Analyzing Collaborative Technology Wi-Fi Bluetooth for localization in Indoor Positioning System in indoor premises

For tracking people or object’s Location in the indoor environments, a network device is suggested known as IPS.. This technology mainly used because of supervising the health, position, guidance in different sectors like Universities, museums, airports, hospitals and warehouses the main advantages of IPS with distinct feature like reduction of material and energy cost over time that has huge impact compared to other technology which are more costly. Among various technology like (UWB) Wireless Fidelity (Wi-Fi) Bluetooth Low Energy (BLE). (Wi-Fi)Technology is an outstanding approach for indoor navigation and positioning due to already available Wi-Fi Infrastructure Wireless IPS has categorized into two approaches :geometrical –calculation based and Scene –Analysis based former relies on measurement of geometrical parameter of Distance and angles by physical characteristics of Signal. RSS is the performance parameter used for achieving robustness and accuracy which is most concerned factor for current scenario Most Recent Research has solved the problem of inconsistency on Received Signal Strength (RSS) using fingerprint method. But the RSSI that received may contain some noise. This paper mainly proposed a method to estimate or tracking the real position of dynamic user. With RSSI value as input to be processed and the result of it will be a location (x, y) value, repeat the process to create an estimate coordinate map of route taken. Our proposed method is based on fingerprinting with weighted sum of four nearest reference access point to estimate the position of dynamic user then using Extended Kalman Filter as a tracking algorithm. In this paper we try new ways to collect the data of RSS by dynamically collecting the data in many routes to see whether the proposed algorithm could estimate the position better. We achieve an average mean of error around 2cm using Weighted Sum using Extended Kalman Filter tested on dynamic data. Key words: Indoor Positioning System, Dynamic User Localization, Fingerprinting Method, Weighted Sum Model, Extended Kalman Filter