| Title |
Automotive Target Tracking Using Prior Knowledge about Spatial Distribution of High-Resolution Radar Detections |
| Authors |
조형찬(Hyung-Chan Cho) ; 이찬석(Chan-Seok Lee) ; 정보영(Boyoung Jung) ; 한슬기(Seul-Ki Han) ; 나원상(Won-Sang Ra) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.2.270 |
| Keywords |
Automotive radar tracking; non-Gaussian measurement noise; Gaussian sum filter; mode management |
| Abstract |
This paper deals with the problem of target tracking using high-resolution automotive radars. Due to the enhanced angular resolution, a high-resolution radar allows a single target to produce multiple detections originated from the main scatters with complicated spatial distribution Thus, the target tracking problem is formulated as a nonlinear state estimation associated with the multi-modal measurement noise distribution. As a practical resolution to this problem, the statistical properties of the multi-detection is modeled by approximating the reflected target signal strength as Gaussian mixture, then a Gaussian sum filter is designed using the prior knowledge about the model. To prevent the divergence of the number constituents of the posterior distribution, the weights of the pairing hypotheses between the multiple detections and the modes of their distribution are evaluated and the mode management technique is applied in implementing the tracking filter. Accordingly, the proposed filter is able to improve computational efficiency while maintaining the estimation performance. Through simulations for typical automotive target tracking scenarios, it is demonstrated that our approach provides reliable target tracking performance compared to the existing filters. |