On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables’ statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology

Enhanced EKF-based Time Calibration for GNSS/UWB Tight Integration

Tight integration of low-cost Ultra-Wide Band (UWB) ranging sensors with mass-market Global Navigation Satellite System (GNSS) receivers is gaining attention as a high-accuracy positioning strategy for consumer applications dealing with challenging environments. However, due to independent clocks embedded in Commercial-Off-The-Shelf (COTS) chipsets, the time scales associated with sensor measurements are misaligned, leading to inconsistent data fusion. Centralized, recursive filtering architectures can compensate for this offset and achieve accurate state estimation. In line with this, a GNSS/UWB tight integration scheme based on an Extended Kalman Filter (EKF) is developed that performs online time calibration of the sensors' measurements by recursively modeling the GNSS/UWB time-offset as an additional unknown in the system state-space model. Furthermore, a double-update filtering model is proposed that embeds optimizations for the adaptive weighting of UWB measurements. Simulation results show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain of 41.60% over a plain EKF integration with uncalibrated time-offset and of 15.43% over the EKF with naive time-offset calibration. Moreover, a real-world experimental assessment demonstrates improved Root-Mean-Square Error (RMSE) performance of 57.58% and 31.03%, respectively

Analysis of GNSS data at the Moon for the LuGRE project

The Lunar GNSS Receiver Experiment (LuGRE) aims at testing positioning and navigation at the Moon by using Earth Global Navigation Satellite Systems. Within this framework, to support the scientific mission definition and to process on-ground the data that will be collected, a proper GNSS software receiver is needed, implementing advanced signal processing algorithms that enable it to work in the Moon scenario. This paper discusses the issues and potentialities, presenting the preliminary results of the simulation of the Moon environment, as far as the navigation tasks are concerned

Enhanced Bayesian State Space Estimation for a GNSS/INS Tightly-Coupled Integration in Harsh Environment: an Experimental Study

Several navigation filters have been developed since the early steps of Global Navigation Satellite Systems (GNSS) to provide high-accuracy Positioning, Navigation and Timing (PNT), and many solutions are available in the literature to support a plethora of applications. In the context of vehicular navigation and positioning, advanced state estimation and sensors fusion techniques cannot cope by themselves with strong multipath effects in dense urban areas. Therefore, these solutions require more robust approaches typically involving an additional processing effort, especially in low-cost Inertial Navigation System (INS)/GNSS Tightly-Coupled (TC) integration scheme. This work analyzes state-of-the-art covariance matrix estimation methods and proposes an INS-based pre-processing stage to mitigate the impact of undesired, multipath-related bias injections without inhibiting the Inertial Navigation System (INS)/GNSS integration. The proposed adaptive solution improves the overall stability and estimation accuracy of a set of Bayesian filters, i.e., Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Unscented Particle Filter (UPF). Results are presented about a real dataset in which an advanced, state-of-the-art Adaptive UPF (AUPF) TC scheme, applied to a low-cost integrated setup, occasionally failed to track the navigation solution due to poorly conditioned GNSS measurements

Improved Outdoor Target Tracking via EKF-based GNSS/UWB Tight Integration with Online Time Synchronisation

Accurate and robust positioning technology in the mass-market segment is pivotal to support a number of critical Positioning, Navigation and Timing (PNT) applications. State-of-the-art Global Navigation Satellite System (GNSS) receivers design has been increasingly targeting flexible, embedded architectures integrating low-cost sensors to overcome GNSS limitations. The widespread proliferation of Ultra-Wide Band (UWB) technology, which enables centimeter-level accurate ranging in cluttered environments, is an appealing candidate for tight hybridisation with GNSS. When dealing with data streams from different Commercial-Off-The-Shelf (COTS) sensors, it is known that temporal misalignment is of concern, and accurate state-estimation via centralised, recursive filtering architectures can be undermined. As a first contribution, this work theoretically analyses the accuracy impact of asynchronous data association in the framework of a tightly integrated GNSS/UWB system leveraging plain Extended Kalman Filter (EKF) integration. Then, it puts forward a novel EKF-based model implementing online time offset estimation and compensation (i.e., time calibration) for GNSS/UWB tight integration. Results obtained in a multi-agent, cooperative scenario demonstrate that the proposed hybridisation methodology can achieve horizontal and vertical positioning accuracy gains of \SI{33.95}{\%} and \SI{59.33}{\%} , respectively, in Root-Mean-Square Error (RMSE) terms

A Customized EKF model for GNSS-based Navigation in the Harsh Space Environment

The extension of the Global Navigation Satellite System (GNSS) Space Service Volume (SSV) is of utmost relevance to afford enhanced autonomy in navigation, guidance, and control of space missions. Pioneering studies have shown the feasibility of using terrestrial GNSS signals in space applications, supporting Orbit Determination and Time Synchronization (ODTS) during Earth-Moon transfer orbits (MTOs) and lunar landings. However, non-terrestrial applications face challenges due to compromised signal availability at high altitudes, thus requiring advanced receiver architectures coupled with external aiding data. This paper presents a customized Bayesian filter, the Trajectory-Aware Extended Kalman Filter (TA-EKF), specifically designed for GNSS navigation along MTOs. The proposed filter architecture integrates aiding information, such as the planned mission orbital trajectory, to speed up filter convergence and achieve highly accurate positioning solutions. The performance of the TA-EKF is evaluated through simulations of MTO mission scenarios supported by Monte Carlo analyses, and it is compared against a standalone EKF

Snapshot Acquisition of GNSS Signals in Space: a Case Study at Lunar Distances

Observation and characterization of GNSS signals in space are gaining momentum for the re-use of GNSS and its integration in Orbit Determination and Time Synchronization solutions, oriented towards more autonomous Guidance, Navigation, and Control systems. In the initial phase of this transient, Radio-Frequency signals observations from space-borne receivers allow supporting GNSS-based space navigation thanks the remote post-processing. This contributes to understand and compensate for unmodelled features of GNSS signals propagating at large distances, up to the Moon's surface. Such activities require the capture of Intermediate Frequency (IF) signal samples, and upcoming Lunar missions, such as the NASA/ASI Lunar GNSS Receiver Experiement (LuGRE) scientific payload, are going to support the collection of raw GNSS signal samples and the transmission of such data to the mission ground segment. The size of such data is the main bottleneck for the typical, narrowband communication channels dedicated to such payloads. Therefore a sufficient amount of signal samples must be defined for an effective post-processing at the ground segment. As an early investigation, this work sets as a minimum objective the acquisition of GPS signals at the low carrier-to-noise density ratio (C/N_0) throughout a sample Moon Transfer Orbit (MTO). The designed acquisition stage implements high-sensitivity techniques and Doppler compensation to guarantee successful signal acquisition at critical C/N_0 values. By investigating the main acquisition parameters, the proposed study identifies minimum chunks length to be imposed as mission requirements