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Scalar and vector tracking algorithms with fault detection and exclusion for GNSS receivers : design and performance evaluation

Abstract : Navigation with Global Navigation Satellite Systems (GNSS) is a real challenge in harsh environments (suburban, urban, heavy foliage) due to multipath and signal blockage. This thesis proposes a number of GNSS receiver architectural and algorithmic solutions to deal with this challenge. These solutions aim at exploiting the strengths of scalar and vector tracking while minimizing their weaknesses and at utilizing the efficiency of some nonlinear Bayesian filtering techniques in addressing the nonlinearities and non-Gaussianities associated with the navigation and vector tracking problem. Attention is given to some Bayesian estimators that approximate the posterior distribution without linearizing the filtering model, namely the unscented Kalman and particle filtering methods, as well as to the extended Kalman filter, whose posterior estimation is grounded on linearization of the filtering model.First, a brief literature review that presents the fundamentals of GNSS and GNSS receivers together with the applied navigation and tracking algorithms is provided. Then an investigation of the GNSS receiver operation in multipath environments is performed. The thesis proposes models for characterizing multipath induced tracking errors in a vector tracking loop. These models make it possible to express the tracking errors with respect to multipath delay, multipath phase and multipath fading frequency. By exploiting the fact that multipath presence is mirrored on the Early-minus-Late correlator output, novel multipath detectors are devised. A correlator-based non-line-of-sight detector is designed as well. Attention is then directed towards the design of robust tracking and positioning GNSS receiver architectures that incorporate the proposed detectors among other signal quality indicators. A vector tracking scheme capable of detecting and excluding unhealthy measurements from position-velocity-time calculation in the navigator using correlator-based signal quality indicators is suggested. Two other novel tracking schemes, the adaptive scalar-vector tracking loop and the conjoint scalar-vector tracking loop, with the same fault detection and exclusion capability, are formulated. They benefit from vector tracking robustness in harsh environments and scalar tracking positioning accuracy in open sky environments. Experimental results show that the proposed solutions have better tracking and positioning performance than the usual scalar and vector tracking loops. Finally, the thesis presents a number of nonlinear Bayesian filtering approaches to solve the navigation and vector tracking problem. Iterative and adaptive strategies as applied to the unscented Kalman filter are studied. A novel unscented particle filter approach, the iterated adaptive unscented particle filter (IAUPF), is proposed. This approach exploits the convergence properties of iterative methods, the divergence suppression benefits of adaptive filters and the synergy of unscented Kalman and particle filtering approaches. Monte-Carlo simulations conducted with a posterior Cramér-Rao lower bound used as benchmarking reference as well as experimental results demonstrate that the IAUPF outperforms the other Bayesian estimators that are presented
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Elie Amani. Scalar and vector tracking algorithms with fault detection and exclusion for GNSS receivers : design and performance evaluation. Signal and Image processing. Université Paris-Est, 2017. English. ⟨NNT : 2017PESC1225⟩. ⟨tel-01981372⟩

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