Experimental study on RSS based indoor positioning algorithms

This work compares the performance of indoor positioning systems suitable for low power wireless sensor networks. The research goal is to study positioning techniques that are compatible with real-time positioning in wireless sensor networks, having low-power and low complexity as requirements. Map matching, approximate positioning (weighted centroid) and exact positioning algorithms (least squares) were tested and compared in a small predefined indoor environment. We found that, for our test scenario, weighted centroid algorithms provide better results than map matching. Least squares proved to be completely unreliable when using distances obtained by the one-slope propagation model. Major improvements in the positioning error were found when body influence was removed from the test scenario. The results show that the positioning error can be improved if the body effect in received signal strength is accounted for in the algorithms.Helder D. Silva is supported by the Portuguese Foundation for Science and Technology under the grant SFRBD/78018/2011.info:eu-repo/semantics/publishedVersio

Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling

Doctor of Philosophy

dissertationLocation information of people is valuable for many applications including logistics, healthcare, security and smart facilities. This dissertation focuses on localization of people in wireless sensor networks using radio frequency (RF) signals, speci cally received signal strength (RSS) measurements. A static sensor network can make RSS measurements of the signal from a transmitting badge that a person wears in order to locate the badge. We call this kind of localization method radio device localization. Since the human body causes RSS changes between pairwise sensor nodes of a static network, we can also use RSS measurements from pairwise nodes of a network to locate people, even if they are not carrying any radio device. We call this device-free localization (DFL). The rst contribution of this dissertation is to radio device localization. The human body has a major e ect on the antenna gain pattern of the transmitting badge that the person is wearing, however, existing r

Doctor of Philosophy

dissertationIn wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. Using a large number of multi-hop network measurements in an ensemble of indoor and outdoor environments, we show statistically significant correlations among shadowing experienced on different links in the network. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Second, we study a special class of algorithms, called kernel-based localization algorithms, that use kernel methods as a tool for learning correlation between the RSS measurements. Kernel methods simplify RSS-based localization algorithms by providing a means to learn the complicated relationship between RSS measurements and position. We present a common mathematical framework for kernel-based localization algorithms to study and compare the performance of four different kernel-based localization algorithms from the literature. We show via simulations and an extensive measurement data set that kernel-based localization algorithms can perform better than model-based algorithms. Results show that kernel methods can achieve an RMSE up to 55% lower than a model-based algorithm. Finally, we propose a novel distance estimator for estimating the distance between two nodes a and b using indirect link measurements, which are the measurements made between a and k, for k ? b and b and k, for k ? a. Traditionally, distance estimators use only direct link measurement, which is the pairwise measurement between the nodes a and b. The results show that the estimator that uses indirect link measurements enables better distance estimation than the estimator that uses direct link measurements

An unsupervised learning technique to optimize radio maps for indoor localization

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m(2), resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data

Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use