In-Vehicle UWB 3D Localization with KDE and Bayesian Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems are unable to accurately determine the 3D location of mobile devices, such as key fobs or smartphones, within a vehicle.
Innovation Solution
A UWB-based system utilizing UWB sensors and a controller to track the 3D location of mobile devices within a vehicle through Gaussian KDE and Bayesian estimation, incorporating UWB anchors and tags, and employing a 3D localization algorithm for precise positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UWB sensors are used for 3D localization of mobile devices in vehicles, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the localization task into multiple components: UWB sensors for distance measurement, anchors for spatial reference, and probabilistic algorithms (Gaussian KDE, Bayesian estimation) for position calculation. This segmentation allows each component to be optimized independently while achieving high overall precision
Solution Approach 2:
The patent introduces intermediate computational layers (Gaussian KDE for density estimation, Bayesian estimation for probability fusion) that mediate between raw sensor data and final position output. These intermediaries transform complex sensor measurements into reliable position estimates, resolving the contradiction between precision and complexity
2Measurement precision
If multiple UWB sensors are deployed to improve localization accuracy, then measurement precision is improved, but manufacturing precision requirements increase
Solution Approach 1:
The system employs dynamic probabilistic modeling that adapts to varying sensor configurations and placement conditions. The Gaussian KDE and Bayesian estimation continuously adjust position probabilities based on real-time sensor data, making the system robust to manufacturing variations in anchor and sensor placement
Solution Approach 2:
The patent changes the approach from fixed deterministic positioning to dynamic probabilistic positioning. By using probability distributions and estimation algorithms, the system can achieve high precision even when physical sensor placement has manufacturing tolerances, as the algorithms compensate for positional uncertainties
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
Achieves fine-grained 3D localization accuracy of mobile devices within a vehicle, with an error of 0.32 m using 4 sensors and 0.14 m using 6 sensors, enhancing vehicle technology for precise device tracking.
Implementation Method 1
receiving, in real time, sensor data from a plurality of UWB sensors inside a vehicle. The plurality of UWB sensors includes a plurality of UWB anchors each disposed in a fixed position inside the vehicle and at least one UWB tag
Data Source
AI summary
A method for in-vehicle localization of a mobile device includes receiving, in real time, sensor data from a plurality of UWB sensors inside a vehicle. The plurality of UWB sensors includes a plurality of UWB anchors and a UWB tag, which is part of the mobile device. The method further includes determining a plurality of location candidates of the UWB tag based on the sensor data received, determining a plurality of sensed current locations of the UWB tag and a plurality of probabilities for each of the plurality of sensed current locations of the UWB tag using a Gaussian Kernel Density Estimation (KDE), tracking a motion of the UWB tag, and determining a real-time position of the UWB tag using a Bayesian estimation.