FMCW Radar Range Validation Using Physiological Detection
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Solution Overview
Problem
Existing range estimation technologies face challenges in accurately determining the distance to a living subject without direct contact, particularly in differentiating between living entities and non-human objects, and in validating range estimates for reliable physiological parameter detection.
Innovation Solution
A method and system utilizing frequency-modulated continuous-wave (FMCW) radar signals, combined with fast Fourier transform (FFT) processing, to generate a range-time map (RTM) and calculate a range score signal (RSS), which is then validated using multiple criteria such as dynamic ratio, inter-quartile range, modified z-score, and signal peaks to establish a reliable range estimate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If FMCW radar signals are used for non-contact range determination, then the ability to measure distance without direct contact is improved, but the reliability of differentiating living entities from non-human objects deteriorates
Solution Approach 1:
The patent segments the range estimation process into multiple independent validation stages: initial range estimation from RTM, physiological parameter detection, and four distinct validity criteria checks (signal strength threshold, range stability threshold, physiological parameter presence, and target classification). This segmentation allows each aspect to be validated separately, improving overall reliability without compromising non-contact operation.
Solution Approach 2:
The system implements feedback mechanisms where physiological parameter detection results feed back into target validation, and validity criterion检查结果 feed back into final range estimate acceptance. The processor continuously monitors signal characteristics and adjusts validation thresholds based on detected physiological patterns, enhancing reliability while maintaining non-contact measurement capability.
2Reliability
If multiple validity criteria are applied to validate range estimates, then the reliability of range determination is improved, but the device complexity deteriorates
Solution Approach 1:
The patent applies preliminary filtering by implementing signal strength and range stability thresholds before more complex physiological analysis. This preliminary action eliminates obviously invalid measurements early in the processing chain, reducing the computational burden of subsequent validation steps while maintaining high reliability through progressive filtering.
Solution Approach 2:
The validation system dynamically adjusts processing intensity based on signal quality indicators. When signals meet basic thresholds, full multi-criteria validation is applied; when signals fail preliminary checks, the system skips to default range values without executing the complete validation algorithm suite, thereby reducing average computational complexity while preserving reliability for valid measurements.
3Reliability
If physiological parameter detection is used to verify living entities, then the ability to differentiate living persons from non-human entities is improved, but the measurement precision requirements deteriorate
Solution Approach 1:
The patent implements partial physiological parameter detection by monitoring only key vital signs (heart rate, respiratory rate) rather than comprehensive physiological analysis. This partial action approach provides sufficient evidence for living entity detection without requiring ultra-high precision measurement of all physiological parameters, thus improving reliability while managing precision requirements.
Solution Approach 2:
The system changes detection parameters dynamically by adjusting sensitivity thresholds and detection windows based on signal quality and measurement conditions. This allows the system to maintain reliable living entity detection across varying precision conditions, adapting to different measurement scenarios without requiring consistently high precision across all parameter sets.
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 solution provides a highly reliable and accurate method for determining the range to a living subject, effectively differentiating between living entities and non-human objects, and validating range estimates for precise physiological parameter detection without direct contact.
Implementation Method 1
a radar sensor can determine distance (and speed) of an object based on changes in the frequency or phase of the reflected radar signal caused by the Doppler effect
Implementation Method 2
a radar sensor can determine distance (and speed) of an object based on changes in the frequency or phase of the reflected radar signal caused by the Doppler effect
Data Source
AI summary
Radar-based range determination and validation. A reflected FMCW radar signal is received from a subject and sampled to generate sample vectors including signal samples for each frame of reflected radar signal. An FFT is applied to sample vectors to generate a range-time map (RTM) data matrix. An initial range estimate of subject is determined by: calculating a range score signal (RSS) by either: cross-multiplying a mean power per RTM range bin with a corresponding variance per range bin, or dividing variance per range bin with a zero-crossing per range bin to second exponent; identifying a maximum value index range bin having a maximum RSS value; and multiplying identified maximum value index range bin with a range bin spacing of range spectrum RSS. At least one physiological parameter is detected to verify that subject is a living entity. Range estimate is validated by determining if predetermined number of validity criteria met.