Radar Living Being Detection via Doppler Pattern Classification
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Solution Overview
Problem
Current radar-based systems for detecting living beings often suffer from false alarms due to the evaluation of movement alone, which can be triggered by small animals, technical equipment, and plants, leading to inefficiencies in systems like intruder alarms and security sensors.
Innovation Solution
A method and device utilizing radar beams to classify properties of detected objects by comparing radar data with characteristics of known living beings, allowing for the automatic determination of the class or type of living being, such as humans, animals, or insects, through the analysis of movement patterns, vital functions, and body dimensions, using Doppler signals and electromagnetic waves in the MHz to GHz range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radar systems evaluate only Doppler signals for movement detection, then the system is simple and cost-effective, but false alarm rate increases due to detection of small animals, insects, and moving objects
Solution Approach 1:
The patent segments the detection process into multiple independent evaluation stages: initial movement detection via Doppler signal, followed by classification of movement patterns, and finally identification of living beings based on characteristic movement signatures. This segmentation allows the system to maintain simplicity while reducing false alarms by progressively filtering out non-living objects.
Solution Approach 2:
The patent adds a new dimension to movement detection by analyzing not just the presence of movement (Doppler signal) but also the characteristics of movement patterns. By evaluating multiple parameters such as movement regularity, speed variations, and pattern recognition, the system distinguishes living beings from inanimate objects without requiring complex hardware upgrades.
2Measurement precision
If video cameras are used for living being identification, then identification accuracy is high, but system cost increases significantly
Solution Approach 1:
The patent replaces the mechanical/optical system of video cameras with an electromagnetic wave-based radar system. By using Doppler effect and movement pattern analysis, the system achieves living being identification without requiring visual capture, thereby reducing hardware costs while maintaining identification capability through alternative physical principles.
Solution Approach 2:
The patent changes the detection parameter from optical information (video) to electromagnetic wave reflection characteristics (Doppler signals and movement patterns). This parameter change enables the use of cheaper radar technology instead of expensive video systems, while still achieving accurate living being identification through analysis of movement characteristics.
3Productivity
If radar systems detect all moving objects, then detection coverage is comprehensive, but false triggering increases due to small animals and environmental factors
Solution Approach 1:
The patent applies preliminary classification of movement patterns before triggering alarms. By first detecting movement and then evaluating whether the movement pattern matches characteristics of living beings, the system maintains comprehensive detection coverage while preventing false triggering from small animals and environmental factors through advance pattern recognition.
Solution Approach 2:
The patent implements feedback mechanisms where detected movement patterns are continuously evaluated against stored characteristics of living beings. The system learns from detected patterns and adjusts its classification, providing feedback that improves accuracy over time while maintaining comprehensive detection coverage and reducing false alarms.
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 reduces false alarm rates by accurately identifying living beings and distinguishing them from inanimate objects, enabling effective human recognition and classification, even behind obstacles, while being more cost-effective than video systems and allowing for penetration through fixed targets.
Implementation Method 1
detecting a living being, in particular a human being, comprises detecting an object by means of radar beams and evaluating radar data on the object detected
Implementation Method 2
The simplest radar principle is the CW (continuous wave) radar. With this radar principle, a Doppler signal generated by a moving object is evaluated and a relative or radial speed of the object is determined
Implementation Method 3
enabling effective human recognition and classification, even behind obstacles, while being more cost-effective than video systems and allowing for penetration through fixed targets
Data Source
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AI summary
The invention relates to a method for detecting a living being, wherein an object (12) is detected by means of radar beams, and wherein the radar data associated with the detected object (12) is evaluated such that at least one property of the object (12) is classified and compared to characteristics for at least one known living being, such that it is determined which class of living being is the object (12). The invention further relates to a device for detecting living beings, to a computer program and to a computer program product.