Robust Multi Sensor Classification via Jointly Sparse Representation

In this project, we have developed various novel collaborative sparse representation methods for multi-sensor classification problem, which take into account correlation as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensors observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference terms. Especially, we observe that incorporating the noise or interfered signal as a low-rank component is essential in a multi-sensor problem when multiple co-located sourcessensors simultaneously record the same physical event. Essentially, our proposal combines the strengths of multiple ideas i incorporating related information from different sources sensors to achieve an improvement in the classification performance ii extracting and suppressing a large, dense and correlated hence low-rank signalnoise interference normally appeared in multi-sensor data and iii exploiting prior structure in sparsity representations for efficiency and robustness.