Integrated Detection and Tracking Based on Radar Range-Doppler Feature
This addresses radar system limitations in complex scenes for applications like autonomous vehicles or surveillance, representing an incremental improvement over existing joint detection-tracking approaches.
The paper tackles the challenge of fully utilizing radar signal potential in joint detection and tracking systems by introducing the Integrated Detection and Tracking (InDT) method, which integrates feature extraction from Range-Doppler matrices with adaptive Kalman filter updates and cosine distance-based data association, achieving validation on simulated and public datasets.
Detection and tracking are the basic tasks of radar systems. Current joint detection tracking methods, which focus on dynamically adjusting detection thresholds from tracking results, still present challenges in fully utilizing the potential of radar signals. These are mainly reflected in the limited capacity of the constant false-alarm rate model to accurately represent information, the insufficient depiction of complex scenes, and the limited information acquired by the tracker. We introduce the Integrated Detection and Tracking based on radar feature (InDT) method, which comprises a network architecture for radar signal detection and a tracker that leverages detection assistance. The InDT detector extracts feature information from each Range-Doppler (RD) matrix and then returns the target position through the feature enhancement module and the detection head. The InDT tracker adaptively updates the measurement noise covariance of the Kalman filter based on detection confidence. The similarity of target RD features is measured by cosine distance, which enhances the data association process by combining location and feature information. Finally, the efficacy of the proposed method was validated through testing on both simulated data and publicly available datasets.