Showing papers by "Mohamed Sahmoudi published in 2017"

Efficient and Innovative Techniques for Collective Acquisition of Weak GNSS Signals

Abstract: Navigation and positioning in harsh environments is still a great challenge for many applications. Collective Detection (CD) is a powerful approach for acquiring highly attenuated satellite signals in challenging environments, because of its capacity to process all visible satellites collectively taking advantage of the spatial correlation between GNSS signals as a vector acquisition scheme. CD combines the correlator outputs of satellite channels and projects them onto the position/clock bias domain in order to enhance the overall GNSS signal detection probability. In CD, the code phase search for all satellites in view is mapped into a receiver position/clock bias grid and the satellite signals are not acquired individually but collectively. In this concept, a priori knowledge of satellite ephemeris and reference location are provided to the user. Furthermore, CD addresses some of the inherent drawbacks of the conventional acquisition at the expenses of an increased computational cost. CD techniques are computationally intensive because of the significant number of candidate points in the position-time domain. The aim of this paper is to describe the operation of the CD approach incorporating new methods and architectures to address both the complexity and sensitivity problems. The first method consists of hybridizing the collective detection approach with some correlation techniques and coupling it with a better technique for Doppler frequency estimate. For that, a new scheme with less calculation load is proposed in order to accelerate the detection and location process. Then, high sensitivity acquisition techniques using long coherent integration and non-coherent integration are used in order to improve the performance of the CD algorithm.

3D-mapping-aided gnss localization for integrity monitoring in urban environments

Abstract: In order to foster the development of Global Navigation Satellite Systems (GNSS) for land navigation services, there is a pressing need for providing a trust level of the localization solution, especially for liability critical applications. In view of such need, integrity monitoring aims to compute protection levels that successfully bounds positioning errors in nominal conditions. Conventional Receiver Autonomous Integrity Monitoring (RAIM) algorithms have been widely used for integrity monitoring especially for aircraft navigation. Conventional RAIM starts out by supposing a Gaussian measurement errors model with known means and variances. However, this assumption does not hold in dense urban environment. Therefore, we propose novel algorithms that dispenses with this classical assumption. We use external information provided by a 3D GNSS simulation to characterize GNSS errors in urban areas. We propose two approaches for protection levels computation based on comparison between predicted 3D pseudo-ranges bias and computed thresholds. These approaches give a new vision on integrity control based on indicators on the pseudo-ranges bias. Experimental results show that proposed algorithms give an acceptable success rate on integrity monitoring in a harsh areas.

Approximate Maximum Likelihood Estimation Using a 3D GNSS Simulator for Positioning in MP/NLOS Conditions

Abstract: Recent trends in Global Navigation Satellite System (GNSS) applications in urban environments have led to a proliferation of studies in this field that seek to mitigate the adverse effect of non-line-of-sight (NLOS) phenomena. However, these methods reduce the availability of positioning in deep urban conditions. For such harsh urban settings, this paper proposes a methodology of constructive use of NLOS signals, instead of their elimination. We propose to compensate for the NLOS errors using a 3D GNSS simulator to predict the measurements bias and integrate them as observations in the estimation method. We investigate a novel GNSS positioning technique based on measurement similarity scoring of an array of position candidates. We improve this technique using an estimation of the uncertainty on the bias prediction by 3D modeling. Experiment results using real GNSS data in a deep urban environment confirm the theoretical sub-optimal efficiency of the proposed approach, despite it intensive computational load.

Regularized estimation for GNSS positioning in multipath/non-line-of-sight environments

Abstract: Considered as the free accessible and suitable solution for positioning in urban areas, Global Navigation Satellite Systems (GNSS) have been widely used these recent years in a wide spectrum of applications. However, signal blockage, non-line-of-sight (NLOS) multipath interferences and signal degradation affect the system performance and represent the major hurdles of GNSS in it course of adoption as a main localization technology in urban environments. Many approaches have been employed to constructively use these degraded signals in order to reduce positioning errors. Following this vision, we propose in this paper a joint estimation method of the position and the bias for measurement correction. This formulation leads to an ill-conditioned estimation problem. In this work, we apply a regularized robust estimation framework to this problem of NLOS mitigation for GNSS positioning in harsh areas. We derive the optimal regularization matrix by minimizing the total Mean Square Errors (MSE) of the considered model. The performance of the proposed method is assessed using real GNSS data collected in a dense urban area in Toulouse City, showing improvements in comparison to some existing methods.