Research On Location Technology In Ultra-Wideband System

Ultra-wideband (UWB) has recently received significant attention in both academia and industry for applications in wireless communications in recent years. It has been proved that UWB can support data rates higher than 100Mbits/s, even 1Gbits/s, within a 10 meters distance and coexist well with existing narrowband wireless systems. UWB impulse radio makes up of ultra-short duration pulses which yield ultra-wideband signals characterized by extremely wide frequency band and extremely low power spectral densities. UWB has many benefits, including high data rate, low transmit power, low probability of intercept, exceptional multipath immunity, low interference and accurate position localization(UWB allows up to a few centimeters ranging accuracy ranging).Whether in the military field or in the civilian field ,its application is becoming wider and wider because of its benefits. If we accomplish wireless localization using UWB on cargo tracking, patients monitoring or searching and rescuing of urgent accident locale, we can get twice the result with half the effort.Wireless localization has been widely applied in the military field and civilian field. Wireless localization and navigation system include: radar, GPS, localization in the mobile communication system and so on. The traditional localization technologies have low positioning precision in the indoor environment owing to the multipath and Non-Line of Sight (NLOS). The coming wireless localization technology will combine indoor localization with outdoor localization; it can achieve seamless and accurate localization. UWB technology makes a figure among many categories of wireless localization technology because it has many benefits itself. Other localization technology doesn't match to UWB technology on localization and tracking.Wireless localization technologies estimate the geometry position of a target mobile by detecting the characteristic parameters of the signals which transmit between the mobile terminal and base stations. We often use some signal characteristic parameters such as Strength of Arrival (SOA), Angle of Arrival (AOA), Time of Arrival (TOA) and Time Difference of Arrival (TDOA). Although the SOA localization method is easy, it is bad in precision. The TOA localization method is high precision but requires much on time synchronization. The TDOA localization method can eliminate the dependence on time datum and still guarantee certain localization precision. The AOA localization method's receiving set is complex but it can improve localization precision. Those four types of method have their own advantages and disadvantages, therefore it can achieve higher precision than single method to consider hybrid methods to integrate the merits of using over two types of schemes.The hybrid methods can improve localization precision.This dissertation chooses hybrid TDOA/AOA localization method based on Kalman Filter algorithm.This dissertation focuses on localization technology in Ultra wideband system. In the first place, this dissertation points UWB's advantage on wireless localization technology through analyzing theory of UWB wireless communication. In the next place, we focus on analyzing all kinds of localization technologies, introduced their localization theory, analyzed their localization methods and pointed the proper method in UWB localization system. In the third place, we have done thorough research on the following algorithms based on time of arrival. We have studied Direct Method (DM), Taylor series method (TS), Optimization-based methods in the static location. We have thorough studied a series of Bayesian methods, Kalman Filter algorithm and Extended Kalman Filter algorithm. This dissertation's research emphases is hybrid TDOA/AOA localization method based Kalman Filter algorithm.Kalman Filter algorithm introduces the concept of state space to the theory of random estimation, which regards the process of signal input as a linear system's output under the condition of white noises, depict these input-output relation through state equation. The style of Kalman Filter relate with transition function and measurement function. Kalman Filter algorithm can estimate not only smooth single dimensional random process but also unsmooth multidimensional random process because the information that it use is variable of time domain. If the system is linear, and the noises are Gaussian, Kalman Filter algorithm provides the optimal solution. Otherwise, if the system is nonlinear, a local linearization of the system equations may be a sufficient description of the nonlinearity. The EKF is based on this approximation, and provides a suboptimal estimation. This dissertation brings up a kind of hybrid TDOA/AOA localization method based on Kalman Filter algorithm.There are six chapters in this dissertation.Chapter one has clarified the developments of UWB, applications of UWB and research status quo of UWB. At the end of this chapter the purpose of research, main content and chapter arrangement are introduced.Chapter two gave an introduction to UWB technology from definiens, advantages, method utilizing UWB, status quo of standard and so on; analyzed channel model of UWB. This chapter brings up channel model in this dissertation. Chapter three studied wireless localization technology, analyzed the advantages and disadvantages of different wireless localization technology in UWB system. This chapter indicated the proper method in UWB localization system by theory and simulations.Chapter four analyzed some localization algorithm in UWB system and compared these localization algorithms through simulations by Matlab. Chapter five introduced the concepts of Bayesian estimation, analyzed theory of Kalman filter, deduced the biased Kalman filter formulates and unbiased Kalman filter formulates, and researched Extended Kalman filter algorithm. To improve the accuracy of position, eliminate or mitigate the effects of NLOS errors and multipath in UWB environment, this chapter brought up hybrid TDOA/AOA positioning scheme with an AOA information selection based on above research. In the first place, we checked periodically LOS/NLOS transmission conditions between mobile stations and base stations using previous range measurement and result from unbiased Kalman filter; if the NLOS propagation scenario is detected, a biased Kalman filter is used in mitigating the NLOS TOA error; the AOA information from all base stations are processed by the AOA selection to avoid introducing large NLOS bearing error into the position tracking stage. The formulated TDOA data and the selected AOA data are processed by the extended Kalman filter to obtain the mobile stations location. This chapter gave detailed deduced process and research performance of localization by computer simulation. The simulation results showed that the position scheme with AOA selection has better performance than the method without AOA selection, especially when the standard derivation of bearing measurement noise is small. Chapter six summarized the work content of this dissertation, and made further prospects according to the status of the research work.