Vehicle Trunk Access Radar for Human Foot Motion Detection
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Solution Overview
Problem
Current systems for controlling access to vehicle trunks are susceptible to electromagnetic compatibility disturbances, false alarms, and environmental conditions such as rain, dust, and low light, leading to inefficiencies and inaccuracies in detecting human presence and intent.
Innovation Solution
A millimeter-wave radar sensor system that transmits and receives RF signals to detect human foot motion patterns, using Gabor time-frequency transforms and machine learning algorithms to filter and process data, thereby distinguishing human signatures from environmental interferences and reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional access control systems are used, then the system is simple to implement, but the system is susceptible to electromagnetic compatibility disturbances and environmental conditions
Solution Approach 1:
The patent replaces conventional mechanical or simple electronic access control systems with a millimeter-wave radar-based detection system. This substitution enables the system to detect human presence and intent through radio wave reflection, providing immunity to electromagnetic compatibility disturbances and environmental conditions while maintaining reliability.
Solution Approach 2:
The patent utilizes the specific parameters of millimeter-wave radar signals (frequency, wavelength, penetration characteristics) to detect human presence. By operating in the millimeter-wave spectrum and analyzing signal reflections, the system achieves reliable detection that is unaffected by visible light conditions, thereby resolving the contradiction between reliability and environmental sensitivity.
2Measurement precision
If radar systems with multiple antennas and MIMO configuration are used, then directional beams and signal processing capability are improved, but device complexity increases
Solution Approach 1:
The patent divides the detection task into distinct processing stages: signal reception, range-gate determination, slow-time data capture, Gabor transform application, and trajectory analysis. This segmentation allows each component to be optimized independently, achieving high measurement precision without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces a Gabor transform as an intermediary processing step between raw radar data and trajectory determination. This time-frequency transform acts as a mediator that extracts relevant motion characteristics while filtering out noise and irrelevant signals, thereby improving measurement precision without requiring excessive hardware complexity.
3Reliability
If environmental filtering is applied, then false alarms are reduced, but processing time increases
Solution Approach 1:
The patent applies preliminary filtering through the Gabor transform before full trajectory analysis. This preliminary action identifies and filters out environmental interferences and non-human motions early in the processing chain, reducing false alarms while minimizing the time required for subsequent detailed analysis.
Solution Approach 2:
The patent implements partial environmental filtering by focusing specifically on the characteristics of human motion patterns rather than attempting to filter all possible environmental factors. This selective approach reduces processing time while still achieving reliable false alarm reduction for the intended application.
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
The system provides robust and accurate detection of human foot signatures, reducing false alarms and operating effectively in various environmental conditions, allowing for reliable control of trunk access while minimizing interference from other objects or environmental factors.
Implementation Method 1
receiving radar data at a millimeter-wave radar sensor, the radar data being generated in response to an incident radio-frequency signal reflecting off an object
Implementation Method 2
performing a Gabor time-frequency transform on the slow-time radar data to generate the first-filtered signal
Data Source
Figure 1
Figure 2A
Figure 2B~2C
AI summary
An embodiment method includes: receiving radar data at a millimeter-wave radar sensor, the radar data being generated in response to an incident radio-frequency signal reflecting off an object located in a field of view of the millimeter-wave radar sensor; filtering the radar data to generate a first-filtered signal; determining a trajectory of motion corresponding to the first-filtered signal; and determining whether the trajectory of motion corresponds to a human signature, the human signature being associated with a respective operation of a vehicle.