Adaptive sparse imaging approach for ultra-wideband through-the-wall radar in combined dictionaries

The existing algorithms of ultra-wideband through-the-wall radar sparse imaging mostly adopt point target model. Also the regularization parameter of sparse optimization can not be adjusted adaptively, and the ghost imaging can be produced if the targets are not positioned at the pre-discretized grid location. To deal with the above issues, an adaptive sparse imaging algorithm based on Bayesian evidence framework is proposed, which represents sparsely the scene with the point targets and the extended targets by combination of appropriate dictionaries, and maximizes hierarchically the likelihood-function of all parameters as well. The first-level inference of the Bayesian, combined with conjugate gradient algorithm, is adopted to estimate the sparse representation coefficients of the combined dictionaries. The second-level inference of the Bayesian is adopted to estimate the regularization parameter as well as the targets' off-grid shifts. Therefore, the problem can be solved through iterative optimizating the parameter setting. The simulation and experimental results show that the proposed method can not only adaptively enhance the characteristics of both the point targets and the extended targets, but also mitigate ghosts caused by off-grid targets. © 2016, Science Press. All right reserved.