Joint Deconvolution and Classification for Signals with Multipath

For many sensing modalities such as sonar, received signals are corrupted by multipath and can be challenging for automatic classification systems. An approach to jointly deconvolve and classify such signals is proposed. Specifically, a filter is estimated that minimizes the distortion between the received signal and a set of training signals, then the received signal is assigned to the class that corresponds to the training signal whose estimated filter is most sparse. Simulations compare the new method with blind deconvolution using Cabrelli's algorithm followed by a correlation-based nearest neighbor classifier. Results indicate that joint deconvolution and classification performs similarly to blind deconvolution in the presence of severe noise, and outperforms blind deconvolution at low and moderate noise levels.