Indoor Mobile Device Location Using Pseudo Access Points
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mobile device location estimation technologies face challenges in achieving accurate indoor location determination without Global Navigation Satellite System (GNSS) signals, as GNSS signals are often weak or unavailable indoors, and traditional RF signal-based methods require GPS location tags, which are not always feasible.
Innovation Solution
The method involves constructing a pseudo-AP map using indoor floor maps and WLAN access point locations, estimating mobile device location using actual and pseudo-APs, and applying weighting factors and Kalman Filtering for improved accuracy, allowing for indoor navigation without GNSS signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RF signal-based location estimation methods are used indoors, then location estimation can be performed without GNSS signals, but location accuracy deteriorates due to signal reflection and obstruction
Solution Approach 1:
The patent creates a virtual copy of the physical environment by generating a 3D model of the indoor space using sensor data (depth cameras, LiDAR, ultrasonic sensors). This virtual copy includes virtual access points positioned throughout the modeled environment, allowing the system to perform location estimation by comparing received signal strengths against the pre-modeled virtual environment, thereby achieving accurate indoor positioning without direct GNSS signals
Solution Approach 2:
The patent performs preliminary actions by pre-modeling the indoor environment and pre-positioning virtual access points before actual location estimation occurs. The system collects sensor data during an initial survey phase to build the 3D model and establish the virtual AP locations, so that during normal operation the system can quickly compare current signal readings against the pre-prepared virtual environment without requiring real-time complex processing
2Measurement precision
If multiple sensor types and processing algorithms are integrated, then location estimation accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the location estimation system into distinct functional modules: sensor data collection (depth cameras, LiDAR, ultrasonic sensors), environment modeling (3D space construction), virtual access point generation, signal strength measurement, and location calculation. Each module handles a specific aspect of the overall task, allowing the system to process information in manageable stages and reduce computational complexity while maintaining high accuracy through the integration of multiple sensor types
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 approach enhances indoor location estimation accuracy to meet FCC requirements, using commonly available RF signals and additional user equipment sensors, and can be adapted for outdoor environments with minimal modifications.
Implementation Method 1
measuring a strength of RF signals from a plurality of access points
Data Source
AI summary
The present disclosure is directed towards mobile device location estimation, and more particularly, to indoor mobile device location estimation. One computer-implemented method includes using indoor floor map, indoor wireless local area network (WLAN) access point (AP) location map and constructing a pseudo-AP map associated with an indoor location, wherein the pseudo-AP map includes at least one pseudo-AP, each pseudo-AP a function of at least a radio-frequency (RF) signal strength indication (RSSI) associated with at least one actual AP associated with the indoor location; estimating a mobile device location within the boundaries of the indoor location using at least one actual AP associated with the indoor location, and at least one pseudo-AP from the pseudo-AP map associated with the indoor location; and using the estimated mobile device location for indoor navigation within the indoor location.