A distributed information fusion method for localization based on Pareto optimization

—To overcome the limitations of specific positioning techniques for mobile wireless nodes and achieve a high accuracy, the fusion of heterogeneous sensor information is an appealing strategy. In this paper, the problem of optimal fusion of ranging information typically provided by Ultra-Wideband radio with speed and absolute orientation information is addressed. A new distributed recursive estimation method is proposed. The method does not assume any motion model of mobile nodes and is based on a Pareto optimization. The challenging part of the new estimator is the characterization of the statistical information needed to model the optimization problem. The proposed estimator is validated by Monte Carlo simulations, and the performance is compared to several Kalman-based filters commonly employed for localization and sensor fusion. Much better performance is achieved, but at the price of an increased computational complexity.

• Type: Journal
• Year: 2011
• Where: DCOSS
• Authors: Alessio De Angelis, Carlo Fischione
• Keywords: DCOSS 2011, Distributed And Parallel Computing, Monte Carlo Simulations, Optimal Fusion, Pareto Optimization