Abstract
Maximum a posteriori (MAP) approach, based on Bayesian criterion, is proposed to overcome the low azimuth resolution in real-aperture imaging. The essence of this approach is to use the statistical characteristics of the imaging background and target to invert the real target scene. This paper presents a deconvolution method based on Maximum a posteriori (MAP) criterion, which combines the Rayleigh distribution and Lognormal distribution, to realize high angular resolution for sea-surface target. Firstly, Rayleigh distribution is considered to express the statistical properties of sea clutter. Moreover, the Lognormal distribution is employed to represent the statistical properties of target as prior information. The reason is that Lognormal distribution can be approximatively regarded as a combined constraint term. Finally, the optimization theory is utilized to obtain the iterative estimated solution. The processed results of simulation and measured data are given to verify the performance of proposed algorithm.
Highlights
The reconnaissance and detection of sea-surface target are of crucial importance, which are applied to marine environmental monitoring, marine vessel detection and maritime rescue
SIMULATON RESULTS the simulation results and the analysis of the handled image are given to verify the performance of proposed RLGMAP algorithm
This paper focused on the problem of low azimuth resolution in forward-looking radar imaging of sea-surface target and proposed a deconvolution RLGMAP algorithm
Summary
The reconnaissance and detection of sea-surface target are of crucial importance, which are applied to marine environmental monitoring, marine vessel detection and maritime rescue. Bayesian deconvolution approaches, relying on the statistical characteristics of noise and the prior knowledge of target scatters, can convert the solving problem of angular superresolution into maximum a posterior probability (MAP) estimation [15], [16]. In these references [17]–[19], the MAP method was presented based on the assumption that the statistic of noise obeys Gaussian or Poisson distribution, which is effective in the process of recovering land target.
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