Biblio

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.

The passive radar also known as Green Radar exploits the available commercial communication signals and is useful for target tracking and detection in general. Recent communications standards frequently employ Orthogonal Frequency Division Multiplexing (OFDM) waveforms and wideband for broadcasting. This paper focuses on the recent developments of the target detection algorithms in the OFDM passive radar framework where its channel estimates have been derived using the matched filter concept using the knowledge of the transmitted signals. The MUSIC algorithm, which has been modified to solve this two dimensional delay-Doppler detection problem, is first reviewed. As the target detection problem can be represented as sparse signals, this paper employs compressive sensing to compare with the detection capability of the 2-D MUSIC algorithm. It is found that the previously proposed single time sample compressive sensing cannot significantly reduce the leakage from the direct signal component. Furthermore, this paper proposes the compressive sensing method utilizing multiple time samples, namely l1-SVD, for the detection of multiple targets. In comparison between the MUSIC and compressive sensing, the results show that l1-SVD can decrease the direct signal leakage but its prerequisite of computational resources remains a major issue. This paper also presents the detection performance of these two algorithms for closely spaced targets.