The Adaptive Multi-Resolution Frequency-Domain ParFlow (MR-FDPF) Method for Indoor Radio Wave Propagation Simulation. Part I : Theory and Algorithms

This report presents the theoretical background and new developments of the multi-resolution frequency domain ParFlow (MR-FDPF) approach for the calculus or radio propagation in Indoor environments for centimetric waves. This method has been developed to face the need of a best understanding of Indoor propagation and to help the WiFi network planning task. Indeed, the development of a wireless design tool is based firstly on a radio propagation engine to predict accurately the radio coverage of access points, with a limited computational load. Usual approaches in the literature are based on either empiric modeling, deducted from measurements, or geometrical optic formalism leading to ray-tracing. While the former suffers a lake of accuracy, the later needs a trade-off between accuracy and computational load, often difficult to assess. The approach proposed herein is based on a finite element approach. Once the problem developed in the frequency domain, the linear system thus obtained is solved in two steps: a pre-processing step which consists in an adaptive multi-resolution (multi-grid) pre-conditioning and a propagation step. The second step computes the coverage of a point source with an up-and-down propagation through the binary tree associated with the multi-resolution description. This approach solves exactly the linear system but with a strongly reduced computational complexity when compared to the time domain approach. For example, a full AP coverage at a macroscopic resolution and for an environment of 1000x600 pixels (i.e. $6000m2$ at a $10cm$ resolution) lasts less than $200ms$

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions