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This chapter presents and discusses the fundamentals of ultra‐wideband (UWB) technology, the main issues and the methods of obtaining high‐resolution ranging and localization. It illustrates some results related to the passive localization of energy autonomous tags using the UWB backscatter principle. The chapter also illustrates an experimental case study where UWB technology has been used to track a mobile node in a typical indoor environment and tracking experimental results using a Bayesian filtering approach, with a PF implementation. It further exploits prior knowledge from the mobility and perception models. After the FCC allowed the use of UWB signals in the USA, considerable amount of effort was put into the development and standardization of UWB systems. This effort resulted in the publication of the IEEE 802.15.4a standard in 2007 (IEE 2007) as a physical layer (PHY) alternative based on UWB to the IEEE 802.15.4 standard.

Cooperative mobile positioning is becoming increasingly important as a promising new branch of wireless location in which several research directions are being explored (e.g., positioning, tracking, data fusion and clustering). This chapter first discusses the framework of cooperative mobile positioning framework and then introduces the COMET as an innovative solution for position determination in fourth generation (4G) wireless networks. It also presents a brief survey of cooperative localization in robot networks and wireless sensor networks (WSNs), and examines the cooperative mobile positioning approach and its overall benefits. The chapter further illustrates the data fusion and filtering techniques, which have been adapted to cope with the cooperative framework. Finally, it describes COMET, an example of a cooperative augmentation system (CAS) in a cellular network, with its system architecture and its data fusion methods; and explores its performance, which has been evaluated via computer simulations.

This chapter begins by presenting the Location Stack, a framework created by Jeffrey Hightower, which offers an overview of an exemplary localization system with all its elements and interrelations, similar to the seven‐layer ISO/OSI model used in the field of communication systems. It introduces the first application area of positioning and thereby the formal definitions of a location‐based service (LBS), which is basically considered as the intersection of new information and communication technologies (NICTs), the Internet and geographic information systems (GISs). The chapter illustrates the "LBS ecosystem", which is an efficient parallel taken from biology, to show the many actors rotating around this business; in particular, these actors will be split into operational and non‐operational actors. It then presents taxonomies of LBSs, which are divided into the service model, application, market segment and content. The chapter focuses on the second of these classes and explains some application categories within LBSs.

This chapter presents two major approaches of data fusion: the least squares (LS) approach and the Bayesian framework. The LS approach is an algorithm that estimates the position of a mobile station (MS) by minimizing the squared error between the actual measurements observed in the wireless channel and the expected measurements resulting from the estimated position. In the Bayesian framework, the position is determined as an estimator that minimizes the mean square error between the actual measurements and the expected measurements. The chapter also provides three major implementations of the Bayesian framework: the Kalman filter (KF), the particle filter (PF) and grid‐based methods. The KF is a group of methods that assumes the measurements to be corrupted by white Gaussian noise, the PF is a Monte Carlo type of algorithm that does not constrain the noise component of the Gaussian distribution, and the grid‐based methods assume that the state space is discrete and finite.