Low cost, compact attitude heading reference systems (AHRS) are now being used to track human body movements in indoor environments by estimation of the 3D orientation of body segments. In many of these systems, heading estimation is achieved by monitoring the strength of the Earth's magnetic field. However, the Earth's magnetic field can be locally distorted due to the proximity of ferrous and/or magnetic objects. Herein, we propose a novel method for accurate 3D orientation estimation using an AHRS, comprised of an accelerometer, gyroscope and magnetometer, under conditions of magnetic field distortion. The system performs online detection and compensation for magnetic disturbances, due to, for example, the presence of ferrous objects. The magnetic distortions are detected by exploiting variations in magnetic dip angle, relative to the gravity vector, and in magnetic strength. We investigate and show the advantages of using both magnetic strength and magnetic dip angle for detecting the presence of magnetic distortions. The correction method is based on a particle filter, which performs the correction using an adaptive cost function and by adapting the variance during particle resampling, so as to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetometer readings. The proposed method was tested in an indoor environment in the presence of various magnetic distortions and under various accelerations (up to 3 g). In the experiments, the proposed algorithm achieves <2° static peak-to-peak error and <5° dynamic peak-to-peak error, significantly outperforming previous methods.

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Estimating the position of mobile devices with high accuracy in indoor environments is of interest across a wide range of applications. Many methods and technologies have been proposed to solve the problem but, to date, there is no "silver bullet". This paper surveys research conducted on indoor positioning using time-based approaches in conjunction with the IEEE 802.11 Wireless Local Area Network standard (WiFi). Location solutions using this approach are particularly attractive due to the wide deployment of WiFi and because prior mapping is not needed. This paper provides an overview of the IEEE 802.11 standards and summarizes the key research challenges in 802.11 time-based positioning. The paper categorizes and describes the many proposals published to date, evaluating their implementation complexity and positioning accuracy. Finally, the paper summarizes the state-of-the-art and makes suggestions for future research directions.

Publication information IEEE Journal of Selected Topics in Signal ProcessingAbstract-This paper presents an indoor broadband ultrasonic system for estimation of a mobile device's 3-D location and threeaxis orientation using beacons. It presents the first implementation and characterization of a real frequency hopping spread spectrum-based ultrasonic positioning system, a novel application of uniform circular array angle of arrival estimation techniques to indoor location-orientation estimation and a novel hybrid algorithm for location-orientation estimation. The performance of the system was assessed experimentally. The system has been shown to provide better accuracy, robustness to noise and multipath than other previously reported indoor ultrasonic location or orientation estimation systems with comparable range in the typical office environment tested. The prototype provided location estimates with an error of less than 1.5 cm and and error of less than 4.5 in the yaw, 3 in the pitch and 3.5 in the roll, in 95% of cases.

Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the e ciency of utilization of three of these resources, namely power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve di↵erent compression approaches. It is high time that these various individual e↵orts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.

The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.

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