In recent years there has been a rapid increase in indoor applications of mobile robots, where the knowledge of real-time location has become more and more important. In this framework, the methods about the location estimation are gaining the focus under the umbrella of Indoor Positioning technology, which is suitable for location-based applications. Here, for the purpose of dynamic and static tracking of a mobile user's location, we combine RSS (Received Signal Strength) measurement of WLANs (Wireless Local Area Networks) with the dynamic modeling of the mobile user. The joint approach is preferred for the minimization of the errors that are bound to occur due to RSS and to improve the accuracy of the overall estimation. This is accomplished through Non-Parametric Information Filter (NI Filter), which does not require an explicit information concerning RSS-position, and the Likelihood Density Estimation, which has low-error rate performance. The overall approach enabled us to develop two algorithms for different dynamic motions. Experimental results are obtained by actual measurements in an office environment and ARMSE (Average Root Mean Square Error) has been used as the assessment criteria. The study in mitigating the difficulties arising due to the unpredictable nature of RSS in indoor environment presented an improvement of 5.15m (49,76 %) in positioning error relative to Memoryless Positioning alone.