WLAN/inertial sensors combined positioning system

RSS (Received signal strength) floating is the major limit to WLAN (wireless local area network) based positioning accuracy. We proposes an algorithm based on particle filter in order to realize a WLAN/inertial sensors combined positioning system using inertial sensors in the intelligent mobile terminals. The system fuses the complementary information from WLAN and inertial sensors positioning techniques to correct the positioning error due to the RSS floating and the accumulated error of inertial sensors. The experimental results show that the positioning accuracy of the proposed system is reduced by 49% and 88% as compared with WLAN based system and inertial sensors based system. Introduction With the rapid development of the Internet of Things and smart city construction, LBS (location based services) based application is more and more widely used. Airports, shopping malls, underground parking and so on all require accurate indoor positioning information. At the same time, the mobile terminal is growing rapidly in recent years, and especially intelligent mobile terminals is sought after by more and more users because of its unique advantages. Positioning applications based on intelligent mobile terminals are mostly applied to relatively open outdoor environment, but it has a poor performance in the indoor environment. To solve the problem of indoor positioning, the location fingerprinting positioning method based on WLAN attracts a wide range of research. Compared with other indoor positioning system, such as Ultra-Wideband and ZigBee, WLAN based indoor positioning system owns the advantages of low cost, high accuracy and wide range of application. But RSS floating caused by multipath interference seriously deteriorates WLAN positioning accuracy. Meanwhile, it will take a toll to positioning failure in the area without the coverage of AP (access point), so WLAN positioning needs supplement of other positioning systems. With inertial sensors are widely used in the intelligent mobile terminal, researches on inertial sensors positioning also become popular. PDR (Pedestrian dead reckoning) is a navigation method using inertial sensors. The advantage of PDR is its low cost and high accuracy in short time. And the disadvantage is the presence of accumulated error, so we are unable to obtain long-term stable and reliable positioning information. We propose an indoor positioning method combining WLAN positioning method and inertial sensors. We use inertial sensors information to correct the errors caused by RSS floating and the lack of Aps. Meanwhile, the WLAN positioning results can help correct accumulated error caused by inertial sensors. Related Work RADAR protosystem put forward by the wireless network communication group, is the first public positioning system based on WLAN. They used Fingerprinting method to position which worked fine and implemented easy. Fingerprinting method has been a popular research direction so far. 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 723 The research of Huang ChengLiang, Chen Wei [5,6] and so on used the acceleration signal to measure pedestrians’ walking step, which used the models to estimate step size and the course signal to calculate the information about the location, distance and direction of the pedestrians. The problem was decomposed into Step Detection, Step Length Estimation and Heading Determination. Precision of inertial sensors was not so important to PDR. Methods Fingerprinting positioning technology based on WLAN. Fingerprinting positioning technology based on WLAN has been a leading indoor positioning method at present. There are two main phases in Fingerprinting positioning process, they are offline phase and online phase. (1) Offline phase The target of Offline phase is to establish a positioning fingerprinting database. Its job is mainly gathering fingerprint feature information-RSS of RP’s (reference point) position in region of interest. Mobile terminal measures RSS from different AP in each reference point one by one. Meanwhile mobile terminal will store the corresponding MAC address and coordinate information in database, until spreading all over whole region of interest. (2) Online phase Mobile terminal measures RSS of AP around, and matching calculates them with RSS vector stored in database beforehand. Matching principle is according to Euclidean distance of RSS, as formula (1) shows: