UWB Radar Breathing Detection via Coupling Path Removal
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Solution Overview
Problem
Ultra-Wideband (UWB) communication devices face challenges in detecting human breathing patterns due to pulse spread caused by RF filtering, which masks target reflections and interferes with signal detection, especially in environments with static objects, leading to difficulties in distinguishing human targets from ghost reflections and stationary targets from moving ones.
Innovation Solution
The method involves transmitting UWB signals, removing coupling path data, performing drift compensation, detecting target peaks, pruning peaks, assigning confidence values, and extracting peaks with specified confidence to differentiate human targets from static objects and resolve ghost reflections, enabling the detection of breathing direction and signals. This is achieved by using channel impulse responses, range doppler maps, and specific filtering techniques to isolate human targets and their breathing patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UWB signals are transmitted to detect human breathing patterns, then breathing detection capability is improved, but pulse spread caused by RF filtering masks target reflections and interferes with signal detection
Solution Approach 1:
The patent extracts and removes the coupling path component from the received signal. By separating the coupling path data from the target reflection data, the harmful pulse spread effect is eliminated, allowing clear detection of breathing patterns without interference from RF filtering artifacts.
Solution Approach 2:
The patent segments the signal processing into distinct steps: removing coupling path data, performing drift compensation, detecting target peaks, pruning peaks, and assigning confidence values. This segmentation allows each processing stage to address specific interference sources systematically.
2Difficulty of detecting and measuring
If signal processing is performed to detect moving targets, then moving target detection capability is improved, but stationary objects create ghost reflections that interfere with target identification
Solution Approach 1:
The patent implements a feedback mechanism through iterative peak pruning and confidence value assignment. Detected peaks are evaluated, and false peaks from ghost reflections are pruned based on confidence thresholds, continuously refining target identification accuracy.
Solution Approach 2:
The patent changes parameters by assigning confidence values to detected peaks and using these values to filter results. By adjusting confidence thresholds, the system can dynamically control the balance between detecting weak moving targets and rejecting ghost reflections from stationary objects.
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 method effectively detects human targets and their breathing patterns by removing clutter and drift, allowing for accurate identification and tracking of breathing signals even in environments with static objects, improving the resolution of multiple human targets and reducing interference from stationary and moving reflections.
Implementation Method 1
Ultra-Wideband (UWB) communication devices face challenges in detecting human breathing patterns due to pulse spread caused by RF filtering
Implementation Method 2
The method involves transmitting UWB signals, removing coupling path data, performing drift compensation, detecting target peaks, pruning peaks, assigning confidence values, and extracting peaks with specified confidence to differentiate human targets from static objects and resolve ghost reflections, enabling the detection of breathing direction and signals
Data Source
AI summary
A method for operating a UWB communication unit (100), comprising the steps:a) transmitting a UWB signal and receiving channel-impulse-responses, CIR;b) removing of data concerning a coupling path between TX and RX and performing a drift compensation of the data concerning the coupling path;c) detection of target peaks;d) pruning of peaks in a peak list with target peaks;e) assigning a confidence value to selected peaks and adapting a confidence value of the selected peaks; andf) extracting at least one peak concerning a human target (T1, T2) out of the list of peaks which has a specified confidence value after completion of step e).