Modified value iteration algorithm and dynamic element matching based MDP for distributed data fusion and sensor management

In this paper, considering the problem of collaborative sensor management and data fusion for multitarget tracking, authors propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov Decision Processes (MDPs). Dynamic Element Matching (DEM) algorithms, widely used for reducing harmonic distortion in Digital-to-Analog converters, are used as a core element in the modified algorithm. In this paper, the authors also introduce and demonstrate a number of new performance metrics, to verify the effectiveness of an MDP policy, especially useful for quantifying the impact of the modified DEM-based Value Iteration algorithm on an MDP policy. The new algorithm is applied to control a group of UAVs carrying out surveillance over a region that includes a number of moving targets with the objective to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Simulation results show a clear improvement in performance compared to the classical algorithm. The proposed method demonstrated robust performance while guaranteeing polynomial computational complexity.