Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario
Forward-looking imaging is crucial in many civil and military fields,such as precision guidance,autonomous landing,and autonomous driving.The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history.The deconvolution method can be used to improve the quality of forward-looking imaging;however,it will not work well for complex imaging scenes.To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations,an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution.First,the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene.Then,the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model.Finally,the super-resolution imaging problem was solved using the Bayesian framework.Because the sparsity characterization parameters are embedded in the entire process of imaging,the forward-looking imaging parameters will be updated during each iteration.The effectiveness of the proposed algorithm was verified using simulation and real data.
万方数据
维普
