TDOA-based adaptive sensing in multi-agent cooperative target tracking

This paper investigates the adaptive sensing for cooperative target tracking in three-dimensional environments by multiple autonomous vehicles based on measurements from time-difference-of-arrival (TDOA) sensors. An iterated filtering algorithm combined with the Gauss-Newton method is applied to estimate the target location. By minimizing the determinant of the estimation error covariance matrix, an adaptive sensing strategy is developed. A gradient-based control law for each agent is proposed and a set of stationary points for local optimum geometric configurations of the agents is given. The proposed sensing strategy is further compared with other sensing strategies using different optimization criteria such as the Cramer-Rao lower bound. Potential modifications of the proposed sensing strategy is also discussed such as to include the formation control of agents. Finally, the proposed sensing strategy is demonstrated and compared with other sensing strategies by simulation, which shows that our method can provide good performance with even only two agents, i.e., one measurement at each time.