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In this paper, we propose a robust novel approach with closed-form estimator for object tracking based on a non-linear measurement model over time from a single sensor with arbitrary noise degradation. Relying on the widely-used dynamic motion model for arbitrary moving targets, tracking of moving objects can be formulated using received signal strength (RSS) measurements. We provide a closed-form solution that integrates localization and filtering for both an ideal channel as well as noisy channel. We first derive an exact linear model from the non-linear system of equations provided by the RSS measurements. We subsequently present an iterative method to estimate the unknown parameters and the error covariance matrix. Moreover, we prove that the estimator gives more accuracy when the number of samples increases. The Cramer-Rao bound (CRB) for the estimator are determined in Gaussian case. Computer simulation demonstrates that the proposed approach not only achieves more accuracy than traditional methods but also saves significant computation time.