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.
Highlights
Intelligent cars equipped with advanced driver assistance systems (ADAS) have become common in recent years, and automotive radars have become increasingly important as an essential sensor for ADAS
The hybrid PDAF/FIR filter (HPFF) is a combination of a main filter, the probabilistic data association filter (PDAF), and an assisting filer, the finite impulse response (FIR) filter
The PDAF is successful in normal situations, it can lose a target under severe conditions
Summary
Intelligent cars equipped with advanced driver assistance systems (ADAS) have become common in recent years, and automotive radars have become increasingly important as an essential sensor for ADAS. Because there is no available measurement, the PDAF cannot perform the measurement update process, which leads to tracking failure In such cases, the HPFF operates the assisting FIR filter and obtains the state estimate and estimation error covariance. The HPFF operates the assisting FIR filter and obtains the state estimate and estimation error covariance With this information, the main filter, PDAF, is reset and rebooted. HYBRID PDA/FIR FILTER The PDAF cannot perform the measurement update when there is no measurement in the validation region. The PDAF does not perform the measurement update, and the estimated position (state) becomes increasingly distant from the actual state This phenomenon leads to the tracking failure (target loss) of the PDAF. The HPFF algorithm performs FIR filtering when there is no measurement in the validation region.
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