Estimating relative position and yaw with laser scanning radar using probabilistic data association

Many vehicle following applications require that the relative position and sometimes yaw between vehicles be measured by the following vehicle. Typically, vision and radar systems are used to obtain the relative position, and, while relative yaw can be measured, the accuracy may sometimes be unacceptable. The focus of this paper is on robust, accurate estimation of target position and yaw relative to the sensor of interest, a laser scanning radar (LIDAR) sensor. A probabilistic data association algorithm, developed by Bar-Shalom (1978) for standard radar sensors, is adapted for use with the LIDAR sensor and for estimation of the relative yaw. Computational concerns for real-time implementation necessitate the use of various pre-filtering and filter restructuring techniques. Tests of the algorithm on actual LIDAR data recorded outdoors show the exceptional performance of the estimator.