Unmanned System Location Fusion Under GPS Jamming
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Solution Overview
Problem
Unmanned Systems (UMS) face challenges in maintaining accurate location awareness and collision avoidance, particularly in environments with malicious, jammed, or malfunctioning data sources, where reliance on GPS or RF signals is unreliable.
Innovation Solution
The implementation of an intelligent location awareness system that combines data from multiple sources such as geolocation sensors, RF receivers, RADAR, LIDAR, SONAR, SLAM systems, and inertial sensors, with an intelligent location awareness module (ILAM) to determine and verify the reliability of these sources, assign weights based on reliability, and provide a more accurate location to the guidance control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional GPS and RF communication are used for location awareness, then the system is simple to operate, but the reliability deteriorates in hostile environments with jamming or malicious data sources
Solution Approach 1:
The patent combines multiple location determination systems (GPS, RF receivers, RADAR, LIDAR, SONAR, SLAM, inertial sensors, acoustic sensors) into a unified location awareness system. The intelligent location awareness module integrates data from all these diverse sources, using weighted fusion algorithms to determine the most reliable location information, thereby achieving high reliability without excessive complexity.
Solution Approach 2:
The system employs multi-functional sensors that can serve multiple purposes. For example, the RF receiver not only provides location data but can also detect jamming attempts. The intelligent location awareness module universally processes data from any available source, adapting to different environmental conditions and maintaining reliability across various hostile scenarios.
2Measurement precision
If multiple data sources are integrated to improve location accuracy, then the reliability improves, but the device complexity increases
Solution Approach 1:
The intelligent location awareness module dynamically adjusts the weighting of different data sources based on their current reliability and accuracy. The system continuously evaluates the quality of input from each sensor and reallocates weights in real-time, allowing it to optimize location accuracy while managing processing complexity through adaptive rather than static weight assignment.
Solution Approach 2:
The system changes the parameters (weights) assigned to different data sources based on environmental conditions and source reliability. When GPS signals are jammed, the system automatically reduces the weight of GPS data and increases reliance on alternative sources like inertial sensors or SLAM, thereby maintaining accuracy without requiring manual reconfiguration of the complex multi-sensor system.
3Reliability
If the system relies on external data sources like GPS, then the ease of operation is maintained, but the vulnerability to jamming and malicious sources increases
Solution Approach 1:
The system incorporates self-service capabilities through the intelligent location awareness module, which autonomously evaluates the reliability of external data sources and determines whether to trust them. The system can independently detect jamming attempts, validate data consistency across multiple sources, and switch to autonomous navigation modes without human intervention, maintaining operational simplicity while achieving independence from potentially compromised external sources.
Data Source
AI summary
In some embodiments, a method for determining a location of an unmanned system (UMS) can include: receiving data from a plurality of data sources, wherein the data sources include a geolocation sensor and at least one of an RF receiver, a RADAR system, a LIDAR system, a SONAR system, an infrared camera, a Simultaneous Location and Mapping Algorithm (SLAM) system, an inertial sensor, or an acoustic sensor; determining a reliability of one or more of the data sources based on the received data; assigning weights to the data sources based at least in part on the determination of the reliability of the one or more data sources; and determining the location of the UMS using the received data and the assigned weights.