A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, or digital compass, which enable the measurement of the step length and the heading direction. Therefore, the use of zero velocity updates is necessary to minimize the inertial drift accumulation of the sensors. The aim of this paper is to develop a foot-mounted pedestrian dead reckoning system based on an inertial measurement unit and a permanent magnet. Our approach enables the stance phase and the step duration detection based on the measurements of the permanent magnet field during each gait cycle. The proposed system involves several parts: inertial state estimation, stance phase detection, altitude measurement, and error state Kalman Filter with zero velocity update and altitude measurement update. Real indoor experiments demonstrate that the proposed algorithm is capable of estimating the trajectory accurately with low estimation error.

Event detection in wireless sensor networks is a sophisticated method for processing sampled data directly on the sensor nodes, thereby reducing the need for multihop communication with the base station of the network. In contrast to application-agnostic compression or aggregation techniques, event detection pushes application-level knowledge into the network. In-network event detection - especially the distributed form involving multiple sensor nodes - has thus an exceptional potential to increase energy efficiency, thus prolonging the lifetime of the network. In this article, we summarize recently proposed system architectures and algorithms employed for event detection in wireless sensor networks. On the example of the AVS-Extrem platform, we illustrate how energy-efficient event detection can be implemented through a combination of custom hardware design and distributed event detection algorithms. We then continue to present a brief evaluation of the detection accuracy and the energy consumption that is achievable by current systems.

Decentralized magnetic indoor localization is a sophisticated method for processing sampled magnetic data directly on a mobile station (MS), thereby decreasing or even avoiding the need for communication with the base station. In contrast to central-oriented positioning systems, which transmit raw data to a base station, decentralized indoor localization pushes application-level knowledge into the MS. A decentralized position solution has thus a strong feasibility to increase energy efficiency and to prolong the lifetime of the MS. In this article, we present a complete architecture and an implementation for a decentralized positioning system. Furthermore, we introduce a technique for the synchronization of the observed magnetic field on the MS with the artificially-generated magnetic field from the coils. Based on real-time clocks (RTCs) and a preemptive operating system, this method allows a stand-alone control of the coils and a proper assignment of the measured magnetic fields on the MS. A stand-alone control and synchronization of the coils and the MS have an exceptional potential to implement a positioning system without the need for wired or wireless communication and enable a deployment of applications for rescue scenarios, like localization of miners or firefighters.

As a step towards ubiquitous and mobile computing, a decentralized localization platform allows positioning for objects and persons. The decentralized computation of the position enables to shift the application-level knowledge into a Mobile Station (MS) and avoids the communication with a remote device such as a server. In addition, computing a position on resource-constrained devices is challenging due to the restricted storage, computing capacity and power supply. Therefore, we propose suitable algorithms to compute unoptimized as well as optimized positions on resource-limited MSs. Algorithms for unoptimized positions will be analyzed with respect to the stability, complexity, and memory requirements. The calculated positions are optimized by using the Gauss--Newton (GNM) or Levenberg--Marquardt methods (LVMs). We analyze and compare the GNM with two variants of the LVM algorithm. Furthermore, we develop an adaptive algorithm for the position optimization, which is based on the Singular Value Decomposition (SVD), LVM algorithm, and the Dilution of Precision. This method allows an adaptive selection mechanism for the LVM algorithm. The influence and choice of the right parameter combination of the LVM algorithm will be analyzed and discussed. Finally, we design and evaluate a method to reduce multipath errors on the MS.

In recent years, a variety of real-time applications benefit from services provided by localization systems due to the advent of sensing and communication technologies. Since the Global Navigation Satellite System (GNSS) enables localization only outside buildings, applications for indoor positioning and navigation use alternative technologies. Ultra Wide Band Signals (UWB), Wireless Local Area Network (WLAN), ultrasonic or infrared are common examples. However, these technologies suffer from fading and multipath effects caused by objects and materials in the building. In contrast, magnetic fields are able to pass through obstacles without significant propagation errors, i.e. in Non-Line of Sight Scenarios (NLoS). The aim of this work is to propose a novel indoor positioning system based on artificially generated magnetic fields in combination with Inertial Measurement Units (IMUs). In order to reach a better coverage, multiple coils are used as reference points. A basic algorithm for three-dimensional applications is demonstrated as well as evaluated in this article. The established system is then realized by a sensor fusion principle as well as a kinematic motion model on the basis of a Kalman filter. Furthermore, a pressure sensor is used in combination with an adaptive filtering method to reliably estimate the platform’s altitude.

A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station and avoids the communication with a central unit such as a server or a base station. In addition, the calculation of the position on low-cost and resource-constrained devices presents a challenge due to the limited computing, storage capacity, as well as power supply. Therefore, we propose a platform architecture that enables the design of a system with the reusability of the components, extensibility (e.g., with other positioning technologies) and interoperability. Furthermore, the position is computed on a low-cost device such as a microcontroller, which simultaneously performs additional tasks such as data collecting or preprocessing based on an operating system. The platform architecture is designed, implemented and evaluated on the basis of two positioning systems: a field strength system and a time of arrival-based positioning system.