Maximum Likelihood Estimation From Sign Measurements With Sensing Matrix Perturbation

The problem of estimating an unknown deterministic parameter vector from sign measurements with a perturbed sensing matrix is studied in this paper. We analyze the best achievable mean-square error (MSE) performance by exploring the corresponding Cramér-Rao lower bound (CRLB). To estimate the parameter, the maximum likelihood (ML) estimator is utilized and its consistency is proved. We show that, compared with the perturbed-free setting, the perturbation on the sensing matrix exacerbates the performance of the ML estimator in most cases. However, suitable perturbation may improve the performance in some special cases. Then, we reformulate the original ML estimation problem as a convex optimization problem, which can be solved efficiently. Furthermore, theoretical analysis implies that the perturbation-ignored estimation is a scaled version with the same direction of the ML estimation. Finally, numerical simulations are performed to validate our theoretical analysis.