Improved indoor positioning based on range-free RSSI fingerprint method

Abstract As the development of modern science and technology, LBS and location-aware computing are increasingly important in the practical applications. Currently, GPS positioning system is a mature positioning technology used widely, but signals are easily absorbed, reflected by buildings, and attenuate seriously. In such situation, GPS positioning is not suitable for using in the indoor environment. Wireless sensor networks, such as ZigBee technology, can provide RSSI (received signal strength indicator) which can be used for positioning, especially indoor positioning, and therefore for location based services (LBS).The authors are focused on the fingerprint database method which is suitable for calculating the coordinates of a pedestrian location. This positioning method can use the signal strength indication between the reference nodes and positioning nodes, and design algorithms for positioning. In the wireless sensor networks, according to whether measuring the distance between the nodes in the positioning process, the positioning modes are divided into two categories which are range-based and range-free positioning modes. This paper describes newly improved indoor positioning method based on RSSI fingerprint database, which is range-free. Presented fingerprint database positioning can provide more accurate positioning results, and the accuracy of establishing fingerprint database will affect the accuracy of indoor positioning. In this paper, we propose a new method about the average threshold and the effective data domain filtering method to optimize the fingerprint database of ZigBee technology. Indoor experiment, which was conducted at the University of Warmia and Mazury, proved that the distance achieved by this system has been extended over 30 meters without decreasing the positioning accuracy. The weighted nearest algorithm was chosen and used to calculate user’s location, and then the results were compared and analyzed. As a result, the positioning accuracy was improved and error did not exceed 0.69 m. Therefore, such system can be easily applied in a bigger space inside the buildings, underground mines or in the other location based services.