connected devices. In this connection. Kalman filter is a well-known, abundantly employed and an established optimal estimator for linear system. Kalman filter has the beauty that it can handle both transition and measurement system noises [i]. The propagation of Gaussianity via system dynamics is the central operational point of KF. The development of Kalman filter has modernized the field of estimation, and has a massive impact on the design and development of accurate navigation systems [i]. It has been used in almost all modern control and communication systems, both military and space technology such as in inertial guidance systems in aircraft [ii], missile autopilots, submarines, phased- array radars to track missiles, the Global Positioning System(GPS) [iii], the space shuttle, rockets [iv] and in separating the speech signals under additive white Gaussian noise channel [v]. However, in majority of applications, the system under observation is nonlinear. Hence filtering schemes were immediately modified to cope the situations including nonlinearity. In modern era, nonlinear filtering and estimation have been a subject of active research such as signal processing, navigation, control, target tracking, neural network training and majority of electrical/electromechanical systems [vi-viii]. For handling nonlinear functions nonlinear estimation tools including the extended Kalman filter (EKF) [ix-x] and unscented Kalman filter (UKF) [xi] are widely used. Generally speaking, in estimation theory EKF may be called the nonlinear adaptation of the linear KF as it linearizes a nonlinear function of the system model around the current state estimate. In this nonlinear estimation scheme, the predicted state is approximated through a GRV that propagate analytically through first order Taylor series. Since EKF employs a posterior mean and covariance entities so it may lead to sub-optimal results. This may cause divergence of filter ultimately. Proposed work in this article that is statistically linearized Kalman filter (SLKF) deal with the mentioned problem resulting optimal estimation. Another dilemma associated with EKF is that, numerical Jacobians are required in the 35 Abstract-Traditional schemes of non-linear estimation includes extended Kalman filter (EKF). However due to several shortcomings caused by Jacobian linearization the usage of EKF is problematic. To avoid the problems linked with Jacobian linearization, this paper presents Kalman filtering technique based on statistically linearization. The derivation of this nonlinear estimation scheme has been achieved by steps similar to standard Kalman filter (KF) techniques. The system is linearized through statistical linearization rather than Taylor series. This statistically linearization is implemented to obtain the state of two important models, namely two phase permanent magnet synchronous motor (PMSM) and univariate non stationary growth model. It has been shown that the schemes has generated improved performance than EKF. Various performance indeces have been shown for performance comparison. Results obtained through two estimation techniques are compared with the actual state values. The results obtained through proposed scheme are significantly improved compared to the results obtained for existing schemes. In consequence, the error linked with proposed estimation techniques has been greatly minimized through the use of statistically linearized KF. Keywords-Nonlinear Filter, Extended Kalman Filter, Statistical Linearization, Global Approximation, Jacobian Matrix, Taylor Series. I. INTRODUCTION The process of filtering and estimation has remained one of the most investigated phenomenon in numerous engineering applications. For example, a standard Gaussian noise may corrupt the health and quality of radio communication signals in various perspectives. An efficient and robust algorithm would be the one that could retain information while discarding the unwanted signal. A notable example is UPS (Uninterruptible Power Supply) which are designed to rectify line-voltage for smoothing purposes. These removed fluctuations might hinder the performance of equipment and affect the life span of Enhanced Performance of Two Phase PMSM and Univariate Non-Stationary Growth Models Through Statistically Linearized Kalman Filter 1 2 3 4 5 N. Khan , Atta Ullah , N. Khan , W. Khan , K. Akhtar 1,2,3,4,5 Electrical Engineering Department, University of Engineering and Technology Peshawar, KPK, Pakistan 5 nkhan@uetpeshawar.edu.pk Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan Vol. 22 No. 3-2017 ISSN:1813-1786 (Print) 2313-7770 (Online)