在合成脉冲和孔径雷达的低空场景中分辨目标和图像

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2024-10-29 DOI:10.1016/j.sigpro.2024.109754
M. Pourjoula, M. Karbasi, M.M. Nayebi, M.R. Bagheri
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引用次数: 0

摘要

在阵列处理系统中,低角度到达方向(DOA)估计是一个具有挑战性的问题。当目标的仰角极低时,直达路径信号(又称目标)将与来自地球的相应反射信号(又称幽灵)结合在一起。传感系统的角度分辨率通常有限,因此两个间隔很近的信号可能会导致 DOA 估计精度较低。与传统的相控阵雷达相比,多输入多输出(MIMO)雷达具有更多的自由度,本文旨在利用基于这种新兴技术的超分辨率方法来解决这一问题。所提出的方法特别关注合成脉冲和孔径雷达(SIAR)的 MIMO 配置,包括两个关键步骤。首先,在匹配滤波过程中分辨目标的距离、多普勒频率和方位角。然后,利用压缩传感(CS)方法获取滤波信号的仰角信息。我们的模拟结果表明,与传统方法相比,就均方根误差(RMSE)标准而言,所提出的方法在区分低角度目标和相应的鬼影方面具有更高的性能。
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Resolving target and image in low altitude scenarios in synthetic impulse and aperture radars
Low-angle direction of arrival (DOA) estimation is a challenging issue in array processing systems. When the target’s elevation angle is extremely low, the direct-path signal (a.k.a. target) will combine with its corresponding reflection from the earth (a.k.a. ghost). Sensing systems usually have limited angle resolution capability, so the presence of two closely-spaced signals could lead to low DOA estimation accuracy. This paper aims to address this issue by utilizing a super-resolution method based on the emerging technology of multiple-input multiple-output (MIMO) radar, which offer more degrees of freedom than traditional phased array counterparts. The proposed method specifically focuses on the MIMO configuration of synthetic impulse and aperture radars (SIAR) and involves two key steps. First, the targets are resolved in range, Doppler frequency, and azimuth angle in the matched filtering process. Next, the elevation information of the filtered signal is obtained using compressed sensing (CS) approaches. Our simulation results indicate that the proposed method achieves a higher performance in distinguishing low-angle targets from their corresponding ghosts, compared with traditional methods in terms of root mean squared error (RMSE) criterion.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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