Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
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The purpose of the work in this paper is to gain insight into several fundamental questions that arise whenever a distributed multiple-vehicle control system is endowed with communication capabilities. These fundamental questions include: what data should each vehicle share?, how frequently should communication take place?, and what benefit does communication provide? These questions are evaluated with respect to a target tracking task in which multiple pursuit vehicles estimate the state of a target vehicle. This task has three main components: communication, estimation, and control. Communication takes place on a broadcast network, estimation is achieved with an Unscented Kalman Filters, and the controller is behavior-based. Simulation results show that communication always improves distributed estimate results. Which information to transmit depends on available bandwidth, and more frequent communication generally yields better estimates. Simulation results also show how coordinated control can be beneficial to target tracking in a cluttered environment.