| Title | Jointly Optimized Target Detection and Tracking Using Compressive Samples |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Feng, Qi, Huang, Jianjun, Yang, Zhaocheng |
| Journal | IEEE Access |
| Volume | 7 |
| Pagination | 73675–73684 |
| ISSN | 2169-3536 |
| Keywords | Bayes methods, composability, compressed sensing, compressive samples, compressive sampling, CSP-JDT, Cyber-physical systems, Detectors, Estimation, high-resolution radar signals, joint CSP Bayesian approach, joint decision and estimation, joint detection and tracking, joint performance metric, joint target detection, object detection, privacy, pubcrawl, radar detection, radar resolution, radar target detection, Radar tracking, Resiliency, signal reconstruction, signal sampling, sparse signals, target tracking, tracking algorithm |
| Abstract | In this paper, we consider the problem of joint target detection and tracking in compressive sampling and processing (CSP-JDT). CSP can process the compressive samples of sparse signals directly without signal reconstruction, which is suitable for handling high-resolution radar signals. However, in CSP, the radar target detection and tracking problems are usually solved separately or by a two-stage strategy, which cannot obtain a globally optimal solution. To jointly optimize the target detection and tracking performance and inspired by the optimal Bayes joint decision and estimation (JDE) framework, a jointly optimized target detection and tracking algorithm in CSP is proposed. Since detection and tracking are highly correlated, we first develop a measurement matrix construction method to acquire the compressive samples, and then a joint CSP Bayesian approach is developed for target detection and tracking. The experimental results demonstrate that the proposed method outperforms the two-stage algorithms in terms of the joint performance metric. |
| DOI | 10.1109/ACCESS.2019.2920264 |
| Citation Key | feng_jointly_2019 |
Jointly Optimized Target Detection and Tracking Using Compressive Samples