Space-Range-Doppler Focus-Based Low-observable Moving Target Detection Using Frequency Diverse Array MIMO Radar | Zendy

Xiaolong Chen | Zendy; Baoxin Chen | Zendy; Jian Guan | Zendy; Yong Huang | Zendy; You He | Zendy

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Space-Range-Doppler Focus-Based Low-observable Moving Target Detection Using Frequency Diverse Array MIMO Radar

Author(s) -

Xiaolong Chen,

Baoxin Chen,

Jian Guan,

Yong Huang,

You He

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2863745

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The radar echoes of moving targets in a clutter background are extremely weak and their characteristics are complex. A low-observable moving target detection technology has become a key constraint to radar performance, requiring the radar to provide flexible freedom and higher parameter estimation capabilities. In this paper, a frequency diverse array MIMO (FDA-MIMO) radar is used for moving target detection in a clutter background, which combines the information in spatial (azimuth)range-frequency (Doppler) domains. Based on the signal model of the FDA-MIMO radar, a novel signal processing method is proposed named as space-range-Doppler focus (SRDF) processing. Theoretical and simulations analysis shows that this method can be regarded as the combination of multiple cascaded signal processing, such as beamforming, pulse compression, and Doppler filtering of a conventional array radar. It can distinguish clutter and moving targets in multidimensional space, improve signal-to-clutter ratio, and achieve high-precision measurement of target motion parameters via sparse time-frequency distributions. It will provide a new approach for moving target detection and classification in complex backgrounds.

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