Position Estimation for IR-UWB Systems

IR-UWB has emerged as a promising candidate for positioning passive nodes in wireless networks due to its extremely short time domain transmitted pulses. The two-step approaches in which first different TOAs are estimated and then fed into a triangulation procedure are suboptimal in general. This is because in the first stage of these methods, the measurements at distinct anchors are independent and ignore the constraint that all measurements must be consistent with a single emitter location. In this chapter, the authors investigate two techniques to overcome this issue. First, a two-step procedure based on multi-TOA estimation is proposed. Second, a positioning approach omitting the intermediate known as DPE is presented. Complementarily, the authors explore the CS-based modeling of both approaches so that the temporal sparsity of the UWB received signal and the consequent sparseness of the discrete spatial domain are exploited to select the most significant TOAs and to reduce the amount of information to be sent to a central fusion unit in the DPE approach.

Robustness in Fingerprinting-Based Indoor Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

Weak GNSS Signal Acquisition for Indoor Environments

High sensitivity and fast acquisition are two important goals that must be considered in the development of signal processing techniques for a GNSS acquisition function to meet the demands for LBS in GNSS-challenged environments, such as indoor and urban canyon. This chapter introduces the fundamentals of GNSS acquisition functions, GNSS acquisition techniques for new GNSS signals, and GNSS acquisition techniques achieving high sensitivity and fast acquisition. Therefore, this chapter contains useful information for engineers who study the fundamentals and principles of GNSS acquisition and the state-of-the-art GNSS signal acquisition techniques for weak signals.

Multi-Sensor Integrated Navigation in Urban and Indoor Environments

The art of multi-sensor processing, or “sensor-fusion,” is the ability to optimally infer state information from multiple noisy streams of data. One major application area where sensor fusion is commonly used is navigation technology. While global navigation satellite systems (GNSS) can provide centimeter-level location accuracy worldwide, they suffer from signal availability problems in dense urban environment and they hardly work indoors. While several alternative backups have been proposed, so far, no single sensor or technology can provide the desirable precise localization in such environments under reasonable costs and affordable infrastructures. Therefore, to navigate through these complex areas, combining sensors is beneficial. Common sensors used to augment/replace GNSS in complex environments include inertial measurement unit (IMU), range sensors, and vision sensors. This chapter discusses the design and implementation of tightly coupled sensor fusion of GNSS, IMU, and light detection and ranging (LiDAR) measurements to navigate in complex urban and indoor environments.

Navigation Based on Sensors in Smartphones

With the increasing ubiquity of smartphones and tablets, users are now routinely carrying a variety of sensors with them wherever they go. These devices are enabling technologies for ubiquitous computing, facilitating continuous updates of a user's context. They have built-in MEMS-based accelerometers for ubiquitous activity monitoring and there is a growing interest in how to use these together with gyroscopes and magnetometers to build dead reckoning (DR) systems for location tracking. Navigation in complex environments is needed mainly by consumer users, private vehicles, and pedestrians. Therefore, the navigation system has to be small, easy to use, and have reasonably low levels of power consumption and price. The technologies and techniques discussed here include the fusion of inertial navigation (IN) and other sensors, positioning based on signals from wireless networks (such as Wi-Fi), image-based methods, cooperative positioning systems, and map matching (MM). The state-of-the-art of MEMS-based location sensors and their integration into modern navigation systems are also presented.

Cooperative Localization in Wireless Networks

This chapter describes cooperative localization in wireless networks, where mobile nodes with unknown positions jointly infer their positions through measuring and exchanging information with each other. The technique of cooperation localization, efficiently even in harsh propagation environment, enables amounts of location-based services that rely on high-accuracy position information of mobile nodes. After a brief introduction of cooperative localization, the Cramer-Rao lower bound is given as a standard metric for performance. Then the information in the temporal and spatial domain is illustrated with geometrical interpretations. Two classes of cooperative localization algorithms, namely, centralized and distributed algorithms, are presented to show the implementation of the cooperative localization in a wireless network. Then the performance of cooperative localization under non-line-of-sight condition is analyzed. Lastly, numerical results are given to illustrate the performance of cooperative localization algorithms.

TDOA-Based Acoustic Direction Finding

This chapter focuses on estimating the azimuth and elevation angles of a sound emitter based on time-difference-of-arrival (TDOA) measurements using an array of acoustic sensors. The TDOA-based direction-finding problem is appropriate because in a range of scenarios the source only emits a transient signal and TDOA measurements provide a simple method of finding the direction of the received signal. Given the measurement of TDOA, three methods for calculating the actual bearing of an acoustic source are considered—algebraic calculations based on trigonometric functions, linear least squares, and nonlinear least squares—and these results are also compared with the Cramer-Rao lower bound (CRLB). In this chapter, a comprehensive analysis of TDOA-based direction-finding methods is presented with regard to different application conditions, while their estimation performances are analysed with both simulation and field experimental results produced by 3-D microphone array.

Received Signal Strength Models for Narrowband Radios

The advancements in wireless communication technologies have enabled new sensing possibilities where the channel measurements of the radio are used for inferring physical changes in the surrounding environment. Relating the channel measurements to the location and actions of people has been of particular interest due to the wide range of application opportunities enabled by such a sensing capability. As an example, the low-amplitude received signal measurements of low-cost wireless communication systems have been used to detect the presence of a person, to locate and track them, identify gestures and activities of the person, and even monitor their vital signs. This chapter aims to give a deep insight on how people influence radio signals, how these effects are observed at the receiver antenna, and how the measurement system impacts the recorded measurements. These topics are presented to shed light on the relation between the location of people and signal strength measurements of narrowband radios.

Integrated Positioning With UWB and INS

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.

GNSS Vulnerability

This chapter examines sources of global navigation satellite system (GNSS) vulnerability, identifying the broad range of topics that comes under this heading, and cites some key references in each category area. GNSS vulnerability has been a very productive area for GNSS researchers in recent years and this chapter sets out to be a comprehensive review of the different ways that the operation of GNSS can be degraded by outside influences, from the high (system) to the low (receiver component) level.