Self-contained indoor position and azimuth estimation for pedestrians based on smartphone sensors

With the use of smartphones in daily life positioning technologies get more and more important. For positioning, satellite receivers, GSM (Global System for Mobile Communications), UMTS (Universal Mobile Telecommunications System), WLAN (Wireless Local Area Network / Wi-Fi) modules and inertial sensors can be used by smartphone applications. The so called location based services range from calls for taxis, finding points of interests to city and museum guides. A prerequisite are new and cheap approaches for seamless pedestrian navigation in indoor and outdoor environments. Commonly the first choice for navigation is the Global Positioning System (GPS). However, the lack of precision and availability of GPS in urban and indoor environments is a prevalent problem. As an alternative or complementary solution for indoor environments positioning approaches based on the received signal strength (RSS) in WLANs. Nowadays, because of an increasing number of public and private base stations WLAN positioning becomes more and more attractive for navigation and is already integrated into many smart phones. The positioning accuracy can be improved by combining WLAN positioning with dead reckoning, using low cost inertial sensors. One remaining challenge is estimating the heading, or better the pose, of a person. Indoors, magnetic disturbances lead to unreliable compass outputs. Estimating the heading of a pedestrian using the speed vector calculated from consecutive positions has a very low accuracy, as pedestrians move very slowly compared to the positioning variance of indoor positioning systems. Also, pedestrians can turn anytime without changing their position. A tracking approach for estimating the azimuth angle regarding north and a two-dimensional position of a mobile unit carried by a pedestrian is presented. Using WLAN signal strength measurements the position of a mobile receiver can be estimated using so called fingerprinting methods. If the signal strengths measurements are collected with directional antennas additionally the azimuth can be estimated. For sensor data fusion of WLAN signal strength measurements, acceleration measurements and angular rate measurements a particle filter is presented. Measurement results are presented. Including step detection based on acceleration measurements reduces mainly the positioning error, including angular rate measurements reduces mainly the azimuth estimation error. Especially in indoor environments this approach facilitates the use of electronic guides that offer additional information by means of augmented reality, e.g. on museum exhibits in visual range

Wi-Fi attitude and position tracking

An approach for pedestrian navigation in indoor environments is presented. It addresses mobile platforms with low processing power and low-cost sensors. Outdoors the horizontal attitude of a device can be easily detected using electronic compasses. Indoors magnetic disturbances lead to unreliable compass outputs. In this paper a novel approach for attitude and position tracking is introduced. Four horizontally arranged directional antennas are used to collect the Wi-Fi signal strengths of transmitters (access points) in range. For attitude estimation an extended Kalman filter is used, and for position tracking Wi-Fi fingerprinting. With this approach the attitude of a mobile device can be estimated and the position can be tracked in indoor environments like e.g. museums. This enables the use of electronic guides that offer additional information by means of augmented reality on exhibits in visual range. Possible accuracies are evaluated in simulations. A test with measurements collected in a museum demonstrates the functionality of the approach