Human Body Tracking Radar for Moving-to-Stationary Detection
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Solution Overview
Problem
Conventional FMCW radar systems struggle with accurately distinguishing human bodies from static objects in dynamic environments, particularly when targets transition from moving to stationary states, leading to false detections and loss of tracking due to computationally expensive background subtraction or lack of environmental data.
Innovation Solution
A human body tracking device and method that utilizes a dual-path architecture, including a first detection unit for moving objects and a second detection unit for stationary bodies, leveraging amplitude and phase correlations to differentiate between human bodies and static objects, and a tracking processing unit to maintain continuous tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FMCW radar uses Doppler shift for target identification, then moving targets can be detected, but stationary targets cannot be distinguished from clutter
Solution Approach 1:
The processing unit segments the detection process into two independent paths: a first detection unit for moving objects using Doppler shift, and a second detection unit for stationary objects using amplitude and phase correlations. This segmentation allows each path to specialize in detecting specific target states without interference from the other, resolving the contradiction between detecting moving targets and distinguishing stationary targets from clutter.
Solution Approach 2:
The processing unit acts as an intermediary that receives signals from both detection units and synthesizes their outputs. It uses the first coordinates from moving object detection and second coordinates from stationary object detection to generate comprehensive tracking information, enabling the system to adapt to both moving and stationary targets effectively.
2Reliability
If background subtraction is used to handle zero-Doppler states, then stationary targets can be detected, but computational cost increases significantly
Solution Approach 1:
The invention extracts the essential detection function for stationary targets from the complex background subtraction process. Instead of performing computationally intensive background subtraction, the system uses a simplified second detection unit that directly detects stationary objects through amplitude and phase correlations, extracting only the necessary information for reliable detection without the computational burden of full background modeling.
3Measurement precision
If environmental information is defined for each location, then target identification improves, but system complexity and data requirements increase
Solution Approach 1:
The system performs self-service by automatically detecting target states through signal analysis without requiring external environmental databases. The second detection unit inherently identifies stationary objects through amplitude and phase correlations, eliminating the need for pre-defined environmental information for each location while maintaining accurate target identification.
4Measurement precision
If Doppler shift is used for tracking, then moving targets are tracked accurately, but tracking is lost when targets become stationary
Solution Approach 1:
The system dynamically adapts its detection strategy based on target motion state. The processing unit switches between using the first detection unit for moving targets and the second detection unit for stationary targets. This dynamic adaptation ensures continuous tracking capability by selecting the appropriate detection method according to the target's current state, preventing tracking loss when targets transition from moving to stationary.
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
Enhances tracking accuracy by distinguishing between moving and stationary human bodies, preventing false positives and maintaining continuous tracking through dynamic environments.
Implementation Method 1
Systems that detect a human body by using RAdio Detection And Ranging (RADAR)
Implementation Method 2
a reflected wave of a radio wave radiated from an antenna is analyzed so that a target can be identified
Implementation Method 3
a target is typically identified based on a Doppler shift caused by relative velocity
Data Source
AI summary
A human body tracking device and a human body tracking method capable of improving the accuracy in tracking of a human body are implemented. The device includes a transmitter/receiver that transmits a radio wave and receives a reflected wave of the transmitted radio wave, and a processor that estimates a location of a human body on the basis of an intermediate frequency (IF) signal output from the transmitter/receiver. The processor is configured to execute a first detection process that detects coordinates of a moving object as first coordinates, a second detection process that detects coordinates of at least the human body in a stationary state as second coordinates, and a tracking process that tracks the human body on the basis of the first coordinates and the second coordinates.