UWB Attack Detection via Channel Quality Feedback
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Solution Overview
Problem
Ultra-wideband (UWB) secure ranging systems face challenges in accurately determining distance due to interference and noise, particularly in High-Rate Pulse (HRP) mode, which can be affected by noise and channel artifacts, leading to misclassification of peaks and reduced accuracy.
Innovation Solution
The system generates a Secure Training Sequence (STS) Confidence Level Figure of Merit (CLFOM) and Attack-Detection Figure of Merit (A-D FOM) to assess channel quality and detect potential attacks, adjusting secure ranging parameters based on learned channel characteristics and geographic location to enhance accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If High-Rate Pulse (HRP) mode is used for UWB secure ranging, then ranging speed and update rate are improved, but the system becomes more susceptible to noise and channel artifacts causing peak misclassification
Solution Approach 1:
The system implements feedback by computing a Channel Quality Indicator (CQI) based on correlation output analysis and using it to dynamically adjust secure ranging parameters. The CQI is calculated by analyzing the correlation output between received signals and reference templates, then this quality metric feeds back to adjust parameters like search window size and peak detection thresholds, creating a closed-loop system that adapts to changing channel conditions and reduces peak misclassification in HRP mode
Solution Approach 2:
The patent applies dynamics by making the secure ranging parameters adaptive rather than fixed. The system dynamically adjusts parameters based on real-time channel quality assessment, allowing the ranging algorithm to optimize its behavior according to current noise levels and interference conditions. This dynamic adaptation enables reliable peak detection across varying operational environments while maintaining high ranging speed
2Measurement precision
If secure ranging parameters are made adaptive based on channel conditions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses feedback through CQI computation to automatically adjust parameters based on measured channel quality. The CQI is derived from correlation output analysis, and this feedback loop enables the system to self-optimize measurement precision without requiring complex external control mechanisms. The feedback-driven adaptation simplifies the overall system architecture compared to manually tuned complex algorithms
Solution Approach 2:
The ranging system performs self-service by automatically assessing channel quality and adjusting its own parameters without external intervention. The device computes CQI from its own correlation outputs and uses this information to adaptively tune its secure ranging parameters, enabling autonomous optimization of measurement precision while avoiding the complexity of external parameter management systems
Data Source
AI summary
Systems, methods, and circuitries are provided for determining the likelihood that a malicious signal component is present in a UWB signal. In one example, a UWB receiver device is configured to receive a UWB signal during a ranging round; correlate the received UWB signal with a reference STS template to generate a correlation output; based on the correlation output, compute a channel quality indicator that characterizes a noise level of the channel; compute an attack-detection figure of merit (A-D FOM) based on the channel quality indicator, a block error of the received UWB signal, or a frequency error of the received UWB signal; and provide data indicative of the A-D FOM to a controller device.