Radar Subject Identification via Range-Doppler Analysis
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Solution Overview
Problem
Existing identification systems relying on vision technology face challenges such as occlusion, poor lighting conditions, and limited information from visible features, making them less reliable for identifying living subjects, especially in natural conditions or among individuals with similar facial features.
Innovation Solution
The use of range and velocity data from radar sensors to identify living subjects and activities, processed by a computation engine to generate range-doppler images and trained using machine learning systems, allowing for reliable identification regardless of occlusion or lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vision technology such as cameras is used to capture images of visible features, then identification can be performed, but reliability deteriorates under occlusion, poor lighting conditions, and limited visible features
Solution Approach 1:
The patent replaces vision technology (optical system) with radar technology (electromagnetic wave system) for identification. The radar sensor emits electromagnetic waves and processes reflected signals to obtain range and velocity data, which are then used to generate range-Doppler images for identification. This substitution eliminates dependence on visible light and visual features, thereby resolving the reliability issues caused by occlusion and poor lighting conditions.
2Reliability
If facial features are used for identification, then identification can be performed, but reliability deteriorates when individuals have similar facial features
Solution Approach 1:
The patent changes the identification parameters from static visual features (facial features) to dynamic motion parameters (range and velocity data). By capturing how subjects move through space over time and analyzing their velocity patterns in range-Doppler images, the system obtains discriminatory information that distinguishes individuals with similar appearances, thereby improving identification reliability.
3Productivity
If camera-based vision systems are used, then identification can be performed, but performance deteriorates in natural conditions with varying lighting
Solution Approach 1:
The patent substitutes camera-based optical detection with radar-based electromagnetic wave detection. The radar sensor actively emits electromagnetic waves and measures the reflected signals, obtaining range and velocity information independent of ambient lighting. This allows consistent identification performance in natural conditions without relying on sufficient illumination intensity.
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
This approach provides reliable identification of living subjects and activities under various conditions, including natural environments, and effectively differentiates between individuals with similar features by utilizing motion patterns and shape data from radar signals.
Implementation Method 1
a radar sensor configured to generate a time-domain or frequency-domain signal representative of electromagnetic waves reflected from one or more objects within a three-dimensional space
Implementation Method 2
The computation engine may process the signal generated by the radar sensor to generate two-dimensional range-doppler images that indicate the range and velocity of objects
Data Source
AI summary
An identification system includes a radar sensor configured to generate a time-domain or frequency-domain signal representative of electromagnetic waves reflected from one or more objects within a three-dimensional space over a period of time and a computation engine executing on one or more processors. The computation engine is configured to process the time-domain or frequency-domain signal to generate range and velocity data indicating motion by a living subject within the three-dimensional space. The computation engine is further configured to identify, based at least on the range and velocity data indicating the motion by the living subject, the living subject and output an indication of an identity of the living subject.