Heuristic occupancy and non-occupancy detection in a lighting system with a single transmitter and multiple receivers

Abstract

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 each of the plurality of receivers 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” having an attorney docket number ABLHD-103US 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 requires a considerable number of dedicated sensors th...

AI Technical Summary

Benefits of technology

[0005]The examples disclosed herein improve over RF-based sensing technologies by heuristically detecting one or more occupants in a space. In such examples, occupancy is sensed based on measurements of RF perturbations in an area or space. An example machine learning algorithm involves determining optimized heuristic algorithm coefficients associated with the RF perturbations to provide occupancy sensing in the area at a time. The optimized heurist

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 occ

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