Multi-sensor classification via sparsity-based representation with low-rank interference

In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification which exploits correlation as well as complementary information among homogeneous and heterogeneous sensors while simultaneously extracting the low-rank interference term. Specifically, 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 sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to optimal solution is guaranteed. Extensive experiments are conducted on a real data set for a multi-sensor classification problem focusing on discriminating between human and animal footsteps. Results are compared with the conventional classifiers and existing sparsity-based representation methods to verify the effectiveness of our proposed models.