Localization and Tracking in Wireless Networks

Given the increasing number of location-based applications in wireless networks, efficient solution to the problem of source localization and tracking has become more and more important. In this dissertation, we present our works on improving source localization and tracking in wireless networks.;First we investigate localization algorithms for the time difference of arrival (TDOA) measurement model. Taking into account the colored measurement noise, we adopt a minmax principle to develop semidefinite relaxation algorithms that can be reliably solved using semidefinite programming with low complexity. The reduction of algorithm complexity is achieved through a simple but effective method for reference node selection among participating measurement nodes such that only a subset of selective time-differences of signal arrival are exploited. Our estimation methods are less sensitive to the source locations and can be used either as the final location estimate or as the initial point for traditional search algorithms.;We also consider the more practical time of arrival (TOA) measurement model, in which the source start transmission time is unknown. We present two new methods that utilize semidefinite programming relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing.;Next, we investigate the emitter source tracking problem in which a mobile tracking sensor and multiple anchored sensors cooperate to track and estimate a mobile source node locations. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semidefinite programming relaxation, and apply a cubic function for mobile sensor navigation. We jointly estimate the location of the mobile sensor and the target to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the mobile sensor to follow the mobile target.;Lastly, we consider the multi source localization problem. With unknown source node index of each received signal, we need to identify the source of each received signal as well as estimate the locations. The combinational nature of the receive signal mixing and ordering makes the problem very complicated. We propose two algorithms based on semidefinite relaxation, and provide corresponding refinement method for each algorithm. In addition, we consider the localization problem given ambiguous anchor node measurement. We propose to identify the ambiguous anchor nodes based on the estimated noise amplitude. After identifying the ambiguous anchor nodes, we can remove the incorrect TOA measurements and obtain more accurate location estimation.;In summary, the works presented in this dissertation provide several effective approaches to localization and tracking in wireless networks under different measurement models. The proposed algorithms improve the performance with relatively low complexity. As the demands for location based service rise, our methods will have applications in real systems.