Abstract
This paper investigates techniques for angular superresolution using limited data of real beam scanning radar (RBSR). In order to improve the angular resolution of RBSR, many algorithms have been proposed. However, for most algorithms, large amounts of sampling data is necessary. The requirement of data increases the burden of the radar system. Fortunately, the sparse signal reconstruction techniques provide a new train of thought for us. It has been proved in array signal processing and image processing that the techniques only need limited sampling data to realize DOA estimation and image superresolution. This paper describes the sparse sampling model of RBSR as an underdetermined equation-solving problem, the received signals are sparsely recovered in target domain. Two algorithms, including smooth approximation algorithm and focal underdetermined system solver (FOCUSS), based on different optimization ideas, are adopted to solve the problem. Simulation results show that compressive sampling methods can recover the target domain accurately, especially under the condition of high signal-to-noise ratio (SNR).
Highlights
Real beam scanning radar (RBSR) has been widely used in both civilian and military fields, owing to its all weather and day/night ability
We can obtain a high-range resolution by transmitting high bandwidth linear FM signal and using the matched filtering technique [3], the limited azimuth angular resolution and unmatched twodimensional radar image greatly restrict the application of the RBSR system
Because the imaging process of RBSR can be regarded as the convolution of antenna pattern function and discretized target vector, the signal model can be written as the following matrix form y = Ax + n
Summary
Real beam scanning radar (RBSR) has been widely used in both civilian and military fields, owing to its all weather and day/night ability. In [6,7,8,9], a kind of novel DOA estimation methods based on the sparse signal recovery were proposed, which took advantage of the sparse distribution of azimuth target when the number of sampling data is limited. It is possible to establish the real beam sparse signal model by greatly increasing the sampling interval, and realize angular superresolution imaging by utilizing sparse signal recovery algorithms.
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