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This paper focuses on the design of a robust Real Time Locating Systems (RTLS) based on the Ultra-Wide Band (UWB) technology for Augmented Reality (AR) applications in TV studios that require artists and/or a presenter to be accurately localized. According to a UWB-based measurement campaign, carried out in a TV studio environment, ranging measurements are heavily affected by the human body interference. Indeed, lots of outliers are present as the UWB receiver may synchronize to reflected paths, which result to be much stronger than the direct one. As a consequence, range errors are very large. In this context, to improve the localization performance, we increased the redundancy of the RTLS by employing more than one tag to localize the artists on the TV scene. In particular, we have applied the Extended Kalman Filter (EKF) algorithm to work with two and three tags. Moreover, an outlier detection and correction procedure have been defined and adopted for the ranging phase. The resulting localization performance, based on real range measurements, shows that the EKF with two tags outperforms by 83.5 % the one with single tag.

We survey the state-of-the-art on the Internet-of-Things (IoT) from a wireless communications point of view, as a result of the European FP7 project BUTLER which has its focus on pervasiveness, context-awareness and security for IoT. In particular, we describe the efforts to develop so-called (wireless) enabling technologies, aimed at circumventing the many challenges involved in extending the current set of domains (“verticals”) of IoT applications towards a “horizontal” (i.e. integrated) vision of the IoT. We start by illustrating current research effort in machine-to-machine (M2M), which is mainly focused on vertical domains, and we discuss some of them in details, depicting then the necessary horizontal vision for the future intelligent daily routine (“Smart Life”). We then describe the technical features of the most relevant heterogeneous communications technologies on which the IoT relies, under the light of the on-going M2M service layer standardization. Finally we identify and present the key aspects, within three major cross-vertical categories, under which M2M technologies can function as enablers for the horizontal vision of the IoT.

Dense urban areas are particularly challenging environments for global navigation satellite systems (GNSS) because the satellite visibility is heavily reduced due to the presence of the so called urban canyons, composed of a series of tall buildings and narrow streets. In order to improve GNSS performance in these environments, hybrid (GNSS/terrestrial) and cooperative positioning algorithms that combine pseudorange measurements from visible satellites and terrestrial range measurements from neighboring terrestrial receivers have been recently proposed in the literature. Mainly, these algorithms have been tested in static or pedestrian mobility scenarios. This paper, first introduces a robust procedure that selects the most reliable neighbors for cooperation and then analyzes the performance of the optimized hybrid and cooperative positioning algorithms in a typical urban canyon scenario for vehicular navigations. Simulation results demonstrate that the positioning algorithms can accurately track the movement of vehicles in urban canyons even without fixed terrestrial infrastructure. Moreover, an accurate complexity analysis of the different hybrid algorithms is presented to understand their suitability for a real implementation.

This paper presents a novel cognitive and cooperative tracking (CCT) approach based on extended Kalman filter (EKF) to localize mobile nodes in wireless networks. The proposed algorithm shows three important features: energy efficient, cognitive and cooperative. More specifically, the tracking algorithm adaptively adjusts the transmission power to optimize the energy consumption while meeting the required localization accuracy Pa imposed by a generic application. Moreover, it adopts a self-learning scheme to track the time-variant environment's characteristics (e.g., range measurement noise) and use this knowledge to improve tracking performance. Finally, the algorithm exploits the cooperation among unknown nodes that leads to further improved performance and reduced power consumption. Simulation results show that the proposed CCT approach is able to improve positioning performance and meet the required accuracy Pa while energy consumption is optimized.

This work analyzes the impact of medium access control (MAC) layer on the performance of real-time cooperative positioning in ad-hoc mobile wireless networks. The probability mass function (pmf) of the latency introduced by the MAC layer is here derived to assess the influence of MAC parameters on the latency and then operate on those to improve the positioning performance while reducing the number of position estimation iterations.

This paper presents a novel ‘Energy Efficient Tracking’ (EET) algorithm that tries to meet the localization accuracy requirement, P a , imposed by a generic location-based application while the energy consumption for ranging and communication is optimized. More specifically, given the set of range measurements performed by a mobile node with respect to its neighboring anchors (i.e., nodes whose exact positions are known a priori), the proposed EET algorithm selects, on the basis of the Cramer-Rao lower bound (CRLB), the closest set of anchors such that the resulting positioning error is lower than the required P a . At the same time, the EET approach minimizes the average energy consumption by dynamically adapting the transmitted power, P t , to be used in the next positioning estimation step. Based on time-of-arrival (TOA) estimation with ultra-wide bandwidth (UWB) nodes, the EET approach uses an Extended Kalman Filter (EKF) to track the mobile node's position. Simulation results show that the EET algorithm effectively reduces the energy consumption while the achieved positioning performance is almost identical to the one achieved by a classical EKF with fixed P t , named ‘Energy Fixed Tracking’ (EFT) algorithm.

We propose a novel hybrid GNSS-terrestrial localization algorithm based on particle filter that fuses ranging data from both satellites and terrestrial receivers. The proposed positioning approach, named hybrid-cooperative particle filter (HCPF), is fully distributed and allows both increased positioning availability and accuracy compared to GNSS-only localization in challenged scenarios. Moreover, simulation results based on a realistic indoor scenario show that the proposed solution outperforms several state of the art algorithms such as unscented Kalman filter and an approach based on belief propagation.

In this paper we propose a novel hybrid and cooperative positioning approach based on extended Kalman filter (EKF) to localize mobile targets in indoors. The algorithm fuses both received signal strength (RSS) measurements performed by nodes of a wireless sensor network (WSN) and proximity information from radio frequency identification (RFID) devices. Simulation results prove that the proposed cooperative approach outperforms the non-cooperative version of the algorithm.

We propose a simulation tool able to test hybrid GNSS-terrestrial and cooperative positioning algorithms that fuse both pseudorange measurements from satellites and range estimates between terrestrial receivers. In particular, the tool simulates devices belonging to a peer-to-peer (P2P) network where peers cooperate by exchanging data in order to improve both positioning accuracy and availability. More specifically, the simulation tool generates range and pseudorange measurements by employing realistic propagation models and GPS orbits. Furthermore, the simulation tool allows the user to test different algorithms based on Bayesian and least squares methods in two realistic GNSS challenged scenarios namely indoor and urban canyon. Finally, the simulator take into account communication and mobility aspects allowing the user to evaluate their impact on the final positioning accuracy.

Nowadays indoor positioning applications have received great attentions among short range wireless communication systems. In particular, Wireless Sensor Network (WSN) is the most widely used technology for indoor localization as it is cheap and easy to deploy. However, location accuracy is limited by the harsh propagation of the Radio Frequency (RF) signals in indoor environments, for instance, caused by the presence of obstacles between wireless nodes, multi-paths, interference, etc. To overcome these effects and improve positioning accuracy, Radio Frequency Identification (RFID) technology is employed to assist the WSN based positioning. This paper proposes two hybrid positioning approaches based both on WSN and RFID. One solution uses an Extended Kalman Filter (EKF) algorithm while the other one uses a Particle Filter (PF) approach. Both methods are compared through Matlab simulations which demonstrate that in general the WSN/RFID hybrid approach is feasible and provides good positioning estimation capabilities even under harsh indoor conditions.

Cooperative localization algorithms have been recently introduced to overcome the limitations of systems relying on GPS or other terrestrial infrastructure. The novel hybrid cooperative unscented Kalman filter (hcUKF) approach, which fuses ranging data from both satellites and terrestrial receivers, allows increased availability, robustness and accuracy compared to GNSS-only localization in challenged scenarios. Simulation results show that the proposed solution outperforms traditional algorithms such as extended Kalman filter.

This paper presents a wireless local area network (WLAN)-based real time system for indoors and outdoors ve- hicle localization. The proposed solution uses a neural network trained with a map of received power fingerprints from WLAN Access Points (APs) surrounding the vehicle. The practical implementation of the system is described and results from an outdoor experimental testbed are analyzed to address system performance in terms of calibration, estimation complexity, and location accuracy. Pre- and post-processing approaches aimed at improving system accuracy are also discussed. Index Terms—positioning, neural network, fingerprint, real time locating system, wireless local area network ∗

This paper proposes a complete system for nodes localization in a Wireless Sensor Network (WSN) based on the ZigBee standard. The system includes a real-time location engine, which adopts a Received Signal Strength Indicator (RSSI)-based localization algorithm, and three tools, namely an Environment Description Tool (EDT), a Channel Modeling Tool (CMT) and a Network Planning Tool (NPT), which enable efficient deployment and accurate operation. Experimental evaluations show how the system performs in a real environment and how the proposed approach improves localization accuracy.

Moore at al. presented in [1] a cluster-based distributed algorithm for localizing wireless sensor network (WSN) nodes in presence of range measurement noise. They introduced the notion of "robust quadrilaterals" as a means of avoiding flip ambiguities that often corrupt the results of localization algorithm. This paper1 proposes an enhancement of the algorithm presented in [1] that uses a new quadrilateral robustness test. A second novel distributed algorithm for anchor-less localization of WSN nodes in presence of range measurements noise is presented. This second algorithm reduces the total computational complexity with respect to the first two algorithms by a factor at least equal to the number of nodes in the WSN. It is made of two main steps: the first one, where a single cluster of nodes is localized, and a second one where the other unknown nodes are estimated by successive trilaterations. The original algorithm [1] and the two novel algorithms proposed in this paper are compared by means of simulations in terms of complexity, convergence and location accuracy performance.