Improvement of Anomalous Behavior Detection of GNSS Signal Based on TDNN for Augmentation Systems.

Jeongho Cho,Daehee Kim

doi:10.3390/s18113800

Abstract

The reliability of a navigation system is crucial for navigation purposes, especially in areas where stringent performance is required, such as civil aviation or intelligent transportation systems (ITSs). Therefore, integrity monitoring is an inseparable part of safety-critical navigation applications. The receiver autonomous integrity monitor (RAIM) has been used with the global navigation satellite system (GNSS) to provide integrity monitoring within avionics itself, such as in civil aviation for lateral navigation (LNAV) or the non-precision approach (NPA). However, standard RAIM may not meet the stricter aviation availability and integrity requirements for certain operations, e.g., precision approach flight phases, and also is not sufficient for on-ground vehicle integrity monitoring of several specific ITS applications. One possible way to more clearly distinguish anomalies in observed GNSS signals is to take advantage of time-delayed neural networks (TDNNs) to estimate useful information about the faulty characteristics, rather than simply using RAIM alone. Based on the performance evaluation, it was determined that this method can reliably detect flaws in navigation satellites significantly faster than RAIM alone, and it was confirmed that TDNN-based integrity monitoring using RAIM is an encouraging alternative to improve the integrity assurance level of RAIM in terms of GNSS anomaly detection.

Highlights

In areas that require stringent performance when using the global navigation satellite system (GNSS), such as civil aviation or intelligent transportation systems (ITSs), the integrity of GNSS plays an important role and its monitoring is required
) obtained through the trained time-delayed neural networks (TDNNs) was used to form the newly proposed test statistic by estimates based on the offline pseudo-range and comparing estimates based on the offline pseudo-range
When injecting a +150-m bias forinanchanging hour of while injection time all active satellites. These results showed that the proposed detectionstart rateinjection and for compared with when injecting a +150-m bias that for receiver autonomous integrity monitor (RAIM)/TDNN

Summary

Introduction

In areas that require stringent performance when using the global navigation satellite system (GNSS), such as civil aviation or intelligent transportation systems (ITSs) (e.g., electronic fee collection, route guidance, advanced driver assistance systems including collision avoidance systems, and intelligent speed adaptation), the integrity of GNSS plays an important role and its monitoring is required In these liability-critical applications, the reliability of location solutions derived from navigation data must be considered [1,2,3,4]. An alternative integrity augmentation mechanism is needed to detect error sources that exceed a predefined stringent safety limit and safely send alerts to manned/unmanned ground systems to avoid the constraints For this reason, the goal of this paper is to devise a new method to more efficiently capture anomalous behavior in a ranging signal. The ability to detect anomalies has been improved, especially when there is an abnormality in the measurements, and our proposed method shows a clear advantage in being able to detect small variations, as compared to a typical monitor

GNSS Signal Modeling

TDNN for Anomalous Event Detection

Simulation Case Studies

Proposed

Performance comparison of of anomaly detection

Conclusions