Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning radar based on Bayesian theory, which states that the angular super-resolution can be realized by solving the corresponding deconvolution problem with the maximum a posteriori (MAP) criterion. The algorithm considers that the noise is composed of two mutually independent parts, i.e., a Gaussian signal-independent component and a Poisson signal-dependent component. In addition, the Laplace distribution is used to represent the prior information about the targets under the assumption that the radar image of interest can be represented by the dominant scatters in the scene. Experimental results demonstrate that the proposed deconvolution algorithm has higher precision for angular super-resolution compared with the conventional algorithms, such as the Tikhonov regularization algorithm, the Wiener filter and the Richardson–Lucy algorithm.
The forward looking radar imaging task is a practical and challenging problem for
adverse weather aircraft landing industry. Deconvolution method can realize the
forward looking imaging but it often leads to the noise amplification in the radar
image. In this paper, a forward looking radar imaging based on deconvolution method
is presented for adverse weather aircraft landing. We first present the theoretical
background of forward looking radar imaging task and its application for aircraft
landing. Then, we convert the forward looking radar imaging task into a corresponding
deconvolution problem, which is solved in the framework of algebraic theory using
truncated singular decomposition method. The key issue regarding the selecting of the
truncated parameter is addressed using generalized cross validation approach.
Simulation and experimental results demonstrate that the proposed method is effective
in achieving angular resolution enhancement with suppressing the noise amplification
in forward looking radar imaging.
The aim of angular super-resolution is to surpass the real-beam resolution. In this paper, a method for forward-looking scanning radar angular super-resolution imaging through a deconvolution method is proposed, which incorporates the prior information of the target's scattering characteristics. We first mathematically formulate the angular super-resolution problem of forward-looking scanning radar as a maximum a posteriori (MAP) estimation task based on the forward model, and convert it to an equivalent unconstrained optimization problem by applying the log-transforms to the posterior probability, which guarantees the solution converges to a global optimum of an associated MAP problem and it is easy to implement. We then implement the unconstrained optimization task in convex optimization framework using an iterative shrinkage method, and the computational complexity of the proposed algorithm is also discussed. Since the anti log-likelihood of the noise distribution and the prior knowledge of the scene are utilized, the proposed method is able to achieve angular super-resolution imaging in forward-looking scanning radar effectively. Numerical simulations and experimental results based on real data are presented to verify that the proposed deconvolution algorithm has better performance in preserving angular super-resolution accuracy and suppressing the noise amplification.
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