The chapter studies the positioning techniques based on ultra wideband (UWB) and low cost inertial measurement unit (IMU) with a focus on the fundamental theories of integrated positioning based on UWB and IMU. Details are provided for multilateral positioning theory of UWB, UWB calibration method, IMU error analysis, inertial navigation algorithm, and Kalman filter (KF) theory. Particularly, to mitigate the influence of non-line-of-sight (NLOS) propagation on positioning accuracy, a NLOS mitigation method based on fuzzy theory is presented. Meanwhile, the detailed data fusion processes of loosely coupled and tightly coupled systems are introduced and performance evaluation is conducted using experimental data.
TopIntroduction
Indoor location techniques can roughly divided into three categories, namely self-contained location (SCL), fast networking location (FNL) and all-source location (ASL). SCL technique refers to that position (and velocity and attitude in some scenarios) can be obtained by a single device independently without additional infrastructures. The typical representation of SCL is inertial navigation system (INS). SCL technique is appealing due to simple operation and wide availability, but at the cost of a high requirement of the performance of devices, that leads to either high cost implementation or low precision location performance. FNL technique depends on infrastructures to implement positioning or navigation, such as Bluetooth, Wireless Fidelity (WiFi), ZigBee and Ultra-Wideband (UWB), which universally achieves low positioning accuracy. Although UWB can achieve sub-meter precision based on low cost sensors, the reliability and accuracy will be affected in complex propagation environment. ASL technique realizes positioning and navigation by making use of complementary characteristics of different technologies to achieve a high accuracy at an acceptable implementation cost. ASL has been drawing significant attention in the field of indoor location, especially for affordable high accuracy commercial applications.
UWB refers to any radio signal with an absolute bandwidth larger than 500 MHz or a relative bandwidth larger than 20% (Federal Communications Commission, 2002), which is one of the most promising technologies for indoor location due to the properties of centimeter ranging accuracy in line-of-sight (LOS) scenarios, strong multipath resistance and to some extent penetrability through wall, and hence is usually used for accurate indoor fast networking location1. However, UWB indoor location systems have two main deficiencies. The first is that it is easy to suffer from non-line-of-sight (NLOS) propagation in complex environments, leading to degraded positioning accuracy. The other deficiency is that it is unable to provide precise information about orientation and velocity, although roughly velocity may be calculated using estimated position coordinates. ASL technique shows potential to solve the problems by combining UWB and other complementary technologies, which is currently an active research area of UWB-based indoor location. The goal of this chapter is to investigate issues in the integration of UWB and INS as well as to discuss a new NLOS mitigation method for mobile tracking. Firstly fundamentals of UWB and INS systems are described, including the basic positioning principle of each system, and the system error analysis and mitigation. Then the chapter introduces a few machine learning based NLOS mitigation methods, and presents a new NLOS mitigation method based on fuzzy theory. In addition, two schemes for UWB and INS integration are introduced and performance evaluation is performed with experimental data collected in very recent experiments. Finally, the chapter is concluded and future research directions are recommended.