Single-target tracking using a standard Kalman filter with fixed measurement noise covariance will be effective if the target originated measurement is known. Under measurement origin uncertainty (MOU) the target state is updated in a probabilistic data association (PDA) framework using the set of measurements obtained inside a validation region (gate region). This paper develops a model for validated measurements using a conventional target originated measurement model and a model for measurements with uncertain origin. Using the developed model for validated measurements the measurement noise covariance under measurement origin uncertainty (MOU) is estimated. With this model the multiplicative scalar information reduction factor (IRF) in the computation of Cramer´-Rao lower bound (CRLB) with MOU is shown to be due to an additive term in the measurement noise covariance. This additive term is used in the probabilistic data association (PDA) filter for computing the spread of innovation. This leads to a modified measurement noise covariance, innovation covariance and Kalman filter gain resulting in an adaptive iterative PDA (Iter-PDA) filter. Improvements obtained using the proposed approach are demonstrated through Monte Carlo (MC) simulations by comparing with PDA and CRLB. The consistency of the modified filter is checked and found to be within the acceptable limits.