Search

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.

Even though state-of-the-art RTK-based GNSS receivers usually match the demanding requirements posed by precise agriculture (PA) applications in open sky conditions, propagation effects degrade the performance under foliage or in presence of surrounding obstacles. This paper introduces a hybrid GNSS/INS system able to overcome some of the current limitations of GNSS in constrained agriculture environments. Authors performed several tests in different agricultural scenarios, such as vineyards, orchards and greenhouses. The paper presents the performance assessment through the evaluation of specific Key Performance Indicators (KPIs) and the benchmarking of this system with state-of-the art receivers, evaluating the benefits provided by new GNSS constellations and signals.

With the advent of new services and the evolution of transportation systems the need of a reliable and accurate knowledge of the position information became of paramount importance. Among the multiple technologies and sensors that might be used for positioning, Global Navigation Satellite System (GNSS) can be considered as the core technology and a superior system capable to provide the user with estimates of his own position in a global reference frame. Unfortunately, received GNSS signals are extremely weak and thus vulnerable to non-intentional and intentional Radio Frequency Interference. In particular, the malicious intentional interference produced by jammers is proliferating and it is becoming a serious threat for GNSS receivers. The integration of GNSS and external sensors with complementary characteristics, is the key for overcoming the weaknesses and enhancing the strengths of each sub-system. In this paper, the design of an ultra-tight hybrid navigation system is proposed with the aim to enhance the robustness of the navigation system in presence of jamming. A GNSS unit has been integrated with external sensors, namely Inertial Navigation Systems, visual sensor and odometer. Exploiting the “record and replay” of real GNSS signal artificially augmented with jamming interference within the L1 GNSS bandwidth, the performance has been assessed in controlled scenarios. The results presented in this paper show that the multi-sensor integrated system can sustain the navigation even in presence of severe jamming, although such a strong interference completely masks the GNSS signal denying the navigation of a GNSS stand-alone system. Moreover, the test procedures suitability of the record and replay approach for the jamming scenario generation and combination with real datasets are discussed and analyzed.

In the modern automotive industry, the wide variety of Intelligent Transport System (ITS) services needs a positioning unit. The core of such unit is a Global Navigation Satellite System (GNSS) receiver hybridized or assisted by other sensors or data. In harsh environments, such as urban scenarios, the testing and performance assessment of these terminals is of paramount importance for ITS applications. In fact such environments are very challenging for the GNSS core unit. This paper proposes some test procedures suitable to assess the performance of GNSS receivers, as part of an hybridyzed positioning unit, in urban scenarios. The benefits of a GNSS receiver based on the Software Defined Radio (SDR) technology is exploited by presenting a set of results obtained on field tests.

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways., Support funded by the Spanish Ministry of Economy and Competitiveness under DELPHIS project: TEC 2009-09712.

In recent years, Kalman filter (KF)-based tracking loop architectures have gained much attention in the Global Navigation Satellite System field and have been widely investigated due to its robust and better performance compared with traditional architectures. However, less attention has been paid to the in-depth theoretical analysis of the tracking structure and to the effects of Kalman tuning. A new approach is proposed to analyze the KF-based tracking loop. A control system model is derived according to the mathematical expression of the Kalman system. Based on this model, the influence of the choice of the setting parameters on the temporal evolution of the system response is discussed from the perspective of a control system. As a result, a reasoned and complete suite of criteria to tune the initial error covariance as well as the process and measurements noise covariances is demonstrated. Furthermore, a strategy is presented to make the system more robust in higher order dynamics without degrading the accuracy of carrier phase and Doppler frequency estimates.

This paper considers estimation problems where inequality constraints are imposed on the outputs of linear systems and can be modeled by nonlinear functions. In this case, censoring functions can be designed to constrain measurements for use by filters and smoothers. It is established that the filter and smoother output estimates are unbiased, provided that the underlying probability density functions are even and the censoring functions are odd. The Bounded Real Lemma is employed to ensure that the output estimates satisfy a performance criterion. A global positioning system (GPS) and inertial navigation system (INS) integration application is discussed in which a developed solution exhibits improved performance during GPS outages when a priori information is used to constrain the altitude and velocity measurements.

This paper presents the design of a truly low-cost multi-GNSS receivers /IMU navigation system that exploits the benefits of GNSS technology in attitude determination. The sensor package of the system consists of 4 single chipset receivers, 4 antennas, and one low grade MEMS IMU. Both parts, the GNSS and the IMU ones, are integrated by following a heading-constrained Tightly-Coupled approach, where inertial and GNSS raw measurements as well as GNSS computed heading are blended into an Extended Kalman Filter. To compute the attitude solution based on GNSS carrier phase measurements an integer ambiguity resolution algorithm has been developed, which takes advantage of the a priori solution of the EKF to reduce the search space and to provide an on-the-fly solution. The heading-constrained Tightly-Coupled approach followed in this paper has shown superior attitude accuracies when compared to the regular Tightly-Coupled implementation.

The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively identify unknown process noise variances. The use of smoothed estimates, as opposed to filtered estimates, leads to improved approximate Cramer-Rao lower bounds for the unknown parameters. It is also shown that the sequence of iterates are monotonic and asymptotically approach the actual values under prescribed conditions. A nonlinear mining navigation application is described in which unknown parameters are estimated.

The convergence of an expectation-maximization (EM) algorithm for state matrix estimation is investigated. It is shown for the expectation step that the design and observed error covariances are monotonically dependent on the residual error variances. For the maximization step, it is established that the residual error variances are monotonically dependent on the design and observed error covariances. The state matrix estimates are observed to be unbiased when the measurement noise is negligible. A navigation application is discussed in which the use of estimated parameters improves filtering performance.

The European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) has been developed by the European Union to enhance safety and efficiency of the railway transportation. So far, the positioning and monitoring of the trains have been based mainly on trackside equipment named balises: the introduction of Global Navigation Satellite Systems (GNSS) to implement an absolute positioning system on trains could lead to a better utilization of the line with a consequent line capacity increase, along with reliability increasing and cost reducing due to the removal of expensive infrastructures. This paper describes the insertion of a state-of-the-art channel model, named Land Mobile Satellite Channel Model (LMSCM) properly adapted to represent railways scenarios, into a hardware-in-the-loop system, where the parameters generated by LMSCM are used to configure an RF GNSS signal generator. The use of the hardware generator eases the simulation of the entire GNSS constellation, while the availability of the RF signal enables the assessment of the effects of rail-domain multipath on commercial receivers. The resulting model, named Rail-Land Mobile Satellite Channel Model (Rail-LMSCM), has been enhanced with the support of different kind of scenarios a train may encounter around the rail track, as well as with the introduction of GNSS outages due to the presence of tunnels. Such enhancements make the Rail-LMSCM a more realistic average representation of the railway environment. The implemented Rail-LMSCM has been used to test in the lab the local propagation effects on the performance of a complete GNSS receiver.