Indoor positioning has attracted considerable attention for decades due to the increasinguddemands for location based services. In the past years, although numerousudmethods have been proposed for indoor positioning, it is still challenging to find audconvincing solution that combines high positioning accuracy and ease of deployment.udRadio-based indoor positioning has emerged as a dominant method due toudits ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator)udhas been investigated in the area of indoor positioning for decades. However, itudis prone to multipath propagation and hence fingerprinting has become the mostudcommonly used method for indoor positioning using RSSI. The drawback of fingerprintingudis that it requires intensive labour efforts to calibrate the radio mapudprior to experiments, which makes the deployment of the positioning system veryudtime consuming. Using time information as another way for radio-based indoorudpositioning is challenged by time synchronization among anchor nodes and timestampudaccuracy. Besides radio-based positioning methods, intensive research hasudbeen conducted to make use of inertial sensors for indoor tracking due to the fastuddevelopments of smartphones. However, these methods are normally prone to accumulativeuderrors and might not be available for some applications, such as passiveudpositioning.udThis thesis focuses on network-based indoor positioning and tracking systems,udmainly for passive positioning, which does not require the participation of targetsudin the positioning process. To achieve high positioning accuracy, we work on someudinformation of radio signals from physical-layer processing, such as timestampsudand channel information. The contributions in this thesis can be divided into twoudparts: time-based positioning and channel information based positioning. First,udfor time-based indoor positioning (especially for narrow-band signals), we addressudchallenges for compensating synchronization offsets among anchor nodes, designingudtimestamps with high resolution, and developing accurate positioning methods.udSecond, we work on range-based positioning methods with channel information toudpassively locate and track WiFi targets. Targeting less efforts for deployment, weudwork on range-based methods, which require much less calibration efforts than fingerprinting.udBy designing some novel enhanced methods for both ranging and positioningud(including trilateration for stationary targets and particle filter for mobileudtargets), we are able to locate WiFi targets with high accuracy solely relying on radioudsignals and our proposed enhanced particle filter significantly outperforms theudother commonly used range-based positioning algorithms, e.g., a traditional particleudfilter, extended Kalman filter and trilateration algorithms. In addition to usingudradio signals for passive positioning, we propose a second enhanced particle filterudfor active positioning to fuse inertial sensor and channel information to track indoorudtargets, which achieves higher tracking accuracy than tracking methods solelyudrelying on either radio signals or inertial sensors.
展开▼
