当前位置: X-MOL 学术IEEE Trans. Aerosp. Electron. Sys. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
On Estimating Nonlinear Frequency Modulated Radar Signals in Low SNR Environments
IEEE Transactions on Aerospace and Electronic Systems ( IF 4.4 ) Pub Date : 2021-01-11 , DOI: 10.1109/taes.2021.3050649
Sebastian Alphonse , Geoffrey A. Williamson

Estimating the signal transmitted by a noncooperating radar is of great use in radar countermeasures. In this article, we propose to improve the noise resilience of the radar signal estimation by constraining the estimate to be in a low-dimensional signal space. In particular, we focus on estimating nonlinear frequency modulated radar signals that have good target resolution characteristics. A flexible 3-D odd polynomial frequency signal model is introduced as the low-dimensional signal space to compute accurate estimates at low signal-to-noise ratios (SNRs) with small number of intercepted signals. The quality of the low-dimensional signal estimates is compared with that of the estimate from the unconstrained high-dimensional space computed using principal component analysis estimator, which is the method in predominant use in the multisensor scenario. It is shown that signal estimation through low-dimensional signal spaces yields more accurate estimates at low SNRs with a smaller number of sensors compared to searching in the unconstrained high-dimensional signal space.

中文翻译:

在低信噪比环境中估计非线性调频雷达信号

估计非合作雷达发射的信号在雷达对抗中非常有用。在本文中,我们建议通过将估计限制在低维信号空间中来提高雷达信号估计的噪声弹性。特别是,我们专注于估计具有良好目标分辨率特性的非线性调频雷达信号。引入了灵活的 3-D 奇多项式频率信号模型作为低维信号空间,以计算具有少量截获信号的低信噪比 (SNR) 下的准确估计。将低维信号估计的质量与使用主成分分析估计器计算的无约束高维空间的估计进行比较,这是多传感器场景中主要使用的方法。结果表明,与在无约束的高维信号空间中搜索相比,通过低维信号空间的信号估计在低 SNR 下产生更准确的估计,传感器数量更少。
更新日期:2021-01-11
down
wechat
bug