There has been massive amount of research have been conducted in the area of indoor positioning systems specifically it's upwards research trending in Localization Based Services (LBS) within a non-open space environment or in the vicinity of high rise buildings due to the incapability of Global Positioning System (GPS) to do so. Most of the indoor localization techniques proposed by researchers to discover an optimized solution for indoor location tracking that has high precision and accuracy. This paper proposes a model for better accuracy on range-based localization algorithm in non-GPS positioning systems. The proposed model adopts the enhanced Kalman Filter (KF) and Centroid Localization Algorithm that can manipulate noise signal from raw Received Signal Strength Indicator (RSSI). There are 12 tests conducted in two different environments; at the area with less-obstacles and at the area with more obstacles. Three different algorithms are deployed with and without KF where a series of observations and comparisons are made to measure the effectiveness and reliability of KF implementation. Our analysis and finding show that the proposed model improves the accuracy percentage by more than 80%.