Cooperative Vehicle Tracking in VANET Using a Distributed Improved Cubature Kalman Filter

Abstract

This letter addresses the issue of cooperative vehicle tracking in vehicular ad-hoc networks (VANETs) through the fusion of global navigation satellite system (GNSS) data and time-of-arrival (TOA) based ranging information. We propose a novel distributed improved Cubature Kalman Filter (CKF) to enhance the state estimation accuracy of all vehicles. This approach comprises two parts: local improved CKF processing and cooperative fusion tracking. Due to the nonlinearity of the ranging measurement function with respect to both local vehicle state and neighboring vehicle state, an augmented parameter vector is constructed in the improved CKF method to tackle this challenge. Then, we present the optimal cooperative fusion of the local vehicle state estimate and the estimates from its neighbors, in the sense of minimizing the fused mean squared error. Numerical examples demonstrate that the root of average mean squared error (RAMSE) of the proposed method can be significantly reduced.