In-Vehicle UWB Key Fob Localization Using CIR Classification
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Solution Overview
Problem
Ultra-wideband (UWB) technologies face challenges in accurately localizing a key fob inside a vehicle due to environmental conditions such as dense multipath environments, which lead to errors in distance and position estimation caused by signal reflections and interference.
Innovation Solution
Combining machine learning techniques with UWB radio devices to analyze channel impulse response (CIR) features, allowing for accurate localization of a key fob within a vehicle cabin by distinguishing direct and reflected signals, even in dense multipath conditions, using a classification algorithm that processes both CIR data and distance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UWB distance estimation algorithms (ToA, ToF, TDoA, AoA) are used for key fob localization, then positioning capability is provided, but measurement precision deteriorates due to environmental conditions and multipath effects
Solution Approach 1:
The patent introduces an artificial neural network (ANN) as an intermediary between the raw UWB signal measurements and the final localization decision. The ANN processes the channel impulse response (CIR) features and learned distance measurements to classify the key fob location, effectively mediating the harmful effects of multipath interference by learning to distinguish direct paths from reflected paths in the CIR data
Solution Approach 2:
The patent transforms the localization problem from direct distance estimation to a classification problem by changing the output parameter from continuous distance values to discrete location categories (e.g., driver seat, passenger seat, trunk). This parameter change allows the system to leverage the ANN's ability to handle uncertainty and provide reliable localization even when precise distance measurement is compromised by environmental factors
2Measurement precision
If machine learning classification process is applied to CIR features, then localization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the artificial neural network offline with labeled CIR data from various locations. This preliminary training phase allows the ANN to learn the characteristic patterns of CIR features for different locations before deployment. During actual operation, the pre-trained ANN requires only inference rather than full training, significantly reducing the computational complexity and processing time needed in the vehicle system while maintaining high localization accuracy
Solution Approach 2:
The patent replaces traditional signal processing and geometric localization methods with a data-driven machine learning approach. Instead of relying on complex mathematical models of signal propagation and geometric calculations that are sensitive to environmental variations, the system substitutes these with an ANN that learns the mapping from CIR features to locations directly from training data, simplifying the operational complexity while improving robustness
Data Source
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AI summary
A system for localizing an ultra-wide band (UWB) apparatus comprises a UWB transceiver that identifies and extracts features of at least one channel impulse response (CIR); and a special-purpose processor that applies a machine learning classification process to the extracted CIR features to localize the UWB apparatus in a vehicle.