Aircraft GPS Anomaly Mapping Using ADS-B Position Data
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Solution Overview
Problem
Aircraft GPS systems may fail due to interference, jamming, or spoofing, leading to navigation issues without internet connectivity for real-time data access, and web-based platforms provide delayed or incomplete information on GPS malfunctions.
Innovation Solution
A system that receives real-time data from other aircraft via ADS-B signals to identify GPS anomalies, creating digital maps indicating compromised locations, allowing pilots to adjust flight paths to avoid these areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If web-based platforms are used to provide GPS interference information, then information about GPS malfunctions can be accessed, but the aircraft requires internet connectivity and experiences time lag in detecting GPS issues
Solution Approach 1:
The system performs preliminary action by proactively detecting GPS anomalies and generating alerts before the aircraft reaches the problematic areas. The anomaly detection system continuously monitors GPS signal quality and identifies potential issues in advance, allowing the pilot to take corrective action before GPS failure occurs during flight.
Solution Approach 2:
The patent introduces an intermediary system consisting of anomaly detection algorithms and alert generation mechanisms that mediate between the GPS receiver and the pilot. This intermediary continuously processes GPS data, identifies anomalies, and provides timely warnings without requiring direct access to web-based platforms, thereby eliminating time lag.
2Loss of information
If web-based platforms are used to identify GPS interference locations, then location data can be provided, but the platforms lack real-time capability and may not have data for upcoming flight paths
Solution Approach 1:
The system implements continuous monitoring of GPS signal characteristics throughout the aircraft's operational area. The anomaly detection algorithm continuously processes GPS data streams, maintaining real-time awareness of signal quality and generating alerts as anomalies are detected, ensuring no time lag between detection and notification.
Solution Approach 2:
The system performs self-service by autonomously detecting GPS anomalies and generating alerts without requiring external web-based platforms. The integrated anomaly detection algorithms process GPS data locally, enabling the system to serve itself by providing real-time GPS interference information independently.
3Reliability
If GPS data is continuously monitored for anomalies, then real-time detection of GPS malfunctions is achieved, but data processing complexity increases
Solution Approach 1:
The system applies local quality by tailoring the anomaly detection algorithms to specific GPS signal characteristics and flight conditions. Different detection thresholds and parameters are applied based on the current operational context, such as altitude, speed, and geographic location, optimizing detection accuracy for each specific scenario without processing all data uniformly.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting detection thresholds and alert criteria based on changing flight conditions and GPS signal characteristics. The system modifies monitoring parameters in real-time according to the aircraft's state and environmental factors, maintaining high detection accuracy while adapting to varying operational requirements.
Data Source
AI summary
A system and a method continuously receives data at a first aircraft from a second aircraft while the first and second aircrafts are traveling. The data includes at least some positional information of the second aircraft. One or more anomalies in the data are identified that are indicative of one or more geographic locations where a global positioning system of the second aircraft was compromised. A digital map is automatically created that indicates the one or more geographic locations associated with the one or more anomalies that are identified in the data.