Abstract
Abstract With the rapid progress in wireless communications, location estimation techniques, including positioning and tracking algorithms, have received a great deal of attention for location-based services (LBSs) in indoor environments, such as surveillance, guidance, obstacle avoidance, etc. The common methods of determining a location of mobile terminal (MT) in outdoor environments are using the global positioning system or cellular networks. However, such approaches usually could not provide enough location accuracy for indoor applications. Basically, there are two major challenging issues in indoor location estimation; one is the location accuracy, and the other is the computational complexity. To have better indoor LBSs, it is necessary to develop indoor location estimation techniques with good location accuracy and/or low computational complexity. In this dissertation, we investigate how to improve the location accuracy of different location estimation schemes. We present adaptive algorithms based on radio propagation modeling (RPM), Kalman filtering (KF), and radio-frequency identification (RFID) assistance for indoor wireless local area networks (WLANs). In the RPM scheme, we use some specific polynomial fitting functions to determine the location of an MT, which can reduce the number of training data points in comparison with the fingerprinting (FP) method. To improve the location accuracy, we then use the KF algorithm to smooth the location estimation results obtained from the FP and the proposed RPM method. To enhance the location accuracy further, we also use the velocity information of an MT to develop an extended KF (EKF) tracking scheme. In this scheme, the estimated location of an MT is calculated from the constant-speed trajectory and the radio propagation model. Without using the RPM method to obtain the location information, the complexity of the EKF scheme is less than that of the KF scheme. As compared to the FP scheme, both the KF and EKF tracking schemes can alleviate the problem of aliasing, which is the phenomenon of misinterpretation of signal measurements. Furthermore, to overcome the inaccuracy problem around corners, RFID is applied to assist the KF tracking algorithm. The RFID-assisted KF tracking scheme can calibrate the location estimation results and correct the corner effects. Experimental results show that it can achieve excellent location accuracy at the expense of high computational complexity. To reduce the computational complexity of the KF tracking algorithm, we develop two efficient methods for location tracking. First, we replace the decision mode of the KF tracking algorithm with an Alpha-Beta (α-β) algorithm, which is a degenerate form of the KF algorithm, to avoid repeatedly calculating the Kalman gain. With α-β tracking, the exact information of the state and measurement noise parameters used in the KF algorithm is not required. Simulation results show that the performance of the α-β tracking method is close to that of the KF algorithm under a stationary environment. Nevertheless, the α-β tracking scheme is based on a fixed-coefficient filtering, so it is not flexible enough. To avoid this disadvantage, we use a forward factor graph (FG) algorithm, instead of the KF algorithm, to simplify the implementation of Bayesian filtering. The FG algorithm is based on passing the data reliability information between the variable nodes and the factor nodes, and this inherent message-passing nature is helpful to location tracking. As compared to the KF tracking scheme, the proposed FG approach achieves close location accuracy with much lower computational complexity. With both features of good location accuracy and low computational complexity, the proposed FG tacking scheme is attractive for use in indoor WLAN applications.