Clutter Mitigation in Range Enhanced Radar Images Using Sparsity Based Denoising Autoencoders

Complex propagation channels in indoor environments introduce considerable clutter and distortions to frontal radar images. Autoencoders, when provided with corrupted and corresponding clean images during training, can learn how to mitigate clutter in these images during testing. The main advantage is that an analytical framework of the channel is not required. On the other hand, the algorithm relies on the availability of a large database of correctly labelled training data - the target scenarios in cluttered environments must be exactly replicated in free space. We propose to augment the conventional denoising autoencoder with sparsity based multiple layer representations to handle the labelling mismatch errors. We hypothesize that the deeper representations with sparsity constraints will enable extraction of more fundamental features of the images. Our radar data consists of measurements made with a wideband RF imaging sensor called the Walabot. Our algorithm improves upon the conventional autoencoder in terms of performance with respect to signal to clutter and signal to noise ratios, labelling mismatch errors and computational time during testing.