Experimental vital signs estimation using commercially available IR-UWB radar

© 2014 IEEE. This paper studies the feasibility of estimating vital signs exploiting commercially available Impulse Radio Ultra Wideband (IR-UWB) radar. The focus is on extracting breathing and heart beat rates following the consideration of a nonstationary analytical model for the reflected signal from the human body. The Hilbert-Huang Transform (HHT), which is adaptive to nonlinear and nonstationary signals, is proposed and applied to the intrinsic mode functions of the received signal providing frequency information evolving with time and quantifying the amount of variation due to different signal content contribution. Experimental results are presented demonstrating the effectiveness of the proposed technique for determining respiration and heartbeat rates

Ultra-wideband-based multilateration technique for indoor localisation

In this study, the authors present a novel geometrically driven multilateration technique that is based on ultra-wideband (UWB) technology. The authors refer to their proposed solution as time reflection of arrival (TROA). They demonstrate in this study how the position estimation error is improved upon by carefully considering the inherent properties of the UWB technology and the reflection properties of transmitted UWB signals. By a direct comparison between TROA and two widely used multilateration techniques, the authors show that indoor position estimation can be done much more effectively using their proposed solution. They also derive a new Cramér–Rao lower bound for TROA multilateration and use it to show its level of efficiency

GNSS Shadow Matching: The Challenges Ahead

GNSS shadow matching is a new technique that uses 3D mapping to improve positioning accuracy in dense urban areas from tens of meters to within five meters, potentially less. This paper presents the first comprehensive review of shadow matching’s error sources and proposes a program of research and development to take the technology from proof of concept to a robust, reliable and accurate urban positioning product. A summary of the state of the art is also included. Error sources in shadow matching may be divided into six categories: initialization, modelling, propagation, environmental complexity, observation, and algorithm approximations. Performance is also affected by the environmental geometry and it is sometimes necessary to handle solution ambiguity. For each error source, the cause and how it impacts the position solution is explained. Examples are presented, where available, and improvements to the shadow-matching algorithms to mitigate each error are proposed. Methods of accommodating quality control within shadow matching are then proposed, including uncertainty determination, ambiguity detection, and outlier detection. This is followed by a discussion of how shadow matching could be integrated with conventional ranging-based GNSS and other navigation and positioning technologies. This includes a brief review of methods to enhance ranging-based GNSS using 3D mapping. Finally, the practical engineering challenges of shadow matching are assessed, including the system architecture, efficient GNSS signal prediction and the acquisition of 3D mapping data

Occupancy Based Household Energy Disaggregation using Ultra Wideband Radar and Electrical Signature Profiles

Human behaviour and occupancy accounts for a substantial proportion of variation in the energy efficiency pro le of domestic buildings. Yet while people often claim that they would like to reduce their energy bills, rhetoric frequently fails to match action due to the effort involved in understand- ing and changing deeply engrained energy consumption habits. Here, we present and, through dedicated experiments, test in-house developed soft-ware to remotely identify appliance energy usage within buildings, using energy equipment which could be placed at the electricity meter location. Furthermore, we monitor and compare the occupancy of the location under study through Ultra-Wideband (UWB) radar technology and compare the resulting data with those received from the power monitoring software, via time synchronization. These signals when mapped together can potentially provide both occupancy and speci c appliances power consumption, which could enable energy usage segregation on a yet impossible scale as well as usage attributable to occupancy behaviour. Such knowledge forms the basis for the implementation of automated energy saving actions based on a households unique energy profi le

Intelligent GNSS Positioning using 3D Mapping and Context Detection for Better Accuracy in Dense Urban Environments

Conventional GNSS positioning in dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings. Here, we present a full implementation of the intelligent urban positioning (IUP) 3D-mapping-aided (3DMA) GNSS concept. This combines conventional ranging-based GNSS positioning enhanced by 3D mapping with the GNSS shadow-matching technique. Shadow matching determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping. Thus, IUP uses both pseudo-range and signal-to-noise measurements to determine position. All algorithms incorporate terrain-height aiding and use measurements from a single epoch in time. Two different 3DMA ranging algorithms are presented, one based on least-squares estimation and the other based on computing the likelihoods of a grid of candidate position hypotheses. The likelihood-based ranging algorithm uses the same candidate position hypotheses as shadow matching and makes different assumptions about which signals are direct line-of-sight (LOS) and non-line-of-sight (NLOS) at each candidate position. Two different methods for integrating likelihood-based 3DMA ranging with shadow matching are also compared. In the position-domain approach, separate ranging and shadow-matching position solutions are computed, then averaged using direction-dependent weighting. In the hypothesis-domain approach, the candidate position scores from the ranging and shadow matching algorithms are combined prior to extracting a joint position solution. Test data was recorded using a u-blox EVK M8T consumer-grade GNSS receiver and a HTC Nexus 9 tablet at 28 locations across two districts of London. The City of London is a traditional dense urban environment, while Canary Wharf is a modern environment. The Nexus 9 tablet data was recorded using the Android Nougat GNSS receiver interface and is representative of future smartphones. Best results were obtained using the likelihood-based 3DMA ranging algorithm and hypothesis-based integration with shadow matching. With the u-blox receiver, the single-epoch RMS horizontal (i.e., 2D) error across all sites was 4.0 m, compared to 28.2 m for conventional positioning, a factor of 7.1 improvement. Using the Nexus tablet, the intelligent urban positioning RMS error was 7.0 m, compared to 32.7 m for conventional GNSS positioning, a factor of 4.7 improvement. An analysis of processing and data requirements shows that intelligent urban positioning is practical to implement in real-time on a mobile device or a server. Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. Specifically, 3DMA GNSS should be selected when the user is in a dense urban environment, not indoors or in an open environment. Algorithms for detecting indoor and outdoor context using GNSS measurements and a hidden Markov model are described and demonstrated