Abstract
Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation, and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many superresolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm (ISTA) is one of the most famous methods because of its antinoise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this article, a second-order accelerated ISTA for scanning radar forward-looking superresolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.
Highlights
M ANY applications, such as terrain avoidance, autonomous landing, and precision guidance, have an urgent need for radar forward-looking imaging
In the real beam scanning radar system, the received echo can be considered as the convolution of the effective scattering coefficients and the convolution kernel consisting of the antenna pattern in azimuth and the pulse modulation function in range direction [18]
Compared with iterative shrinkage-thresholding algorithm (ISTA), the extra computation of SO-accelerated ISTA (AISTA) is the calculation of the predicated vector through vector dot products and simple addition whose computational complexity is O(N )
Summary
M ANY applications, such as terrain avoidance, autonomous landing, and precision guidance, have an urgent need for radar forward-looking imaging. A fast ISTA (FISTA) which has a higher convergence speed is proposed in [26] This algorithm adopts Nesterov accelerated gradient method; before each iteration, it utilizes historical iteration information to construct iteration vectors, reducing the number of iterations and achieving acceleration. The main idea of the accelerated algorithm is that it conducts the iterations on a prediction vector constructed by the first- and the second- order difference information of the iteration vector, to improve the convergence speed further.
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