Superresolution Imaging for Forward-Looking Scanning Radar With Generalized Gaussian Constraint

The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets' backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited antenna beamwidth. However, as a typical inverse problem, deconvolution is essentially a highly ill-posed problem which is sensitive to noise and cannot ensure a reliable and robust estimation. In this paper, multi-channel deconvolution is proposed for improving the performance of deconvolution, which intends to considerably alleviate the ill-posed problem of single-channel deconvolution. To depict the performance improvement obtained by multi-channel more effectively, evaluation parameters are generalized to characterize the angular spectrum of antenna pattern or singular value distribution of observation matrix, which are conducted to compare different deconvolution systems. Here we present two multi-channel deconvolution algorithms which improve upon the traditional deconvolution algorithms via combining with multi-channel technique. Extensive simulations and experimental results based on real data are presented to verify the effectiveness of the proposed imaging methods.

Section: Mathematical Model Of Signalmentioning

confidence: 99%

Section: Multi-channel Maximum a Posteriori-based Deconvolutionmentioning

confidence: 99%

Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging

Xia

Chen

2017

“…Real aperture radar (RAR) can scan an observation area using an antenna to produce two-dimensional range and cross-range imagery. RAR can be used for any imaging geometry, without the need for coherent processing in the cross-range context [12]. Therefore, RAR is of considerable interest for forward-looking imaging, where synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) techniques fail to work [13].…”

Section: Introductionmentioning

confidence: 99%

Real Aperture Radar Angular Super-Resolution Imaging Using Modified Smoothed L0 Norm with a Regularization Strategy

Yang,

Zhao,

Tuo

et al. 2023

Self Cite

Restricted by the ill-posed antenna measurement matrix, the conventional smoothed L0 norm algorithm (SL0) fails to enable direct real aperture radar angular super-resolution imaging. This paper proposes a modified smoothed L0 norm (MSL0) algorithm to address this issue. First, as the pseudo-inverse of the ill-posed antenna measurement matrix is required to set the initial values and calculate the gradient projection, a regularization strategy is employed to relax the ill-posedness. Based on the regularization strategy, the proposed MSL0 algorithm can avoid noise amplification when faced with the ill-posed antenna measurement matrix of real aperture radar. Additionally, to prevent local minima problems, we introduce a hard thresholding operator, based on which the proposed MSL0 algorithm can accurately reconstruct sparse targets. Simulations and experimental results verify the performance of the proposed MSL0 algorithm.

“…This method uses the L 2 norm and the covariance matrices associated with data for denoising and reconstructing sparse signals with the aid of additional information. At the same time, the Bayesian estimation methods are utilized by maximum a posteriori (MAP) to improve the azimuth resolution [26,27]. In the Bayesian methods, the prior information of the imaging system is crucial [28,29].…”

Section: Introductionmentioning

confidence: 99%

Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method

Zuo

et al. 2021

Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target.