Pulse Radar Motion Recognition Using Micro-Range Enhanced Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional CW radar-based motion recognition technologies are limited in accurately measuring human motion due to their inability to account for changes in distance, assuming all motions occur in a single plane, which is not reflective of the 3D nature of human movement.
Innovation Solution
A method and apparatus using pulse radar signals to enhance micro-range signals by imaging and reinforcing components of motion, utilizing a machine learning-based learning model to determine target motion by analyzing changes in distance and motion intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CW radar-based motion recognition technology is used, then motion recognition can be performed based on Doppler shift, but distance measurement capability is lost and motion accuracy deteriorates due to assuming all targets exist in one plane
Solution Approach 1:
The patent segments the radar signal processing into two distinct components: range dimension processing and velocity dimension processing. By separating these dimensions, the system can independently analyze distance changes and motion velocity, thereby recovering the distance information that was lost in conventional CW radar systems while maintaining motion recognition capabilities.
Solution Approach 2:
The patent transitions from the single-dimensional Doppler shift analysis of CW radar to a two-dimensional analysis by introducing the range dimension. This dimensional expansion allows the system to capture both distance and velocity information simultaneously, enabling accurate 3D motion recognition while eliminating the planar assumption limitation of conventional methods.
2Device complexity
If CW radar assumes all targets exist in one plane, then processing is simplified, but motion recognition accuracy deteriorates due to ignoring 3D space distance changes
Solution Approach 1:
The patent divides the complex 3D motion analysis into separate range and velocity processing segments. This segmentation allows the system to handle 3D spatial information without overwhelming computational complexity, as each dimension can be processed independently using specialized algorithms.
Solution Approach 2:
By adding the range dimension to the traditional velocity dimension, the patent creates a two-dimensional signal space that naturally captures 3D motion information. This dimensional transformation enables accurate motion detection in three-dimensional space while maintaining manageable processing complexity through established radar signal processing techniques.
3Loss of information
If pulse radar is used instead of CW radar, then distance measurement capability is restored, but signal processing complexity increases due to need for micro-range signal enhancement
Solution Approach 1:
The patent transforms the one-dimensional pulse radar signal into a two-dimensional representation by incorporating both range and velocity dimensions. This dimensional expansion enables the extraction of micro-range signals that contain motion information, recovering distance measurement capability while providing a structured framework for processing that manages the inherent complexity.
Solution Approach 2:
The patent introduces an intermediary processing stage that enhances micro-range signals by exploiting the correlation between range and velocity dimensions. This intermediary step acts as a bridge between raw pulse radar data and motion recognition results, filtering and enhancing relevant signals while suppressing noise and redundant information.
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
Accurately recognizes various human motions by incorporating distance changes and motion intensity, enhancing the precision of motion detection beyond conventional CW radar limitations.
Implementation Method 1
receiving a plurality of frame signals based on radar pulses reflected from a target at different times
Implementation Method 2
micro-Doppler technology recognizes motion based on a feature in which a Doppler value generated from an arm, a leg, a torso, a head, etc. changes according to human motion
Data Source
AI summary
A motion recognition method may include receiving a plurality of frame signals based on radar pulses reflected from a target at different times, generating a plurality of micro-range enhanced frame signals obtained by reinforcing a component of a second region having more movement than movement of a first region among the plurality of frame signals, generating a micro-range enhanced frame set formed by stacking the plurality of micro-range enhanced frame signals at preset time intervals, generating a micro-range enhanced radar image which is an image obtained by viewing the micro-range enhanced frame set in a direction perpendicular to a time axis of the micro-range enhanced frame set, and determining motion of the target by inputting the micro-range enhanced radar image to a machine learning-based learning model.