Multi-sensor target tracking in the presence of clutter using game theory

Abstract

The problem of multi-sensor target tracking in the presence of clutter has not been studied from a game theory point of view. This paper presents a game theory approach for such a problem. Fundamental sub-problems are state estimation and data association in multi-sensor target tracking systems. In this paper, the state estimation problem is formulated as zero-sum game and a minimax filter is developed to estimate the target characteristics, including its position, velocity, and acceleration. To extend the filter in the presence of clutter, a data association framework is proposed. In data association framework, set of new hypotheses in each step is produced from set of validated measurements and the ith global hypothesis is obtained from the previous step. Next, posteriori estimate of ith global hypothesis is updated based on the filter for each new hypothesis. To manage exponential growth of new hypotheses, a new k-best algorithm is implemented. Simulation results illustrate the improved performance of the proposed filter compared to multiple hypothesis tracking and nearest-neighbor fuzzy filters.