Integrity monitoring for undifferenced and uncombined PPP under the local environment

Integrity monitoring of precise point positioning (PPP) can provide tightly guaranteed absolute position error bounds for safety-critical applications. However, complex local environment makes PPP integrity monitoring much more challenging, such as urban canyons. Significant prone multipaths and low observation redundancy are main difficulties to the accuracy and the reliability of PPP. Therefore, we proposed a solution separation-based integrity monitoring algorithm, which is based on a single and dual frequency-mixed undifferenced and uncombined PPP model considering compensation for the multipath error distortion by Gaussian overbounding. Both the static and the kinematic data are utilized to test the proposed algorithm. The results show that the proposed algorithm can produce adequate protection level in horizontal and vertical directions. Furthermore, the proposed algorithm can obtain smoother protection level and positioning error under the dynamic local environment, and effectively suppress the misleading information.

Integrity Monitoring of PPP-RTK Positioning; Part I: GNSS-Based IM Procedure

Nowadays, integrity monitoring (IM) is required for diverse safety-related applications using intelligent transport systems (ITS). To ensure high availability for road transport users for in-lane positioning, a sub-meter horizontal protection level (HPL) is expected, which normally requires a much higher horizontal positioning precision of, e.g., a few centimeters. Precise point positioning-real-time kinematic (PPP-RTK) is a positioning method that could achieve high accuracy without long convergence time and strong dependency on nearby infrastructure. As the first part of a series of papers, this contribution proposes an IM strategy for multi-constellation PPP-RTK positioning based on global navigation satellite system (GNSS) signals. It analytically studies the form of the variance-covariance (V-C) matrix of ionosphere interpolation errors for both accuracy and integrity purposes, which considers the processing noise, the ionosphere activities and the network scale. In addition, this contribution analyzes the impacts of diverse factors on the size and convergence of the HPLs, including the user multipath environment, the ionosphere activity, the network scale and the horizontal probability of misleading information (PMI). It is found that the user multipath environment generally has the largest influence on the size of the converged HPLs, while the ionosphere interpolation and the multipath environments have joint impacts on the convergence of the HPL. Making use of 1 Hz data of Global Positioning System (GPS)/Galileo/Beidou Navigation Satellite System (BDS) signals on L1 and L5 frequencies, for small- to mid-scaled networks, under nominal multipath environments and for a horizontal PMI down to , the ambiguity-float HPLs can converge to 1.5 m within or around 50 epochs under quiet to medium ionosphere activities. Under nominal multipath conditions for small- to mid-scaled networks, with the partial ambiguity resolution enabled, the HPLs can converge to 0.3 m within 10 epochs even under active ionosphere activities.

Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.

Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model.

ESTIMATION OF GPS DISPERSIVE AND NON-DISPERSIVE NETWORK CORRECTION FOR ISKANDARnet NETWORK-BASED REAL-TIME KINEMATIC (N-RTK) POSITIONING SYSTEM

The concept of N-RTK positioning has been extensively developed in order to better model the distance-dependent errors of GPS carrier-phase measurements. These errors can be separated into a frequency-dependent or dispersive component (i.e., the ionospheric delay) and a non-dispersive component (i.e., the tropospheric delay and orbit biases) to express the network correction in order to attain better modelling of GPS distance dependent errors. However, the N-RTK performance may degrades due to severe atmospheric irregularities that would seriously affect the modelling of the GPS distance-dependent errors, thus affecting the quality of network correction generation. The development of integrity monitoring for network correction would be great idea to identify the quality and reliability of network correction data dissemination. Therefore, this paper aims to estimates the trend of GPS dispersive and non-dispersive network correction to supports future development of integrity monitoring for network correction of ISKANDARnet N-RTK positioning system. The first part of this paper is to extract the GPS dispersive and non-dispersive network residual components. This part includes the double-differencing technique, ambiguity resolution and carrier-phased linear combination in the process. The LIM then are applied for user network coefficient value computation purpose in the second part. Finally, the GPS dispersive and non-dispersive network correction can be generated with GF and IF network correction algorithm respectively. The trend of GPS dispersive and non-dispersive network correction is expected to aid the estimation and realization of threshold limit value for development of integrity monitoring for network correction of ISKANDARnet N-RTK positioning system.

Multi-Layer Defences for Robust GNSS Timing Retrieval

A multi-layered interference mitigation approach can significantly improve the performance of Global Navigation Satellite System (GNSS) receivers in the presence of jamming. In this work, three levels of defence are considered including: pre-correlation interference mitigation techniques, post-correlation measurement screening and FDE at the Position, Velocity, and Time (PVT) level. The performance and interaction of these receiver defences are analysed with specific focus on Robust Interference Mitigation (RIM), measurement screening through Lock Indicator (LIs) and Receiver Autonomous Integrity Monitoring (RAIM). The case of timing receivers with a known user position and using Galileo signals from different frequencies has been studied with Time-Receiver Autonomous Integrity Monitoring (T-RAIM) based on the Backward-Forward method. From the experimental analysis it emerges that RIM improves the quality of the measurements reducing the number of exclusions performed by T-RAIM. Effective measurements screening is also fundamental to obtain unbiased timing solutions: in this respect T-RAIM can provide the required level of reliability.