Geometry-Information-Aided Efficient Radial Velocity Estimation for Moving Target Imaging and Location Based on Radon Transform

Abstract

Real-time radial velocity estimation is a key challenge for moving target imaging and location in current single-antenna synthetic aperture radar (SAR)-ground moving target indication systems. Since the conventional methods suffer from ambiguity, complexity realization, or heavy computation load for fast moving target motion estimation, this paper emphasizes the estimation efficiency by simple realization. An efficient Radon transform (RT) estimation is proposed to estimate the radial velocity of fast moving target by utilizing the geometry information, and much more geometry information is exploited to realize clutter cancellation, noise cancellation, and estimation error minimizing in the RT domain, which is not proposed by the others. With only two to four angles used to calculate rather than search for the radial velocity of moving targets, the proposed methods simplify the conventional range and angle (2-D) searching procedure into several time range (1-D) searching procedure efficiently. The theoretical and experimental analysis provides qualitative and quantitative evaluations into the effectiveness of the proposed methods. In the single-antenna SAR system, the proposed methods can estimate the radial velocity of fast moving target efficiently and accurately in high signal to clutter plus noise ratio scenarios.Index Terms-Efficient estimation, geometry information, radial velocity estimation, Radon transform (RT), synthetic aperture radar (SAR)-ground moving target indication (GMTI).

A fast non-searching algorithm for radar maneuvering target detection

“…After performing the range inverse fast Fourier transform (IFFT), the compressed signal in the range time domain is obtained as where A 3 denotes the signal amplitude after performing range IFFT. From (5), one can see that c 1 will cause range walk and Doppler center shift, while c 2 and c 3 will cause range curvature and Doppler frequency migration. During a long integration time, both range migration and Doppler frequency migration will deteriorate the target detection performance.…”

“…The linear range migration, i.e., range walk, is caused by the uniform motion of the target during a long integration period. In order to remove range walk, some algorithms have been proposed which aim at accumulating the energy along the linear trajectory, such as Radon transform (RT) [3][4][5], Hough transform (HT) [6][7][8][9], Radon Fourier transform (RFT) [10][11][12] and improved axis rotation MTD (IAR-MTD) [13]. The above methods need to conduct the parameter searching process which has a high computational burden.…”

Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm

“…, N is the fast time. For NBI, its frequency spectrum usually concentrates within a narrow frequency bins, which can be written as [4] I NBI,n = L ∑ l=1 a l exp{j(2π f l n + ϕ l )}, (2) where a l , f l and ϕ l denote the amplitude, frequency and phase of the l-th NBI, respectively, and L represents the number of NBI. For WBI, LFM-WBI is mainly discussed in this paper, which can be expressed as [36] I WBI,n = L ∑ l=1 a l exp j(2π f l n + πγ l n 2 + ϕ l )…”

“…Synthetic aperture radar (SAR) has become an important instrument for earth mapping and been widely utilized in both military surveillance and civilian exploration. However, well-focused SAR images are always prominently corrupted by untargeted interference caused by natural or man-made factors, especially the interferences whose frequencies fall into the frequency spectrum of useful signals [1,2]. Although the two-dimensional matched filter has an inherent ability in interference suppression, interferences with stronger power will defocus the image and degrade the imaging quality seriously [3,4].…”

“…After that, well-focused SAR imaging results can be obtained by conventional imaging methods. The proposed algorithm has three advantages: (1) both AF and CAF have excellent energy concentration; (2) auto-terms of interferences are more easily identified and extracted in the AF and CAF domains due to their useful properties; and (3) SSM from AF and CAF is faster and more accurate than the traditional SSM from WD. Thanks to these advantages, the proposed algorithm not only suppresses interference but also preserves the useful information as much as possible.…”