AI UAV Detection and Countermeasure Orchestration
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Solution Overview
Problem
Current detection technologies for unmanned aerial vehicles (UAVs) face limitations such as limited range, signal interference, clutter, precision issues, and high costs, making them ineffective for real-time low-altitude airspace management and security.
Innovation Solution
The proposed solution involves a method and system that utilize a trained artificial intelligence (AI) model to classify objects based on conditioned signals, including micro-doppler signatures and intensity plots, and employ jamming and spoofing techniques to counter UAVs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current detection technologies (RF, acoustic, EO/IR) are used for UAV detection, then detection capability is provided, but detection precision and reliability deteriorate due to signal interference, clutter, and environmental factors
Solution Approach 1:
The patent combines multiple detection technologies (RF detection, acoustic detection, and EO/IR detection) into an integrated detection system. This fusion approach allows the system to overcome the individual limitations of each technology by cross-validating detections and reducing false positives from environmental interference and signal clutter.
Solution Approach 2:
The patent introduces an AI-based signal processing intermediary that processes and analyzes raw detection signals from multiple sensors. This intermediary layer filters out environmental noise and clutter, extracting meaningful patterns that improve both detection precision and reliability in challenging conditions.
2Measurement precision
If advanced detection technologies are deployed to improve detection capability, then detection range and accuracy are enhanced, but system complexity and cost increase
Solution Approach 1:
The patent designs a multi-functional detection system where a single integrated platform performs RF detection, acoustic detection, and EO/IR detection simultaneously. This universal system reduces overall complexity compared to deploying separate specialized systems for each detection modality, while maintaining high detection accuracy through AI-based signal processing.
3Adaptability or versatility
If existing countermeasure techniques (capture nets, interceptors) are used, then short-range countermeasures are provided, but adaptability to various UAV types and ranges is limited
Solution Approach 1:
The patent implements dynamic countermeasure selection where the system automatically adapts its response based on the detected UAV type, range, and threat level. The AI system adjusts detection parameters and selects appropriate countermeasures in real-time, providing versatility across different scenarios while maintaining reliability through data-driven decision-making.
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
This approach enhances the detection accuracy and adaptability of UAVs, reduces false positives and negatives, and provides a comprehensive and cost-effective solution for low-altitude airspace security and management.
Implementation Method 1
a radar unit that transmits radar signals and receives reflected radar signals
Implementation Method 2
receives a signal reflected by an object in an environment
Implementation Method 3
providing the conditioned signal as an input to a trained artificial intelligence (AI) model and determining whether the object is an unmanned aerial vehicle (UAV) based on an output of the trained AI model
Implementation Method 4
activating a jammer for a first time-period in response to receiving the trigger signal. One or more jamming signals are transmitted by the jammer
Implementation Method 5
activating a spoofer at the completion of the first time-period. One or more spoofing signals are transmitted by the spoofer. Further, the method comprises calibrating power of the one or more spoofing signals based on the range of the UAV
Data Source
AI summary
The present disclosure describes a system and a method for object detection and counter measures. A signal reflected by an object in an environment is conditioned to improve various parameters such as signal to noise ratio, spectral resolution, color mapping, or the like. A determination whether the object is unmanned aerial vehicle is based on an output of a trained AI model. The trained AI model classifies the detected object into a category based on the conditioned signal. Additionally, a jammer and spoofer are orchestrated based on determination that the object is an unmanned aerial vehicle. A control of the object is achieved based on the orchestration to perform counter measures such as jamming and spoofing.