Heuristic occupancy and non-occupancy detection in a lighting system

Abstract

Disclosed herein is a lighting system configured to obtain an indicator data of a RF spectrum 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 coefficients to each indicator data 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.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application is related to a patent application entitled “Heuristic Occupancy and Non-Occupancy Detection in a Lighting System with a Single Transmitter and Multiple Receivers” having an attorney docket number ABLHD-108US filed herewith and a patent application entitled “Heuristic Occupancy and Non-Occupancy Detection in a Lighting System with Multiple Transmitters and a Single Receiver” having an attorney docket number ABLHD-109US also filed herewith.BACKGROUND[0002]In recent years, a number of systems and methods have been proposed for occupancy detection within a particular area utilizing radio frequency (RF) based technologies. Examples of such systems include video sensor monitoring systems, radio frequency identification (RFID) systems, global positioning systems (GPS), and wireless communication systems among others. However, many of these systems have several disadvantages. For example, the video sensor monitoring system requi...

AI Technical Summary

Benefits of technology

The patent describes a method for improving RF-based sensing technologies by detecting occupancy in a space using a machine learning algorithm. The method involves measuring RF perturbations in an area and applying optimal heuristic algorithm coefficients to the data to provide real-time occupancy sensing. The lighting system includes a control module that processes indicator data from multiple receivers and compares the data with a threshold to detect occupancy or non-occupancy conditions. The technical effects include improved sensing accuracy and efficiency, as well as adaptive learning capabilities for optimizing opportunities and detecting new occupancy patterns.

Problems solved by technology

However, many of these systems have several disadvantages.

For example, the video sensor monitoring system requires a considerable number of dedicated sensors that are expensive and the system requires a large amount of memory for storing data.

However, such systems are generally less effective indoors or in other environments where satellite signals may be blocked, reducing accuracy of detecting the occupant in the area.

However, an inaccurate detection of the occupant in a region or a sub-area in the area can occur when multiple transmitters are transmitting the RF signals from multiple different regions / sub-areas of the area.

Although there have been suggestions to control lighting based on RF wireless detection results, prior RF-based detection systems have not themselves been integrated as part of a machine learning (ML) in a lighting system of which the lighting operation are controlled as a function of the detection.

Images