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Multi-scale moving target detection with FDA-MIMO radar.

Authors :
Wang, Keyi
Liao, Guisheng
Xu, Jingwei
Zhang, Yuhong
Lan, Lan
Wang, Weiwei
Source :
Signal Processing. Mar2024, Vol. 216, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

The recently developed frequency diverse array (FDA) radar applies a small frequency increment across the array elements, which provides advantages in clutter suppression, jamming suppression, and target detection for existing radar systems. In this paper, the frequency increment is enlarged to guarantee that the transmit waveforms are orthogonal in frequency domain for multiple-input multiple-output (MIMO) radar. A multi-scale moving target detection method of FDA-MIMO radar is proposed. The FDA radar with a large frequency increment suffers from the deteriorated decorrelation of scatterers within the same range bin and causes the inner-bin range dependence (IRD) property in transmit spatial domain. By taking additional space–time snapshots in transmit domain as training samples, the space–time adaptive processing (STAP) is applied in joint receive and Doppler domain for clutter suppression, which alleviates the requirement of insufficient training samples in non-stationary clutter environment. In the sequel, a set of beamformers within the whole angular domain of interest is utilized for target detection, which achieves target detection under the condition of random target locations and sizes. Numerical simulation results are provided to verify the effectiveness of the proposed method. • FDA radar of large Δ f can realize waveform orthogonality and obtain range DOFs. • FDA of large Δ f improves the range resolution to distinguish inner-bin scatterers. • The proposed STAP method alleviates the pressure on insufficient training samples. • A set of beamformers are designed to detect target with random locations and sizes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01651684
Volume :
216
Database :
Academic Search Index
Journal :
Signal Processing
Publication Type :
Academic Journal
Accession number :
174036541
Full Text :
https://doi.org/10.1016/j.sigpro.2023.109301