Classification of multiple targets based on disaggregation of Micro-Doppler signatures

Micro-Doppler signatures of dynamic indoor targets (such as humans and fans) serve as a useful tool for classification. However, all the current classification methods are limited by the assumption that only a single target is present in the channel. In this work, we propose a method to classify multiple targets that are simultaneously present in the channel on the basis of a single channel source separation technique. We apply sparse coding based dictionary learning (DL) algorithms for disaggregating micro-Doppler returns from multiple targets into its constituent signals. The classification is subsequently carried out on the disaggregated signals. We have tested the performance of the proposed algorithm on simulated human and fan data.