Multi-Target State Estimation Using Interactive Kalman Filter for Multi-Vehicle Tracking

Abstract

In this paper, an interactive Kalman filter (IKF) is proposed to demonstrate the interaction between targets as to how the behavior of a desired target is affected by the behavior of its neighbors. The IKF utilizes two types of interactions available in multi-agent systems, namely, cooperative and competitive. The IKF is similar to the distributed Kalman filter (DKF) in terms of architecture, method of representation of equations, and use of neighborhood weight matrix while IKF appears to be a general form of DKF. In this method, a network of IKF nodes is constructed such that each node is associated with every target. There are edges between nodes for which the corresponding targets have effect on each other. Time-varying weights are used to control the interaction information exchanged among IKF nodes. The method of calculating interaction weights in the weight matrix plays a key role on the estimation results. The calculation of optimal IKF gain and evaluations on MOTP, MOTA, and MSE metrics illustrate the effectiveness of the proposed filter in vehicle tracking.