Wireless Sensing Clustering for Motion Detection
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Solution Overview
Problem
Wireless sensing technologies face challenges in distinguishing between changes in a wireless communication channel caused by motion and changes due to other factors, such as noise or transmit parameter adjustments, leading to reduced reliability and accuracy in motion detection.
Innovation Solution
Implementing a wireless sensing component that clusters channel estimates based on transmit parameter sets and uses machine learning algorithms to classify and differentiate between channel variations from motion and transmit parameter changes, enhancing the reliability and accuracy of wireless sensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless sensing is used to detect motion by monitoring channel changes, then motion detection capability is provided, but reliability and accuracy deteriorate due to inability to distinguish motion-induced changes from changes caused by noise or transmit parameter adjustments
Solution Approach 1:
The patent segments channel estimation measurements into multiple clusters based on transmit parameter sets. Each cluster corresponds to a specific transmit configuration, allowing the system to separately analyze channel changes within each cluster. This segmentation enables distinction between channel changes caused by transmit parameter adjustments (which would appear as transitions between clusters) and channel changes caused by motion (which appear as variations within a cluster), thereby improving reliability without requiring overly complex unified analysis.
2Measurement precision
If clustering of channel estimates is implemented to differentiate motion from transmit parameter changes, then accuracy of motion detection is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary clustering of channel estimation measurements according to transmit parameter sets before conducting motion detection analysis. By pre-organizing the data into clusters based on known transmit configurations, the system establishes a structured framework that simplifies subsequent motion detection. This preliminary action allows the system to quickly identify whether channel changes result from transmit parameter adjustments (by detecting cluster transitions) or from motion (by analyzing variations within clusters), improving measurement precision while managing processing complexity through staged analysis.
Data Source
AI summary
For example, a first wireless communication device may be configured to cluster a plurality of channel estimation measurements into a plurality of clusters based on a clustering criterion, the plurality of channel estimation measurements corresponding to a respective plurality of Physical Protocol Data Units (PPDUs) received from a second wireless communication device over a wireless communication channel; and, based on clustering of the plurality of channel estimation measurements into the plurality of clusters, selectively provide a clustered channel estimation measurement to be processed for detection of changes in an environment of the wireless communication channel, by providing the clustered channel estimation measurement together with one or more other clustered channel estimation measurements of a same cluster of the clustered channel estimation measurement to be processed for the detection of the changes in the environment.