Collaborative Position Location for Wireless Networks Using Iterative Parallel Projection Method

In this work, we propose a set-theoretic approach to collaborative position location for wireless networks. The proposed method borrows the concept from the parallel projection method (PPM), originally developed for signal recovery with inconsistent convex feasibility sets, revises and extends the technique to an iterative and distributed numerical framework to estimate node locations, based on incomplete and noisy inter-node distance estimates. We demonstrate that the proposed iterative PPM is computationally much more efficient than existing methods, while achieving comparable and often better localization accuracy and robustness to non-line-of-sight (NLOS) bias. Our proposed method can be implemented in a parallel and distributed fashion, and is scalable for large network deployment.