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The authors consider the problem of identifying bad measurements in compressive sensing. These bad measurements can be present due to malicious attacks and system malfunction. Since the system of linear equations in compressive sensing is under-constrained, errors introduced by these bad measurements can result in large changes in decoded solutions. They describe methods for identifying bad measurements so that they can be removed before decoding. In a new separation-based method they separate out top nonzero variables by ranking, eliminate the remaining variables from the system of equations, and then solve the reduced over-constrained problem to identify bad measurements.
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