Fang-Cheng Chan

Decorrelation of Troposphere Across Short Baselines

The performance of GNSS RTK systems is strongly dependent on spatial correlation of tropospheric errors. However, normal storm activity can cause conditions where tropospheric delays can be significantly different even across relatively short baselines. In this paper, the decorrelation of tropospheric delay over short baselines (2 to 10 km) is investigated. Data from pairs of CORS stations as well as independently collected data are used to derive double- difference phase residuals for L1 and L2 measurements. The known coordinates of the ends of the baseline are accounted for in the calculation of the residuals to provide a measurement of double-difference phase errors. Multipath contributions to the phase errors are minimized by comparing data collected on consecutive days. Tropospheric effects are isolated from ionospheric effects by looking for signatures unique to the troposphere. One such signature is the relative magnitude of the L1 and L2 residuals. A second signature is correlation with local weather data and absence of correlation with ionospheric activity. A strong correlation is seen between storm activity shown on local weather radar and high residuals. Residuals attributed to the troposphere equivalent to at least 12 parts per million for overhead satellites are observed across short baselines.

GPS spoofing detection using RAIM with INS coupling

In this work, we develop, implement, and test a monitor to detect GPS spoofing attacks using residual-based Receiver Autonomous Integrity Monitoring (RAIM) with inertial navigation sensors. Signal spoofing is a critical threat to all navigation applications that utilize GNSS, and is especially hazardous in aviation applications. This work develops a new method to directly detect spoofing using a GPS/INS integrated navigation system that incorporates fault detection concepts based on RAIM. The method is also capable of providing an upper bound on the proposed monitors integrity risk.

High integrity stochastic modeling of GPS receiver clock for improved positioning and fault detection performance

In this paper, a validated stochastic clock random error model is used to derive a correct time correlation matrix for a sequence of clock random errors. A batch estimator incorporating the complete time correlation matrix is developed to account for clock random errors. Performance improvement for the proposed receiver clock-aided navigation system is fully investigated. A benchmark application of an aircraft precision approach is used to evaluate the system availability performance with a single satellite failure assumption.

Stochastic modeling of atomic receiver clock for high integrity gps navigation

The Global Positioning System (GPS) was designed as a passive radio navigation system to provide positioning capability without the need for receiver clock synchronization to GPS time. In theory, a GPS receiver equipped with a high-quality receiver clock can provide better system performance. More importantly for integrity-driven navigation applications, the additional redundancy provided by a clock dynamic model can increase fault detection performance and availability for receiver autonomous integrity monitoring (RAIM). In the work presented here, a stochastic model is used to rigorously account for the time correlation of random errors in high-quality GPS receiver clocks. A batch estimator (BE) is then developed to use GPS measurements collected over time to estimate current user position. The BE incorporates the stochastic clock model to ensure that position errors are properly quantified and, therefore, that navigation integrity is ensured. The resulting improvement in positioning performance is demonstrated using covariance analysis.

Tightly coupled GPS/INS integration for differential carrier phase navigation systems using decentralized estimation

Much research has been conducted in the area of tightly coupled GPS/INS, and this work has resulted in a vast array of navigation algorithms. A common theme of these methods is that they operate on low rate GPS ranging measurements of code and carrier phase together with high rate raw inertial measurements, such as specific force and inertial angular velocity. For stand-alone (i.e., non-differential) GPS navigation applications, high data rate INS outputs can be properly accommodated with todays computer processors. For relative (i.e., differential) GPS navigation applications, the optimal analogous solution would be for the mobile user to have access to the reference stations raw inertial measurements along with its own. However, due to communication bandwidth limitations, it is generally not possible to broadcast high data rate inertial navigation data. In response, an alternative tightly-coupled, differential GPS/INS navigation system is developed here using a decentralized Kalman filtering approach, which can operate at manageable broadcast data rates.

ARAIM Integrity Support Message Parameter Validation by Online Ground Monitoring

In this paper we introduce a ground monitoring architecture to validate the integrity support message (ISM) parameters to be used by aircraft for Advanced Receiver Autonomous Integrity Monitoring (ARAIM). This work focuses on two critical ISM parameters: Psat, which designates the prior probabilities of satellite faults, and bmax, which is a range domain bound on small faults that may occur at probabilities higher than Psat. We show that the choices of bmax and Psat are not independent. The paper first establishes the relationship between bmax, Psat, time to integrity alert (TIA) and constellation service provider performance commitments. We then provide an example ground monitor design that detects interfrequency bias faults and code-carrier divergence faults. We show that the performance of the monitor can be used to validate specific bmax and Psat values for ARAIM.

Bayesian Fault-Tolerant Position Estimator and Integrity Risk Bound for GNSS Navigation

The advent of multiple Global Navigation Satellite System (GNSS) constellations will result in a considerable increase in the number of satellites for positioning worldwide. This substantial improvement in measurement redundancy has the potential to radically advance receiver autonomous integrity monitoring (RAIM) performance. However, regardless of the number of satellites, the performance of existing RAIM methods is sensitive to the assumed prior probabilities of individual fault hypotheses. In this paper, a new method is developed using Bayes’ theorem to generate upper bounds on posterior probabilities of individual fault hypotheses given current user measurements. These bounds are used in a Bayesian fault-tolerant position estimator (FTE) that minimizes integrity risk. The detection test statistic is a measurement-based integrity risk bound, which is directly compared with a pre-specified risk requirement. The associated challenge of quantifying continuity risk is resolved using a bounding approach, which is also detailed in this work. The new Bayesian FTE method is shown to be more robust to uncertainty in prior probability of fault occurrence than existing RAIM methods.

High-integrity GPS/INS integrated navigation with error detection and application to LAAS

Abstract—A dynamic state realization for tightly coupling Global Positioning System (GPS) measurements with an Inertial Navigation System (INS) is described. The realization, based on the direct fusion of GPS and INS systems through Kalman filter state dynamics, explicitly accounts for temporal and spatial decorrelation of GPS measurement errors (such as tropospheric, ionospheric, and multipath errors) through state augmentation, thereby ensuring Kalman filter integrity under fault-free error conditions. Predicted system performance for a Local Area Augmentation System (LAAS) aircraft precision approach application is evaluated using covariance analysis and validated with flight data. Built-in fault detection mechanisms based on the Kalman filter innovations are also evaluated to help provide integrity under certain fault conditions. It is shown that an algorithm based on the integral of Kalman filter innovations outperforms other existing GPS fault detection methods in the detection of slowly developing ranging errors, such as those caused by ionospheric and tropospheric anomalies.

Optimal antenna topologies for spatial gradient detection in differential GNSS

This paper describes new methods to determine optimal reference antenna topologies for detection of spatial gradients in differential Global Navigation Satellite Systems (GNSS). Such gradients can be caused by ionospheric fronts and orbit ephemeris faults, and if undetected, represent major threats to aircraft navigation integrity. Differential carrier phase measurements between ground antennas are highly sensitive to spatial gradients. Therefore, monitors using spatially separated ground antennas have recently attracted great interest. However, they cannot detect gradients of all sizes and directions due to the presence of integer ambiguities. These ambiguities cannot be resolved because the gradient magnitude is unknown a priori. Furthermore, the performance of such monitors is highly dependent on the spatial distribution of reference antennas. In this work, we introduce new methods to find optimized antenna topologies for spatial gradient detection.