A maximum a posteriori (MAP) approach, based on the Bayesian criterion, is proposed to overcome the low cross-range resolution problem in forward-looking imaging. We adapt scanning radar system to record received data and exploit deconvolution method to enhance the real-aperture resolution because the received echo is the convolution of target scattering coefficient and antenna pattern. The Generalized Gaussian distribution is considered as the prior information of target scattering coefficient in MAP approach for the reason that it could express different target scattering coefficient properties with the control of statistic parameter. This constraint term makes the proposed algorithm useful in different applications. On the other hand, the reconstruction problem can also be viewed as the l p-norm (0 < p ≤ 2) regularization. Simulation results show the robustness of the proposed algorithm against additive noise compared with other superresolution methods.

In real aperture imaging, the limited azimuth angular resolution seriously restricts the applications of this imaging system. This report presents a maximum a posteriori (MAP) approach based on the Bayesian framework for high angular resolution of real aperture radar. First, Rayleigh statistic and the lq norm (for 0 < q ≤ 1) sparse constraint are considered to express the clutter property and target scattering coefficient distribution, respectively. Then, the MAP objective function is established according to the hypotheses above. At last, a recursive iterative strategy is developed to estimate the original target scattering coefficient distribution and clutter statistic. The comparison of simulations and experimental results are given to verify the performance of our proposed algorithm.

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).

A maximum a posteriori (MAP) algorithm based on Bayesian criterion for high‐resolution direction‐of‐arrival (DOA) estimation in array signal processing is proposed. The generalised Gaussian distribution was considered as the prior information of sources distribution for its wide applicability. The statistic parameter of the generalised Gaussian function can be transformed according to different sources distribution conditions. Only the sparse signal recovery problem is considered. It is found that with the generalised Gaussian sparse constraint, the proposed MAP algorithm provides high DOA estimation performance in the case of limited snapshots. Simulation results are given to verify the effectiveness and efficiency of the proposed algorithm.