Sample-based Probabilistic Estimation for Indoor Positioning and Tracking Under Ranging Uncertainty

by Yuan Yang

Institution: Freie Universität Berlin
Department: FB Mathematik und Informatik
Degree: PhD
Year: 2015
Record ID: 1100344
Full text PDF: http://edocs.fu-berlin.de/diss/receive/FUDISS_thesis_000000098947


The critical problem in range-based indoor positioning is the severe ranging uncertainty, which typically resorts to the probabilistic perspective. Since there is no analytical solution to the nonlinear and non-Gaussian positioning problem, the research trends have moved towards exploring sample-based approximations in the probabilistic frame. However, the sample-based methods are generally inaccessible for application not only because of the high complexity, but also for the sampling difficulty and divergence. This thesis studies the sample-based probabilistic positioning to achieve the trade in performance (accuracy and robustness), cost (time and space complexity), and usability (in terms of the required number of samples and implementation difficulty). The work of this thesis covers both the theoretical and practical sides of sample-based probabilistic algorithms positioning. The proposed algorithms are tested by extensive simulations and real-world experiments on the Nanotron platform. Specifically, the contributions of this thesis can be summarized in the following: (a) Chapter 3.3 characterizes the indoor TOF ranging, i.e., the relationship of the ranging error to time, space, anchor connectivity and the ground truth of the ranging, etc. Then, intensive distribution fitting, hypothesis test, and model verification of different parametric models are carried out. (b) Via the imposition of the state constraints from the most recent observation, four constrained sampling methods are developed (see Chapter 4), with the advantages, including: 1) effectively reduces the sample size; 2) suppress sample degeneracy and impoverishment without resampling; 3) no requirement of keeping all samples’ properties in memory. (c) The NLOS mitigation proposed in Chapter 4 refines the measurement model as a positively biased and right-tail distribution. (d) Both the theoretical and practical anchor deployment are suggested in Chapter 4. (e) To address the estimation instability and sparsity problems, the one time-step smoothing methods are incorporated in the sample-based Bayesian estimation. Das grundlegende Problem von distanzbasierter Indoor-Lokalisierung, ist die hohe Ungenauigkeit der Distanzmessungen. Die weit verbreitetste Strategie mit diesem Problem umzugehen, ist die probalistische Schaetzung. Da es fuer das nicht-lineare und nicht-gaussche Lokalisierungsproblem keine analytische Loesung gibt, besteht ein wachsendes Interesse in der Untersuchung von Sample-basierten Naeherungen. Sample basierte Naeherungen haben jedoch den Nachteil, dass sie Aufgrund ihrer hohen Komplexitaet und des sampling-Aufwandes im Feld kaum anwendbar sind. Daher ist es das Ziel dieser Arbeit Sample-basierte probalistische Lokalisierungsverfahren zu studieren und einen Optimum zwischen Genauigkeit und Robustheit, Kosten (zeitliche und oertliche Komplexitaet) und Anwendbarkeit (im Sinne der benoetigten Sample-Zahl und Implementierungsaufand) zu finden. Der wissenschaftliche Beitrag dieser Arbeit findet sich sowohl auf der anwendungs-…