Information-Theoretic Compressive Measurement Design for Micro-Doppler Signatures

In this work, we utilise gradient-ascent multi-objective optimisation within an information-theoretic compressive sensing framework to classify micro-Doppler (m-D) signatures in the presence of structured input noise. The proposed framework has the potential to simultaneously detect the class of a primary source exhibiting m-D features in its radar return and the class of a secondary, coincident source with its own m-D signature. We demonstrate through simulations with real radar return data that there is a configurable trade-off between the classification accuracies for the two sources and that, given a sufficient number of compressive measurements, the performance for the secondary source can be improved without substantially impacting the classification accuracy for the primary source.