For target location and tracking in wireless communication systems, mobile target positioning and tracking play an important role. Since multi-sensor system can be used as an efficient solution to target positioning process, more accurate target location estimation and tracking results can be obtained. However, both the deployment of designed multi-sensor and location algorithm may affect the overall performance of position location. In this thesis, based on the time difference of arrival (TDOA), two closed-form least-square location methods, spherical-interpolation (SI) method and spherical-intersection (SX) method are used to estimate the target location. The two location methods are different from the usual process of iterative and nonlinear minimization. The locations of the target and the designed multiple sensors may yield geometric effects on location performance. The constraints and performance of the two location methods will first be introduced. To achieve real-time target tracking, the Kalman filtering structures are used to combine the SI and SX methods. Because these two positioning and tracking systems have different and complementary performance inside and outside the multi-sensor array, we consider using data fusion to improve location estimation results by using interacting multiple model (IMM) based estimator, in which internal filters running in parallel are designed as the SX-KF1 and the SI-KF2. However, due to the time-varying characteristics of measurement noises, we propose an adjusting scheme for measurement noise variance assignment in the Kalman filters to obtain improved location estimation results. Simulation results are obtained by running Matlab program. In three-dimensional multi-sensor array scenarios, the results of moving target location estimation shows that the IMM-based estimators effectively improve the position performance.

In indoor environment, the multi-sensor system can be used as an efficient solution for target location process, in terms of lower estimation cost, due to the factor that sensors have the advantages of low power, simple, cheap, and low operation complexity. However, the location methods and the placements of designed multisensor have great impact on the location performance. Based on the time difference of arrival (TDOA), the present research utilizes linear-correction spherical-interpolation (LCSI) method to estimate the location of its targets. The method is a combination of the linear-correction least-squares method and the spherical-interpolation method. Apart from the usual process of iterative, nonlinear minimization, and consequently, under the influence of noise interference and target-sensor geometry, the spherical-interpolation method will produce better results; therefore, SI method is used in place of the LS part of the LCLS method and named as the LCSI method. The objective is to correct the SI method to generate a better estimate performance. In addition to the performance issues, the limitation of the methods will also be examined. The geometric dilution of precision (GDOP) of the TDOA location method in the 3-D scenario is demonstrated with the effects on location performance of both inside and outside of the multi-sensor formation. Programmed 3-D scenario are used in the simulations, where cases with three different multiple sensor formations and two different target heights are investigated. From the simulation results of various location methods, it can be seen that LCSI has has its advantages over other methods in the wireless TDOA location.

In indoor environment, the multi-sensor system has been proved to be an efficient solution for target locating process in terms of lower estimation cost. However, the placement of designed multi-sensor has great impact on the location performance in an indoor environment. Based on the time difference of arrival (TDOA), closed-form least-square location methods, including the spherical-interpolation (SI) and the spherical-intersection (SX) methods, are used in the estimation of target locations. The two methods are apart from the usual process of iterative and nonlinear minimization. Consequently, under the influence of noise interference, the performance of the two methods also produce different results. In addition to the above issues, the limitation of these methods will also be examined. The geometric dilution of precision (GDOP) effects of TDOA location on location performance of both inside and outside of the multi-sensor environment in the 2-D scenario have been studied in the past. This thesis aims to further advance the performance of GDOP in 3-D scenarios, analyze the differences, and propose the suitable needs. Programmed 3-D scenario simulations are used in this research, designed according to multiple sensor arrays and the moving latitude of a target. The Setup interprets the degree of multi-sensor separation, and distances from targets to the sensor array. A suitable location algorithm and optimal multi-sensor deployments in an indoor environment were proposed according to the simulation results.