GNSS Jammer Detection via RF-Optical Fusion and Optical Flow
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Solution Overview
Problem
Current GNSS jammer detection systems cannot accurately identify and track jamming devices, providing insufficient information for precise location and movement analysis, often leading to disruption of both intended and unintended GNSS receivers.
Innovation Solution
A system that calculates position and velocity information from jamming signals to control cameras, capturing images and using optical flow algorithms to extract motion information, thereby identifying and tracking the jammer, and updating tracking data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS jammer detectors determine position and velocity based on detected direction of jamming signals, then position and velocity information is provided, but the information is insufficient to readily identify the jammer
Solution Approach 1:
The system merges radio frequency jamming signal detection with optical detection by integrating a camera system with the GNSS jammer detector. The position and velocity information from RF detection is combined with visual imagery and motion information from the camera to achieve both precise tracking and positive identification of the jammer.
Solution Approach 2:
The system achieves multi-functionality by using a single integrated system that performs both RF jamming signal detection for position/velocity measurement and optical imaging for identification. The camera system serves multiple purposes: capturing visual evidence, extracting motion information, and providing positive identification capability.
2Reliability
If cameras capture images and optical flow algorithms extract motion information, then visual identification is achieved, but system complexity increases
Solution Approach 1:
The system uses feedback by utilizing motion information extracted from camera images to update and improve the position and velocity information initially derived from RF jamming signals. This feedback loop enhances tracking accuracy and confidence in identification while using existing system components.
Solution Approach 2:
The camera acts as an intermediary that bridges RF detection and visual identification. It captures images of the jammer, extracts motion information through optical flow algorithms, and provides visual evidence for positive identification, thereby connecting the RF detection system with the identification requirement.
3Measurement precision
If the system tracks jammer movement using calculated position and velocity information, then tracking accuracy is improved, but computational requirements increase
Solution Approach 1:
The system changes parameters by using motion information from optical flow analysis to update and refine the position and velocity parameters initially calculated from RF signals. This parameter refinement improves tracking accuracy while distributing computational load between RF signal processing and optical flow algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise identification and tracking of jamming devices, increasing confidence in positive identification and providing detailed visual information for authorities, enhancing the accuracy of jammer movement tracking.
Implementation Method 1
detects jamming signals received at one or more antennas
Implementation Method 2
analyzes the images, for example, utilizing an optical flow algorithm, to extract motion information associated with one or more objects identified in the images
Data Source
AI summary
A system detects, identifies, and optically tracks a jammer by calculating position and velocity information associated with the jammer based on jamming signals received at one or more antennas, and utilizing the position and velocity information to control one or more cameras. The cameras capture a series of images that include the calculated location, the expected movement of the jammer, or both. The system analyzes the images to extract motion information associated with one or more objects identified in the images. The system utilizes the calculated position and velocity information and the extracted motion information to determine which of the identified object in the images is the jammer. Further, the jammer motion information extracted from the images may be utilized to update the calculated position and velocity information associated with the jammer, to improve the overall accuracy of the tracking of the jammer.