Wireless Sensing via Time-Frequency Channel Matrix Statistics
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Solution Overview
Problem
Current communication systems lack a clear solution for implementing sensing functions in scenarios such as intrusion detection and trajectory tracking, despite their ability to sense information about target objects through radio signals.
Innovation Solution
A sensing method and apparatus that utilize time-frequency domain channel matrices to obtain sensing measurement results based on time domain variance, standard deviation, or coefficient of variation, allowing for the extraction of information about target objects' positions and velocities, enabling wireless sensing functions in intrusion detection and trajectory tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If communication systems use radio signals for sensing, then sensing information about target objects can be obtained, but clear solutions for implementing sensing functions in specific scenarios such as intrusion detection and trajectory tracking are lacking
Solution Approach 1:
The patent applies multi-functionality by enabling communication systems to perform both communication and sensing functions using the same radio frequency signals and channel matrices. The time-frequency domain channel matrix obtained for communication purposes is reused for sensing measurements, allowing the system to detect target objects, track trajectories, and monitor intrusions without requiring separate dedicated sensing hardware or signal paths.
Solution Approach 2:
The system performs self-service by utilizing its own transmitted signals and received channel state information for sensing purposes. The communication device's transmitted radio signals serve dual purposes: establishing communication links and providing the probe signals necessary for sensing target objects. The channel matrices obtained during communication naturally contain the information needed for sensing measurements, eliminating the need for external sensing infrastructure.
2Measurement precision
If time-frequency domain channel matrices are used for sensing measurements, then sensing precision can be improved, but calculation complexity increases
Solution Approach 1:
The patent extracts the necessary sensing information from the time-frequency domain channel matrix by calculating statistical measures such as variance, standard deviation, or coefficient of variation of the channel coefficients. Instead of processing the entire complex channel matrix, the method extracts specific statistical features that capture the essential sensing information while significantly reducing computational burden. This extraction approach maintains measurement precision by focusing on the most relevant characteristics of the channel responses.
Solution Approach 2:
The patent transforms the raw channel matrix data into meaningful sensing measurements by changing the parameters from complex channel coefficients to statistical descriptors (variance, standard deviation, coefficient of variation). This parameter transformation converts the high-dimensional, complex channel state information into lower-dimensional real-valued metrics that are more suitable for sensing applications and reduce computational complexity while preserving the essential information about target object characteristics.
Data Source
AI summary
This application discloses a sensing method and apparatus, and a communication device. The sensing method includes: A first device obtains at least one sensing measurement result based on at least one of a time domain variance, standard deviation, or coefficient of variation of at least one time-frequency domain channel matrix, and the first device obtains a target sensing measurement result based on the at least one sensing measurement result.