MTRC-Tolerated Multi-Target Imaging Based on 3D Hough Transform and Non-Equal Sampling Sparse Solution

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 19; p. 3817
• Main Authors: Zou, Yimeng, Tian, Jiahao, Jin, Guanghu, Zhang, Yongsheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: 3D Hough transform; 3D imaging; Algorithms; Azimuth; Cell migration; compressed sensing (CS); Computation; Echoes; Hough transformation; Imaging; Interpolation; Motion compensation; MTRC-tolerated; multi-targets; Optimization; Radar; Radar arrays; Realizability; Sampling; Signal processing; sparse array; Spectrum allocation; Surveillance; Target detection; Three dimensional imaging
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13193817

Distributed radar array brings several new forthcoming advantages in aerospace target detection and imaging. The two-dimensional distributed array avoids the imperfect motion compensation in coherent processing along slow time and can achieve single snapshot 3D imaging. Some difficulties exist in the 3D imaging processing. The first one is that the distributed array may be only in small amount. This means that the sampling does not meet the Nyquist sample theorem. The second one refers to echoes of objects in the same beam that will be mixed together, which makes sparse optimization dictionary too long for it to bring the huge computation burden in the imaging process. In this paper, we propose an innovative method on 3D imaging of the aerospace targets in the wide airspace with sparse radar array. Firstly, the case of multiple targets is not suitable to be processed uniformly in the imaging process. A 3D Hough transform is proposed based on the range profiles plane difference, which can detect and separate the echoes of different targets. Secondly, in the subsequent imaging process, considering the non-uniform sparse sampling of the distributed array in space, the migration through range cell (MTRC)-tolerated imaging method is proposed to process the signal of the two-dimensional sparse array. The uniformized method combining compressed sensing (CS) imaging in the azimuth direction and matched filtering in the range direction can realize the 3D imaging effectively. Before imaging in the azimuth direction, interpolation in the range direction is carried out. The main contributions of the proposed method are: (1) echo separation based on 3D transform avoids the huge amount of computation of direct sparse optimization imaging of three-dimensional data, and ensures the realizability of the algorithm; and (2) uniformized sparse solving imaging is proposed, which can remove the difficulty cause by MTRC. Simulation experiments verified the effectiveness and feasibility of the proposed method.