Distributed algorithm of subgradient optimization for localization based on received signal strength in wireless network

In this paper, we propose a subgradient optimization method for pedestrian localization based on received signal strength in wireless network. The objective function of weighted least-squares estimation is adopted, which shows good convexity and has immunity to shadowing effect. We also approximate the subgradient of the objective function by a recursive form so that it can be implemented in a decentralized manner within each sensing node. A variable step size is proposed to take into consideration both the subgradient and minimum adjustment to accelerate convergence. Furthermore, the convergence analysis is also given to show the feasibility of our design for the step size. From simulation results, we can see the proposed algorithm has better accuracy and convergence rate than the conventional decentralized algorithms to localize a stationary or moving target in wireless network.