| With the rapid development of wireless sensor networks and the internet of things, the UWB and ZigBee technology for short range and low-power wireless communication have become an important alternative. For indoor environment, the conventional GPS location could not do work well, the indoor wireless ranging and positioning becomes one of the most fundamental applications of wireless sensor networks. Due to the multipath interference, non-Iine-of-sight error and shadow fading constraints of the indoor communication, the dynamic changes in the network topology and the nonlinear error of location algorithm, the approaches to indoor ranging and positioning in wireless sensor networks are required to solve the above problems.Serval approaches to ranging and positioning with Time of Arrival (TOA) and Received Signal Strength (RSS) measurement in UWB and Zigbee systems are pro-posed based on the optimal estimation theory in this dissertation. The main works and contributions are summarized as follows:1. In order to design precise and feasible ranging method with Impulse Radio Ultra Wide Band signal during energy detection, two new TOA estimation algo-rithms based on optimal and suboptimal thresholds are respectively proposed. For optimal method, with the relationship between energy's statistics in receiver and small-scale attenuation, a closed form of threshold is derived, and the TOA esti-mation is obtained under the minimum mean square error. For suboptimal method based on optimal threshold analysis, a recursive form of threshold selection using Newton iteration is developed with false alarm probability constraint.2. A scalar weighting information fusion smoother with modified biased Kalman filter and maximum likelihood estimation is proposed to mitigate the ranging errors in UWB systems. The information fusion algorithm uses both the TOA and RSS measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE802.15.4a is considered as a multi-sensor system with multi-scale sampling. Then a scalar-based IF smoother is proposed to accurately estimate the range mea-surement in the line of sight (LOS) and non-line of sight (NLOS) condition of UWB sensor network. Investigation of the effectiveness of the IF in mitigating errors dur-ing the LOS/NLOS transitions is especially focused.3. In order to reflect the actual channel attenuation, a model selection algorithm for path loss in wireless sensor networks is proposed for RSS ranging estimation. Firstly, the statistical properties of some path loss models are analyzed, and then ex-pectation maximization algorithm from incomplete data of received signal strength is proposed for parameter estimation, finally a set of weighted coefficients are given on the basis of criterion function, which could select an appropriate path loss model. Through experiment, the proposed model selection method could estimate parame-ters effectively, compared with other similar algorithms, this method could pick up a model fitting the experimental data better.4. Since the location information is required to detect folate distributly and the positioning with RSS measurement in indoor environments faces many problem, a RSS-based positioning algorithm based on Bayes estimation and weighted iteration is proposed to improve the positioning accuracy. At first the maximum likelihood estimation is used for the estimation of path loss model parameters. And then Bayes criteria is utilized to establish the posterior probability of the location information. Finally, a weighted iteration algorithm based on the location information of Bayes estimation is proposed for precise positioning. This mathod is successfully applied to embedded folic acid detection systems.In conclusion, this dissertation focuses on the indoor ranging and positioning in wireless sensor networks. The obtained results have not only important theoretic values, but also extensive practical values.
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