Modular RF Tracking With Directional Antennas and ML Control
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Solution Overview
Problem
Conventional RF systems are limited in functionality, adaptability, and directionality, often requiring significant effort and resources to update or reconfigure, and they typically use omnidirectional antennas that hinder targeted signal transmission.
Innovation Solution
A modular, adaptable, and movable RF system with directional broad-bandwidth antennas, processing modules, and machine learning capabilities, enabling flexible configuration, targeted signal transmission, and efficient power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If omnidirectional antennas are used, then signal coverage in all directions is achieved, but targeted signal transmission is eliminated and power requirements increase
Solution Approach 1:
The patent applies local quality by transitioning from omnidirectional antennas that radiate uniformly in all directions to directional antennas that concentrate signal transmission in specific directions. This allows the system to optimize power usage by directing energy only where needed rather than wasting it in all directions simultaneously.
Solution Approach 2:
The system implements dynamics through movable and adjustable directional antennas that can change their orientation and directionality based on detected object positions. This dynamic adjustment allows the system to maintain effective signal coverage while optimizing power consumption by adapting the transmission pattern to the actual spatial distribution of targets.
2Adaptability or versatility
If conventional RF systems are updated or reconfigured, then new functionalities are achieved, but significant expense and effort are required
Solution Approach 1:
The patent implements universality by designing an integrated system where directional antennas, machine learning models, and processing modules work together to perform multiple functions including detection, tracking, classification, and signal transmission. This multi-functional architecture allows the system to adapt to different applications without requiring separate specialized equipment for each function.
Solution Approach 2:
The system applies segmentation by dividing the RF system into modular components including directional antennas, machine learning processing units, and transmission modules. This modular architecture enables independent optimization and updating of individual components without requiring complete system replacement, thereby reducing configuration effort and expense.
3Adaptability or versatility
If conventional systems are moved or reinstalled, then new locations are covered, but testing and calibration are required
Solution Approach 1:
The patent implements self-service through machine learning models that automatically adapt to new environments and locations without requiring manual calibration. The system autonomously learns the spatial characteristics and signal propagation patterns in the new location, eliminating the time-consuming calibration process that would otherwise be necessary when relocating conventional systems.
4Use of energy by moving object
If modular directional systems are implemented, then targeted transmission and power efficiency are achieved, but system complexity increases
Solution Approach 1:
The patent applies mechanics substitution by replacing traditional mechanical calibration and configuration processes with machine learning-based automated adaptation. The ML models handle the complexity of directional beamforming and signal optimization, allowing the system to achieve power efficiency through intelligent control rather than complex mechanical adjustments.
Data Source
AI summary
A modular, radio frequency (“RF”) system includes one or more directional antennas and is configured with both hardware and software components to enable the RF system to monitor (e.g., detect or track signals or objects) and/or interact with (e.g., track signals or objects, or transmit signals) objects in particular directions. The RF system includes one or more machine learning models to determine, based on received signals, one or more signals to transmit.