GNSS Multipath Signal Detection And Estimation Method Research In Dense Obstacle Environments

With the new application requirements for global navigation satellite systems(GNSS)in complex environments,such as in urban canyons,one important remaining challenge is to reduce the impact of multipath(MP)on positioning methods.MP signals are mainly due to the fact that a signal transmitted by a navigation satellite is very likely to be reflected or diffracted and can follow different paths before arriving at the GNSS receiver.An important property of MP signals is that they not only depend on the relative position of the receiver and GNSS satellites,but also on the environment where the receiver is located,especially in urban canyons.Since it is difficult to use a specific propagation model to accurately capture the dynamics of MP signals,it is worth to discuss the property,impact on the GNSS receiver and mitigation approach associated with MP signals.This thesis mainly focus on MP mitigation approaches within GNSS receivers.In general,there are two classes of perturbations affecting the received GNSS signals:(a)MP interference resulting from the sum of the direct signal and of delayed reflections handled by the GNSS receiver(b)Non-line of sight(NLOS)singals which result from a unique reflected signal received and tracked by the GNSS receiver.In order to improve the GNSS-based positioning accuracy,we proposed to exploit the statistical signal processing methods,the expectation-maximization(EM)algorithm and multi-sensor fusion method for reducing the effect MP interference on GNSS receivers.Accordingly,main work of this thesis are following:(1)The impacts of MP signal on the GNSS receiver are demonstrated based on the received baseband signal model associated with GNSS satellites affected by MP signals.In addition,the statistical properties of GNSS measurement noise under the condition of MP interference and NLOS signal are discussed by using data collected from measurement campaign.(2)The problem of MP interference mitigation in the GNSS receiver can be formulated as a joint state(containing the direct signal parameters)and time-varying model parameter(containing the MP signal parameters)estimation.Accordingly,we propose to exploit the EM algorithm for achieving the joint state and time-varying parameter estimation in the context of MP interference mitigation in GNSS receivers.The convergence of the proposed approach is analyzed based on the existing convergence theorem associated with the EM algorithm.Finally,a comprehensive simulation study is conducted to compare the performance of the proposed EM-based MP mitigation approach with other state-of-the-art MP mitigation approaches both in static and realistic scenarios.(3)The effects of NLOS multipath interferences can be modelled as mean value jumps contaminating the GNSS pseudo-range measurements.The marginalized likelihood ratio test(MLRT)is then investigated to detect,identify and estimate the corresponding NLOS multipath biases.However,the MLRT test statistics is difficult to compute.In this work,we consider a Monte Carlo integration technique based on bias magnitude sampling.Jensen's inequality allows this Monte Carlo integration to be simplified.The multiple model algorithm is also used to update the prior information for each bias magnitude sample.Some strategies are designed for estimating and correcting the NLOS multipath biases.In order to demonstrate the performance of the MLRT,experiments allowing several localization methods to be compared are performed.Finally,results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment.(4)Considering that accurate a priori knowledge about the vehicle state can facilitate the detection and consequently the mitigation for MP biases appearing on GNSS pseudo-range and delta range measurements,a monocular vision sensor and a baro-altimeter aided inertial measurement unit(IMU)/GNSS integration architecture is investigated.The proposed integration architecture aims at exploiting the reliable GNSS measurements in urban environments to ensure the required navigation accuracy and reliability.A hierarchical sensor integration scheme is proposed for state estimation.Finally,results from a measurement campaign conducted in urban canyons are presented in order to evaluate the performance of the proposed approach in practice.