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Abstract
Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.
Highlights
Radar forward-looking imaging has extensive applications in military and civil territories, for instance, in the automatic landing of aircraft, material airdrops, topographic mapping, etc. [1,2]
The numerical results based on both the simulation experiment and measured echo are presented to validate the efficiency of maximum a posterior (MAP)-Markov random field (MRF) super-resolution methods for angular super-resolution imaging
It can be observed that the echo energy in the azimuth is broadened and superimposed, which results in the two ships being indistinguishable
Summary
Radar forward-looking imaging has extensive applications in military and civil territories, for instance, in the automatic landing of aircraft, material airdrops, topographic mapping, etc. [1,2]. Radar forward-looking imaging has extensive applications in military and civil territories, for instance, in the automatic landing of aircraft, material airdrops, topographic mapping, etc. Doppler beam sharpening (DBS) techniques cannot be utilized to image the forward area due to the difficulty in acquiring an effective Doppler bandwidth [3,4]. An effective strategy for radar forward-looking imaging is acquiring the real-aperture scanning image first. Signal processing techniques are utilized on the real-aperture image to improve the azimuth resolution. One typical method used is monopulse imaging technology (MIT), which employs the monopulse angle measurement technology to ameliorate the visual quality of the real-aperture image [5,6]. Targets located in the same radar beam cannot be discriminated by the MIT; no actual resolution improvement can be acquired
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