem-specific cost function, which is different from the cost function of CDPD, is first derived. To obtain the cost function at each sensor, we propose to transmit the low-dimensional sufficient statistics across the sensor networks instead of the original high-dimensional raw signal samples, and to reconstruct the cost function via simple interpolations. Simplification of cost function evaluation is also developed to further reduce complexity. Finally, source position is estimated at each sensor efficiently via the importance sampling algorithm. Simulation results demonstrate the superior performance of the proposed method over existing adaptive distributed DPD as well as the two-step algorithms under poor observation conditions, and its computational efficiency is significantly improved compared with the CDPD.