Hybrid PDA/FIR Filtering for Preceding Vehicle Tracking Using Automotive Radars

Abstract

This paper proposes a novel single vehicle tracking algorithm with enhanced reliability for automotive radar systems. The proposed algorithm overcomes the weaknesses of the probabilistic data association filter (PDAF) in single-target tracking in clutter. The PDAF is successful in normal situations, but may fail to track a target owing to various factors, such as the initialization errors and the sudden changes in the target motion. The proposed algorithm can recover the PDAF from failures using an assisting finite impulse response (FIR) filter. The FIR filter operates only when the PDAF cannot track a target properly, and additionally offers state estimate and estimation error covariance to reset the PDAF. The proposed algorithm, the hybrid PDAF/FIR filter (HPFF), combines the PDAF and FIR filter, and hence shows enhanced reliability. Simulations of preceding vehicle tracking using an automotive radar demonstrate the effect and performance of the proposed HPFF.

Finite Impulse Response Filter-Based Track Formation for Preceding Vehicle Tracking Using Automotive Radars

“…Generally, an automotive radar for preceding vehicle tracking is installed in the middle front of the host vehicle, as shown in Figure 1 [20]. The automotive radar emits millimeter waves with a certain field of view (FOV), after which the radar waves scatter and are reflected from various objects on a road.…”

“…where vx and vy are the measurement noises in the Cartesian coordinate. The measurement vector was defined as z = [x m y m ] T , and the noise covariance of z was obtained as follows [12,20]:…”

“…These methods utilize the two-point differencing method, which computes the initial estimate of the velocity by differencing two successive measurements. In contrast, the algorithm proposed in this study is based on finite impulse response (FIR) filtering, which has recently attracted attention from researchers [16][17][18][19][20][21][22][23][24]. FIR filters are bach-processing filters, which can produce state estimate and error covariance using recent finite measurements, without filter initialization.…”