Track Filters for Target Tracking Using mm-wave Radars

Millimeter-wave radar technology is recognized for its ability to provide high-resolution measurements and superior performance under adverse weather conditions, surpassing traditional radar systems. To fully exploit the potential of this technology, it is imperative to implement robust filtering methods for effective target tracking. This study aims to systematically evaluate the efficacy of several filters, including the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and Probability Hypothesis Density (PHD) Filter. The primary focus of this research lies in the application of millimeter wave radar measurements for target tracking within dynamic environments. Target tracking holds paramount significance in various applications such as autonomous vehicles, robotics, and surveillance systems. To heighten the accuracy and reliability of tracking, diverse filtering techniques are explored and compared. Specifically, the study delves into the performance of different variants of the Kalman filter, the Nearest Neighbor filter, and the Probability Hypothesis Density (PHD) filter. The goal is to enhance our understanding of the strengths and limitations of each filtering method, facilitating informed decisions for optimal implementation in real-world scenarios.