Compressive Sensing Radar Imaging With Convolutional Neural Networks

In the area of radar imaging at any frequency band from microwave to optics, the technique of compressive sensing (CS) enables high resolution with reduced number of antenna elements and measurements. However, CS methods suffer from high computational complexity and require parameter tuning to ensure good image reconstruction under different noise, sparsity and undersampling levels. To alleviate such issues, we present a machine learning approach that combines CS and convolutional neural network (CNN) for radar imaging. This CS based CNN (CS-CNN) method maintains good characteristics of CS methods, such as sparse sampling and high resolving power but is free from time-consuming computer optimization and demanding spaces for data storage. In the meantime, it is also robust to environment changes like noise, target sparsity and sampling rate. We have conducted extensive computer simulations for both qualitative and quantitative evaluations. Finally, we experimentally validate the technique with a demonstration of stable high resolution imaging using a sparse multiple-input multiple-output (MIMO) array where traditional imaging methods suffer from serious grating lobes. This approach is generic and can be easily extended to other applications of electromagnetic imaging and sensing.