Semantic Scholar Open Access 2020 21 sitasi

A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers

Linjie Yan P. Addabbo Yuxuan Zhang C. Hao Jun Liu +2 lainnya

Lihat Sumber DOI

Abstrak

In this article, we address the problem of detecting multiple noise-like jammers (NLJs) through a radar system equipped with an array of sensors. To this end, we develop an elegant and systematic framework wherein two architectures are devised to jointly detect an unknown number of NLJs and to estimate their respective angles of arrival. The followed approach relies on the likelihood ratio test in conjunction with a cyclic estimation procedure, which incorporates at the design stage a sparsity promoting prior. As a matter of fact, the problem at hand owns an inherent sparse nature, which is suitably exploited. This methodological choice is dictated by the fact that, from a mathematical point of view, classical maximum likelihood approach leads to intractable optimization problems (at least to the best of authors’ knowledge) and, hence, a suboptimum approach represents a viable means to solve them. The performance analysis is conducted on simulated data and shows the effectiveness of the proposed architectures in drawing a reliable picture of the electromagnetic threats illuminating the radar system.

Topik & Kata Kunci

Computer Science Engineering

Penulis (7)

Linjie Yan

P. Addabbo

Yuxuan Zhang

C. Hao

Jun Liu

Jian Li

D. Orlando

Format Sitasi

APA MLA BibTeX

Yan, L., Addabbo, P., Zhang, Y., Hao, C., Liu, J., Li, J. et al. (2020). A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers. https://doi.org/10.1109/TAES.2020.2988960

Akses Cepat

Lihat di Sumber doi.org/10.1109/TAES.2020.2988960

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 21×
Sumber Database: Semantic Scholar
DOI: 10.1109/TAES.2020.2988960
Akses: Open Access ✓