  In this paper, we propose a general collaborative sparse representation
framework for multi-sensor classification, which takes into account the
correlations as well as complementary information between heterogeneous sensors
simultaneously while considering joint sparsity within each sensor's
observations. We also robustify our models to deal with the presence of sparse
noise and low-rank interference signals. Specifically, we demonstrate that
incorporating the noise or interference signal as a low-rank component in our
models is essential in a multi-sensor classification 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 an optimal
solution is guaranteed. Extensive experiments are conducted on several real
multi-sensor data sets and results are compared with the conventional
classifiers to verify the effectiveness of the proposed methods.
