RF Occupancy Detection Using Heuristic Coefficients
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing RF-based occupancy detection systems in lighting systems face challenges such as high costs, inaccurate detection due to multiple transmitters, and reliance on satellite signals which are ineffective indoors, and lack integration with machine learning for real-time and accurate occupancy sensing.
Innovation Solution
A lighting system that uses a machine learning algorithm to detect occupancy by analyzing RF perturbations through a network of transmitters and receivers, applying heuristic algorithm coefficients to indicator data to determine occupancy or non-occupancy conditions in real-time, and optimizing coefficients through learning processes for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple transmitters transmit RF signals from multiple different regions, then coverage area is improved, but detection accuracy deteriorates due to signal interference
Solution Approach 1:
The system segments the detection task by assigning each transmitter to a specific geographic region. The single receiver processes indicator data from multiple transmitters and uses heuristic algorithms to determine which transmitter's region contains the occupant, based on which transmitter's signal shows theoccupancy condition. This segmentation resolves the contradiction by maintaining clear regional boundaries despite multiple transmitters operating simultaneously.
Solution Approach 2:
The receiver acts as an intermediary that collects indicator data from multiple transmitters and applies heuristic algorithm coefficients to determine occupancy. The heuristic algorithm serves as a mediator that processes the combined data from multiple sources, weighing each transmitter's contribution according to its regional coverage, thereby resolving signal interference while maintaining accurate detection.
2Measurement precision
If video sensor monitoring systems are used for occupancy detection, then detection accuracy is improved, but system cost and complexity increase
Solution Approach 1:
The system makes the lighting transmitters and receiver serve multiple functions: they provide both lighting control communications and occupancy detection. By utilizing the existing RF infrastructure for dual purposes, the system achieves accurate occupancy detection without adding dedicated video sensors or other specialized detection equipment, thereby reducing overall system complexity.
Solution Approach 2:
The lighting system's own RF communication infrastructure performs occupancy detection self-service. The transmitters and receiver that already exist for lighting control automatically detect occupancy by monitoring perturbations in their own signals, eliminating the need for separate detection systems and reducing overall system complexity while maintaining detection accuracy.
3Measurement precision
If GPS systems are used for location detection, then outdoor detection accuracy is improved, but indoor detection fails due to satellite signal blocking
Solution Approach 1:
The system replaces GPS satellite-based electromagnetic detection with a ground-based RF perturbation detection mechanism. By substituting the satellite-dependent GPS system with a local RF monitoring system that detects occupancy through perturbations in lighting control signals, the system achieves reliable indoor detection while maintaining the ability to detect outdoor occupancy when transmitters are in line of sight.
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 rapid and accurate occupancy detection, reduces false positives, and does not require occupants to carry devices, enhancing the efficiency and reliability of lighting control systems.
Implementation Method 1
occupancy is sensed based on measurements of RF perturbations in an area or space
Data Source
AI summary
Disclosed herein is a lighting system configured to obtain an indicator data of a RF spectrum signal generated at a number of times in an area. At each respective one of the number of times, apply one of a plurality of heurist algorithm coefficients to each indicator data from a receiver for the respective time, based on results of the applications of the coefficients to indicator data, generate an indicator data metric value for each of the indicator data for the respective time, and process the indicator data metric values to compute an output value. The lighting system is further configured to compare the output value at each of the plurality of times with a threshold, to detect one of an occupancy condition or a non-occupancy condition in the area and control the light source in response to the detected one of the occupancy condition or the non-occupancy condition in the area at each of the number of times.