RF Occupancy Counting via Heuristic Signal Perturbation Analysis
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Solution Overview
Problem
Existing occupancy counting systems, particularly those using RF wireless communication, face challenges such as inaccurate counting due to multiple transmitters and receivers, lack of integration with lighting systems, and inefficiencies in real-time response and accuracy, especially in managing occupant counts across various areas and regions.
Innovation Solution
A system that integrates RF wireless communication with lighting elements, utilizing a processing circuitry to analyze RF perturbations and apply heuristic algorithm coefficients to determine occupancy counts in real-time, enabling accurate and rapid detection of changes in occupancy levels and managing related controls like lighting, HVAC, and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If RF wireless communication systems use multiple transmitters and receivers to monitor occupants, then the coverage and monitoring capability are improved, but the accuracy of occupancy counting deteriorates due to signal interference and perturbation
Solution Approach 1:
The patent introduces an intermediary processing system that receives RF signals from multiple transmitters and receivers, then applies heuristic algorithms and machine learning models to interpret the combined signals. This intermediary layer separates the complex multi-signal environment from the occupancy determination process, allowing accurate counting despite the presence of multiple transmitters and receivers that would otherwise create signal interference.
2Reliability
If traditional occupancy systems use dedicated sensors and video monitoring, then the occupancy detection capability is improved, but the system complexity and cost increase
Solution Approach 1:
The patent makes existing RF transmitters and receivers (originally designed for wireless communication purposes) serve a dual function by also using them for occupancy detection. The same hardware infrastructure used for data transmission is repurposed to monitor occupancy by analyzing RF signal perturbations, eliminating the need for dedicated occupancy sensors and reducing overall system complexity.
Solution Approach 2:
The system uses its own RF communication signals to perform occupancy detection without requiring external dedicated sensing infrastructure. The RF signals transmitted for communication purposes automatically provide occupancy information through their interaction with occupants (signal perturbations), allowing the system to serve both communication and monitoring functions simultaneously.
3Speed
If RF signal characteristics are analyzed in real-time to determine occupancy, then the response time is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent implements machine learning models and heuristic algorithms during an offline training phase before deployment, where the system learns to recognize occupancy patterns from RF signal characteristics. During real-time operation, the pre-trained models quickly process incoming RF signals to determine occupancy, significantly reducing computational complexity and processing requirements compared to performing full analysis in real-time without prior training.
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
The system provides improved accuracy and rapid response in occupancy counting, reducing false positives and enabling effective management of lighting and other controls based on real-time occupant data, enhancing operational efficiency in various environments.
Implementation Method 1
A plurality of wireless communication transmitters for wireless radio frequency (RF) spectrum transmissions in an area... Each of the plurality of transmitters is integrated into one of the lighting elements
Implementation Method 2
occupancy count is determined based on measurements of RF perturbations in an area or space
Data Source
AI summary
Disclosed herein is system level occupancy counting in a lighting system configured to obtain an indicator data of a RF spectrum signal (signal) generated by a number of receivers at a number of times in an area. At each respective one of the number of times, for each respective one of the receivers, apply one of a plurality of heurist algorithm heuristic algorithm coefficients to each indicator data of the signal, based on results of the application of the heuristic algorithm coefficients, generate an indicator data metric value for each of the indicator data for the respective time. The lighting system is also configured to process each of the indicator data metric value to compute a plurality of metric values for the respective time and combine the plurality of metric values to compute an output metric value for each of a plurality of probable number of occupants in the area for the respective time. The lighting system is further configured to determine an occupancy count in the area at the respective time based on the computed output metric value.