Radar Signal Semantic Parameter Extraction via Velocity Envelope Detection
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Solution Overview
Problem
Existing methods for extracting semantic parameters from radar signals, such as human gait analysis, are limited in effectively isolating and processing time-dependent velocity data to obtain accurate and interpretable information about objects, particularly in distinguishing between different human activities like walking, biking, or standing.
Innovation Solution
The method involves determining an envelope of the time-dependent velocity data, which can be done directly in the time domain or through Fourier transformation, and processing it to extract semantic parameters like minimum and maximum velocities, repetition frequencies, and phases, allowing for the separation of useful information from noise and enabling visualization using human body models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Fourier transformation is applied to determine cadence frequencies, then frequency analysis capability is improved, but computational complexity increases
Solution Approach 1:
The patent applies envelope detection before frequency analysis to pre-process the velocity data and extract the dominant frequency components. This preliminary action simplifies the subsequent Fourier transformation by reducing the data complexity and focusing on the relevant frequency range, thereby improving measurement precision while controlling computational complexity.
2Loss of information
If envelope detection is applied to velocity data, then information extraction capability is improved, but processing complexity increases
Solution Approach 1:
The patent segments the velocity data into distinct phases (e.g., swing phase and stance phase) and applies envelope detection to each segment separately. This segmentation allows for more precise extraction of semantic parameters from different motion phases while managing processing complexity by dividing the overall task into smaller, more manageable sub-tasks.
3Measurement precision
If velocity data segmentation is applied to separate useful information from noise, then signal-to-noise ratio is improved, but processing time increases
Solution Approach 1:
The patent changes the parameter representation by transforming velocity data into envelope amplitude and phase parameters. This parameter transformation consolidates the useful information into fewer, more meaningful parameters while filtering out noise, thereby improving signal-to-noise ratio and reducing the dimensionality of the data that requires further processing.
Data Source
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AI summary
The invention relates to a method for extracting a semantic parameter of an object from a radar signal. The method comprises the step of retrieving time dependent velocity data of the object from the radar signal. Further, the method comprises the steps of determining an envelope of the time dependent velocity data of the object and processing the envelope for obtaining a semantic parameter of the object.