UWB NLOS Signal Recognition via First Path CIR Peak Filtering
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Solution Overview
Problem
Existing UWB positioning technologies face challenges in accurately distinguishing between Line of Sight (LOS) and Non-Line of Sight (NLOS) scenarios, leading to inaccurate distance measurements and reduced positioning accuracy.
Innovation Solution
A UWB NLOS signal recognition method based on the first path of the Channel Impulse Response (CIR), which involves constructing a UWB ranging system, processing raw CIR data, performing peak filtering, identifying the first path peak, and using machine learning models like SVM or BP neural networks for recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification algorithms (SVM, Decision Trees) or machine learning methods are used to identify NLOS signals, then NLOS recognition capability is improved, but model complexity increases and portability becomes difficult
Solution Approach 1:
The patent extracts only the essential feature - the first path arrival time from CIR data - and uses it directly for NLOS recognition. By taking out the core distinguishing feature (first path signal characteristics) and discarding complex model structures, the solution achieves reliable NLOS recognition while maintaining simplicity and portability.
Solution Approach 2:
Instead of using complex models to extract features from CIR data, the patent inverts the approach by directly utilizing the first path arrival time feature and comparing it against threshold values. This inversion from complex feature extraction to simple threshold-based decision-making resolves the contradiction between recognition accuracy and model complexity.
2Speed
If the first path arrival time is used for distance calculation, then positioning speed is improved, but NLOS interference causes measurement errors and reduces positioning accuracy
Solution Approach 1:
The patent performs preliminary NLOS detection by analyzing the first path arrival time characteristics before final distance calculation. By detecting NLOS conditions in advance and flagging affected measurements, the system can then choose alternative positioning methods or discard inaccurate data, thus maintaining both speed and accuracy.
Solution Approach 2:
The first path arrival time feature serves as an intermediary that mediates between speed and accuracy requirements. By using this feature for both rapid NLOS detection and subsequent distance calculation (when LOS is confirmed), the patent achieves fast positioning while maintaining measurement precision through conditional processing.
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 method improves the recognition accuracy of NLOS signals, enhances the utilization of raw data, and reduces model complexity, leading to more reliable UWB positioning systems.
Implementation Method 1
UWB transmits information through ultra-narrow pulse signals, with bandwidth reaching the GHz level
Implementation Method 2
The arrival time of this first path is used as the ToA to calculate the straight-line distance between the transmitter and receiver
Implementation Method 3
Since the dielectric constant of the obstacle is always greater than that of air, the propagation speed of electromagnetic waves is slower than assumed
Data Source
AI summary
The invention discloses an UWB NLOS signal recognition method based on the first path of CIR, including constructing a UWB ranging system comprising tags and anchors. The system controls communication between the anchor and tag, processes the raw CIR data obtained from each communication to construct data samples, and labels these samples to build a raw CIR dataset. Peak filtering is then performed on the CIR waveforms in the raw CIR dataset to identify the first path peak points of the data samples. Based on the first path peak points, valid data is determined as new data samples, and one-hot encoding is applied to the data labels. A training dataset is constructed using all new data samples and their corresponding data labels. A machine learning model is then developed and trained using the training dataset, and the trained model is saved for identifying unknown CIR signals.


