Recovery of Binary Sparse Signals with Biased Measurement Matrices
Provides theoretical recovery guarantees for sparse binary signals in compressed sensing with biased measurements, which is an incremental contribution to the field.
This paper addresses recovery of sparse binary signals using box-constrained basis pursuit with biased measurement matrices, showing that under certain probabilistic conditions recovery is highly likely and the solution can be obtained via box-constrained least-squares.
This work treats the recovery of sparse, binary signals through box-constrained basis pursuit using biased measurement matrices. Using a probabilistic model, we provide conditions under which the recovery of both sparse and saturated binary signals is very likely. In fact, we also show that under the same condition, the solution of the boxed-constrained basis pursuit program can be found using boxed-constrained least-squares.