Parameter Estimation via Unlabeled Sensing Using Distributed Sensors

In this letter, the problem of estimating an unknown deterministic parameter is studied, where each sensor acquires a noisy version of the signal and the data at fusion center are unlabeled. Two scenarios are studied: one in which each sensor uses analog communication to transmit its observations with different channel coefficients, and the other in which each sensor uses a different threshold to quantize the noisy signal with a transition matrix describing the channel. Sufficient conditions are provided, under which maximum likelihood (ML) estimators can be found in polynomial time, and numerical simulations are conducted to evaluate performances of ML estimators.