Stepped-Frequency Radar Moving Entity Detection

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Solution Overview

Problem

Current detection methods fail to effectively identify moving entities, such as people, concealed behind walls, due to limitations in visual recognition and existing radar technologies, which struggle to differentiate between moving objects and stationary ones or distinguish between direct and indirect reflections.

Innovation Solution

A method involving stepped-frequency radar signals transmitted through barriers to differentiate between multiple detections by applying fuzzy c-means clustering and analyzing parameters like range, Doppler values, and angle of arrival, allowing for the identification and tracking of moving objects behind walls.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Difficulty of detecting and measuring

If traditional radar signals are transmitted through a barrier, then the ability to detect objects on the other side is improved, but the ability to differentiate between moving objects and stationary objects deteriorates

Engineering Contradiction:
Improvedetection capability through barrierVSAvoiddifferentiation between moving and stationary objects
Core Design Contradiction:
Difficulty of detecting and measuringVSMeasurement precision

Solution Approach 1:

The patent segments the detection process into multiple independent parameters (range, Doppler, angle of arrival) and uses fuzzy c-means clustering to process these segmented measurements. This allows the system to distinguish between moving and stationary objects by analyzing multiple parameter dimensions simultaneously, resolving the contradiction between detecting objects through barriers and precisely differentiating their motion states.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent introduces additional measurement dimensions (range, Doppler shift, angle of arrival) beyond simple detection. By transforming the detection problem into a multi-dimensional parameter space and applying fuzzy logic clustering, the system achieves precise differentiation between moving and stationary objects while maintaining barrier penetration capability.

Inventive Principle:
Principle #17Another dimension (Dimensionality change)

2Ease of operation

If visual recognition is used to identify objects, then the ease of operation is improved, but the ability to detect concealed objects deteriorates

Engineering Contradiction:
Improvevisual recognitionVSAvoiddetection of concealed objects
Core Design Contradiction:
Ease of operationVSDifficulty of detecting and measuring

Solution Approach 1:

The patent uses radar signals as an intermediary medium to detect objects concealed behind barriers. Instead of relying on direct visual recognition which is blocked by walls, the radar signals penetrate the barrier and interact with objects on the other side, providing a non-line-of-sight detection capability that resolves the contradiction between ease of visual operation and ability to detect concealed objects.

Inventive Principle:
Principle #24Intermediary (Mediator)

3Quantity of substance

If multiple detections are made by radar, then the quantity of information is improved, but the complexity of processing and identifying accurate detections deteriorates

Engineering Contradiction:
Improvenumber of detectionsVSAvoidprocessing complexity
Core Design Contradiction:
Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent employs fuzzy c-means clustering algorithms that automatically organize and classify multiple radar detections without requiring complex manual processing. The algorithm self-organizes the detection data into coherent groups, identifying accurate detections and filtering false alarms automatically, thus reducing processing complexity while maintaining high information quantity.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The patent transforms multiple raw radar detections into processed parameters (range, Doppler, angle) and uses fuzzy logic to reclassify them. This parameter transformation and reclassification process simplifies the identification of accurate detections from multiple raw measurements, reducing processing complexity while preserving detection quality.

Inventive Principle:
Principle #35Parameter changes

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

Enables accurate detection and tracking of moving entities, distinguishing between moving persons and stationary objects, even when visual detection is obstructed, by processing reflected signals to determine their presence and motion behind walls.

Implementation Method 1

transmitting a stepped-frequency radar signal through a barrier

Methodology Applied
Scientific EffectRadar: Radar

Implementation Method 2

sensing a signal including a reflection of the transmitted signal from the first object and a reflection of the transmitted signal from the second object

Methodology Applied
Scientific EffectReflection: Reflection

Implementation Method 3

The first detection and the second detection may include multiple detections. The first detection associated with the first object may include multiple detections, and the second detection associated with the second object may include multiple detections

Methodology Applied
Scientific EffectDoppler effect: Doppler Effect

Data Source

PatentEP2947476B1Moving entity detection
Publication Date: 2018.08.15 L 3 COMM CORP
  • EP2947476B1 patent drawingFigure 1A
  • EP2947476B1 patent drawingFigure 1B
  • EP2947476B1 patent drawingFigure 2A~2B

AI summary

A stepped-frequency radar signal is transmitted through a barrier. A transmitter of the stepped-frequency radar is on a first side of the barrier, a first object is on a second side of the barrier, and a second object that is distinct from the first object is on the second side of the barrier. A signal including a reflection of the transmitted signal from the first object and a reflection of the transmitted signal from the second object is sensed. The sensed signal is analyzed to determine that a first detection is associated with the first object and a second detection is associated with a second object.