Mobile terminal geo-location

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Table of contents

1 State of the art: mobile terminal geo-location 
1.1 Introduction
1.2 Indoor geo-location: different measurements and techniques
1.2.1 Range-based techniques
1.2.1.1 Distance-based techniques
1.2.1.1.a Time Of Arrival (TOA)
1.2.1.1.b Time Difference Of Arrival (TDOA)
1.2.1.1.c Received Signal Strength (RSS)
1.2.1.1.d Round-Trip Time (RTT)
1.2.1.1.e Received Signal Phase (RSP)
1.2.1.2 Angle-based techniques: Angle Of Arrival (AOA)
1.2.2 Range-free techniques
1.2.2.1 Associated Cell (CellId)
1.2.2.2 Location patterning techniques
1.2.2.2.a Probabilistic methods
1.2.2.2.b K-nearest neighbors (KNN)
1.2.2.2.c Artificial neural networks (ANN)
1.2.2.2.d Support vector machine (SVM)
1.2.2.2.e Smallest M-vertex polygon (SMP)
1.3 Tracking systems and prediction filters
1.3.1 Tracking systems
1.3.2 Prediction filters
1.3.2.1 Kalman filter
1.3.2.1.a Kalman filter modeling
1.3.2.2 Particle filter
1.3.2.2.a Particle filter modeling
1.4 Location systems: architectures and requirements
1.5 Conclusion
2 Our indoor location based on TOA and AOA using coordinates clustering 
2.1 Introduction
2.2 Clustering and problem formulation
2.2.1 Cluster analysis
2.2.2 Measurements choice
2.2.3 Problem formulation
2.3 Proposed method
2.3.1 Two dimensional environment case
2.3.2 Extension to three dimensional environment case
2.4 Experimental results and discussion
2.4.1 Case study
2.4.2 Two dimensional case
2.4.3 Thresholds impact
2.4.4 Three dimensional case
2.5 Comparison
2.6 Conclusion
3 A comparison of learning and deterministic range-free techniques for indoor geo-location 
3.1 Introduction
3.2 Artificial neural networks (ANN)
3.2.1 Definition
3.2.2 Neural network topologies
3.2.2.1 Feedforward neural networks
3.2.2.2 Recurrent neural networks
3.2.2.3 Hybrid neural networks
3.2.3 Neural network training (learning)
3.2.3.1 Supervised learning
3.2.3.2 Unsupervised (adaptive) learning
3.2.3.3 Reinforcement learning
3.2.4 Neural network applications
3.3 Proposed ANN approach for indoor location
3.3.1 Case study and fingerprint collection
3.3.1.1 Case study
3.3.1.2 Fingerprint collection
3.3.2 ANN-based proposed algorithm for indoor location
3.3.3 ANN experimental results
3.3.3.1 Impact of hidden layers number
3.3.3.2 Impact of heterogeneous fingerprints
3.3.3.3 Impact of fingerprint database resolution
3.4 K-nearest neighbor (KNN)
3.4.1 Proposed KNN-based algorithm for indoor location
3.4.2 KNN-based experimental results
3.4.2.1 Impact of nearest neighbor number K
3.4.2.2 Impact of the chosen metric: -nearest neighbor (-NN)
3.4.2.3 Impact of heterogeneous fingerprints
3.4.2.4 Impact of fingerprint database resolution
3.5 ANN vs KNN: comparison and discussion
3.6 Conclusion
4 Mobile tracking based on fractional integration
4.1 Introduction
4.2 Digital fractional integration: characteristics and applications
4.2.1 Fractional integration
4.2.2 Properties of the fractionally integrated trajectory
4.2.2.1 Regularity
4.2.2.2 Statistical analysis of the fractionally integrated path
4.2.2.2.a Average value of the differentiated function
4.2.2.2.b Autocorrelation
4.2.3 The short-memory principle
4.3 Our proposed method
4.4 Results and discussion
4.4.1 Enhancement of the path prediction using DFI
4.4.2 On the decrease of archive size
4.4.2.1 The linear predictor (LP) case
4.4.2.2 The Kalman filter case
4.4.3 The short-memory principle
4.5 Conclusion
Conclusion and perspectives