RF Occupancy Counting in Lighting Networks With Heuristic Signal Fusion
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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 machine learning applications, leading to false positives and inadequate control of lighting and other environmental systems.
Innovation Solution
A system that integrates RF wireless communication with lighting elements, utilizing a processing circuitry to apply heuristic algorithm coefficients to RF signal data from multiple transmitters and receivers to determine occupancy counts in real-time, enabling accurate and rapid detection of changes in occupancy levels and controlling lighting and other systems like HVAC and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple transmitters and receivers are used in RF-based occupancy counting, then the coverage and detection capability are improved, but the accuracy of occupancy count deteriorates due to signal interference and measurement errors
Solution Approach 1:
The patent divides the occupancy detection task into multiple independent transmitter-receiver pairs, where each pair independently measures occupancy in its specific zone. The processing circuitry segments the overall detection area into multiple regions, with each transmitter-receiver pair responsible for a specific segment, thereby maintaining measurement accuracy while achieving comprehensive coverage through the combination of multiple segments.
Solution Approach 2:
The processing circuitry acts as an intermediary that receives signals from multiple transmitters and receivers, applies heuristic algorithm coefficients to correct for interference and measurement errors, and synthesizes the individual measurements into an accurate overall occupancy count. This intermediary processing layer reconciles the conflicting requirements of multiple sensors and measurement accuracy.
2Speed
If RF signal measurements are processed in real-time with multiple transmitters and receivers, then the response speed is improved, but the complexity of signal processing and algorithm computation increases
Solution Approach 1:
The patent pre-calculates and stores heuristic algorithm coefficients that are specific to each transmitter-receiver pair and their respective zones. These coefficients are determined in advance through calibration processes, allowing the processing circuitry to perform real-time occupancy calculations by simply applying these pre-computed coefficients to current signal measurements, thereby reducing real-time computational complexity while maintaining fast response.
Solution Approach 2:
The patent transforms the complex signal processing problem into a simpler parameter-based calculation by using heuristic algorithm coefficients that encapsulate the relationship between RF signal characteristics and occupancy. Instead of performing complex real-time signal analysis, the system changes the approach to using pre-determined parameter relationships, significantly reducing processing complexity while maintaining real-time response capability.
3Measurement precision
If heuristic algorithm coefficients are optimized through machine learning, then the accuracy of occupancy counting is improved, but the time required for learning and system commissioning increases
Solution Approach 1:
The patent performs machine learning optimization and heuristic coefficient determination as a preliminary action during the commissioning phase or system installation. The learning process is completed in advance, and the optimized coefficients are stored for use during normal operation. This preliminary action separates the time-consuming optimization process from the real-time occupancy counting operation, thereby achieving high accuracy without sacrificing operational response time.
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 in occupancy counting, reduces false positives, and enables rapid, real-time responses for lighting and environmental control, enhancing the management of spaces by integrating RF wireless communication with machine learning algorithms within lighting systems.
Implementation Method 1
A plurality of wireless communication transmitters for wireless radio frequency (RF) spectrum transmission in an area... A wireless communication receiver configured to receive RF spectrum signals of transmissions from each of the plurality of transmitters through the area
Implementation Method 2
The receiver is configured to generate an indicator data of a signal characteristic of received RF spectrum signal... the processing circuitry is configured to apply one of a plurality of heuristic algorithm coefficients to each indicator data of an RF spectrum signal
Data Source
AI summary
System level occupancy counting in a lighting system configured to obtain an indicator data of a RF spectrum signal (signal) generated at a number of times in an area. At each respective one of the number of times, based on results of application of heuristic algorithm coefficients, the lighting system generates an indicator data metric value for each of the indicator data for the respective time. The lighting system processes 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 determines an occupancy count in the area at the respective time based on the computed output metric value.