Wireless Proximity Sensing via Channel Statistics
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Solution Overview
Problem
Existing wireless proximity sensing technologies face limitations in accurately detecting motion proximity without requiring complex setup or high precision localization, and they often fail to efficiently recognize motion within specific ranges, which is crucial for smart home automation and health care applications.
Innovation Solution
A system that processes wireless channel information (CI) to obtain time series of channel information (TSCI) and computes inter-component statistics to determine proximity information of an object, enabling task performance based on this information without the need for precise localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing wireless proximity sensing technologies are used, then motion detection can be performed, but accurate detection of motion proximity cannot be achieved without complex setup or high precision localization
Solution Approach 1:
The patent extracts and processes only the essential channel information components (CI components) from the full wireless channel data. By identifying and analyzing specific statistical characteristics of these components, the system achieves proximity detection without requiring complex setup or high precision localization infrastructure.
Solution Approach 2:
The system transforms wireless channel information into time series data and computes statistical parameters (inter-component statistics) to determine proximity. This parameter transformation enables proximity detection using standard wireless communications, eliminating the need for specialized hardware or complex deployment.
2Productivity
If existing wireless proximity sensing technologies are used, then motion detection can be performed, but efficient recognition of motion within specific ranges is not achieved
Solution Approach 1:
The patent segments the wireless channel information into multiple CI components, each associated with an index. By analyzing the statistical relationships between these segmented components, the system efficiently determines motion proximity and enables targeted automation responses without processing the entire channel data set.
Solution Approach 2:
The system introduces time series channel information (TSCI) and inter-component statistics as intermediary representations that bridge raw wireless channel data and proximity detection. This intermediary layer enables efficient computation of proximity information, allowing the system to recognize motion within specific ranges and trigger appropriate automation tasks.
3Measurement precision
If precise localization is required for motion detection, then accurate position information can be obtained, but complex setup and high precision localization infrastructure are needed
Solution Approach 1:
The system uses the wireless channel information itself to provide proximity detection capabilities. By analyzing the statistical characteristics of channel components, the system self-determines proximity without requiring external localization infrastructure such as cameras, ultrasonic sensors, or complex antenna arrays.
Solution Approach 2:
The patent leverages the existing wireless communication channel to serve dual purposes: data transmission and proximity sensing. This multi-functionality eliminates the need for dedicated localization hardware, as the same wireless infrastructure used for communication also provides motion proximity detection capabilities.
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
Enables efficient detection and recognition of motion proximity, allowing for effective automation in smart home systems and health care applications by accurately determining the proximity of moving objects without complex setup or high precision localization.
Implementation Method 1
The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a movement of an object in the venue
Data Source
AI summary
Methods, apparatus and systems for wireless proximity sensing are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a movement of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises a plurality of CI components, each of which is associated with an index; computing an inter-component statistics based on the plurality of CI components; computing, based on the inter-component statistics, a proximity information of the object with respect to a reference location in the venue; and performing a task based on the proximity information of the object.