Multi-Source Reckoning System for Resilient Navigation
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Solution Overview
Problem
GPS systems suffer from accuracy and reliability degradation in environments with insufficient or unreliable data, and are vulnerable to interference and spoofing, which affects critical infrastructure and navigation systems.
Innovation Solution
A multi-source reckoning system that utilizes a diverse set of sensors, including IMUs, digital magnetic compasses, and speed sensors, to generate consensus heading and distance data, and employs artificial intelligence to identify and correct errors, providing reliable location information even when GPS is unavailable or degraded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS systems are used for localization and navigation, then positioning capability is provided, but accuracy and reliability degrade in environments with insufficient or unreliable GPS data
Solution Approach 1:
The patent combines GPS data with data from multiple secondary sensors (accelerometers, gyroscopes, magnetometers, barometers, cameras, LIDAR, RADAR) to create a multi-source reckoning system. This merging of multiple data sources allows the system to maintain reliable positioning even when GPS data is insufficient or unreliable, as the secondary sensors can compensate for GPS degradation through sensor fusion algorithms.
Solution Approach 2:
The system implements a universal positioning approach that can operate in multiple modes: GPS-only mode, multi-source reckoning mode when GPS is unavailable, and hybrid mode combining both. This multi-functionality allows the same system to adapt to different environmental conditions and maintain both reliability and accuracy across diverse scenarios, from open skies to urban canyons with GPS blockage.
2Adaptability or versatility
If GPS data is relied upon, then navigation functionality is provided, but the system becomes vulnerable to interference and spoofing
Solution Approach 1:
The system implements preliminary anti-action by using secondary sensors to detect and counteract GPS interference or spoofing before it completely compromises navigation. The multi-source reckoning system continuously monitors GPS data against independent sensor measurements, allowing it to identify and reject spoofed GPS signals by comparing them with physically plausible data from accelerometers, gyroscopes, and other sensors that cannot be easily spoofed.
Solution Approach 2:
The secondary sensors act as intermediaries between the vehicle and the navigation system. Instead of relying directly on vulnerable GPS signals, the system uses sensor data from accelerometers, gyroscopes, magnetometers, and other devices as intermediary measurements to verify GPS authenticity and maintain navigation integrity when GPS data may be compromised.
3Reliability
If secondary sensors are used for reckoning without GPS, then GPS independence is achieved, but unacceptable drift occurs
Solution Approach 1:
The system implements feedback by continuously comparing multi-source reckoning position estimates with available GPS data when GPS is partially functional. This feedback loop allows the system to detect and correct drift in the secondary sensor-based positioning by periodically recalibrating against reliable GPS measurements, maintaining both GPS independence capability and acceptable positioning precision over extended periods.
Data Source
AI summary
Method, systems, and computer-readable media containing instructions which, when executed by a computing device, cause it to receive data from an inertial measurement unit, including GPS data, velocity data, and bearing data, receive data from a digital magnetic compass, including bearing data, receive data from a Doppler sensor, including velocity data and distance data, determining whether GPS location data is in consensus with a previous derived multi-source reckoning system location, determining a consensus distance value from a weighted average of data from the inertial measurement unit and the Doppler sensor, determine a consensus heading value from a weighted average of data from the inertial measurement unit and the digital magnetic compass, determine a consensus geolocation value from a weighted average of data from the inertial measurement unit and the previous derived multi-source reckoning system location, and determine a derived multi-source reckoning system location.