Enhancing People Tracking in Frequency-Modulated Continuous-Wave Radar Systems: Optimal Parameter Selection for Integrated Probabilistic Data Association Filter Tracker

Widely used in automotive safety, industrial automation, and surveillance, Frequency-Modulated Continuous-Wave (FMCW) radar provides accurate object tracking (OT). Kalman filter (KF)-based algorithms are proven to work reliably in industry for OT with FMCW radar. Extending the KF functionality, Probabilistic Data Association Filter (PDAF) is a method that also enables the association of detected reflections with the observed target. Integrated PDAF (IPDAF) offers formulations for both the probability of track existence and data association simultaneously. This paper shows how parameters for IPDAF tracker can be selected to optimize the First, FMCW radar is simulated to generate precise distance, velocity, and angle measurements through a multiple-input multiple-output (MIMO) antenna setup providing ground-truth labeled data. Next, a practical method for calculating optimal tracking parameters (OTP), particularly for short-range applications, based on Optuna optimizer is introduced. This method aims to improve tracking accuracy in scenarios such as indoor environments and pedestrian safety systems, considering scenarios with different target movement maneuvers and different dynamic movement models.