Location Estimation Using Dynamic Database Weighting for Indoor Accuracy
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Solution Overview
Problem
Existing location estimation technologies face challenges in accurately determining the location of terminals, especially in indoor environments, due to high signal propagation errors and differences in measurement information between collection and user terminals, leading to inaccuracies and the need for calibration and absolute positioning.
Innovation Solution
A location estimation apparatus and method that combines different positioning resources by generating a motion model and calculating weights based on dynamic location databases, correcting measurement information, and applying these weights to estimate the terminal's location and direction, thereby reducing errors and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS is used for location estimation, then high location accuracy is achieved in outdoor areas with direct line of sight, but location error rises to 50m or becomes impossible to determine location in indoor areas and NLOS environments
Solution Approach 1:
The patent combines multiple positioning resources (GNSS, cellular-based positioning, and Wi-Fi-based positioning) into a unified location estimation system. By merging these different positioning technologies, the system achieves high accuracy in outdoor areas while maintaining functionality in indoor and NLOS environments where individual systems would fail or provide poor accuracy.
Solution Approach 2:
The location estimation apparatus is designed to perform multiple positioning functions across different environments using a single integrated system. It can estimate locations in outdoor areas using GNSS, in urban areas using cellular-based positioning, and in indoor areas using Wi-Fi-based positioning, making it universally applicable across diverse scenarios.
2Adaptability or versatility
If cellular-based positioning technology is used, then location can be determined in indoor and outdoor areas, but location accuracy is relatively low with average error of 100 to 800m
Solution Approach 1:
The system merges cellular-based positioning with Wi-Fi-based positioning and GNSS. By combining these resources, the system maintains the broad coverage capability of cellular positioning while significantly improving accuracy through Wi-Fi measurements in indoor areas and GNSS in outdoor areas.
Solution Approach 2:
The system applies different positioning strategies to different locations and environments. In outdoor areas, it prioritizes GNSS for high accuracy; in urban areas, it uses cellular-based positioning; and in indoor areas, it switches to Wi-Fi-based positioning. This local adaptation optimizes accuracy for each specific environment.
3Measurement precision
If Wi-Fi-based positioning technology is used for indoor positioning, then precise location information is achieved, but direction information cannot be provided and calibration is required for each terminal type
Solution Approach 1:
The system combines Wi-Fi-based positioning with sensor-based positioning (using accelerometers, gyroscopes, and magnetometers). This integration provides direction information through sensor data while maintaining the precise location accuracy of Wi-Fi positioning, eliminating the need for separate direction measurement systems.
Solution Approach 2:
The patent introduces a measurement information correction database that acts as an intermediary to correct measurement information from collection terminals to match user terminals. This correction mechanism eliminates the need for individual terminal calibration by using the database to adjust for terminal-specific variations.
4Productivity
If measurement information from collection terminal is used to create location DB, then location estimation is possible, but errors occur due to differences between collection and user terminals requiring calibration
Solution Approach 1:
The patent introduces a measurement information correction database as an intermediary between collection terminals and user terminals. This database stores correction values that adjust measurement information from collection terminals to match the characteristics of user terminals, eliminating the need for individual terminal calibration while maintaining accuracy.
Solution Approach 2:
The system changes the parameters of measurement information by applying correction values from the measurement information correction database. These parameter adjustments transform the raw measurement data from collection terminals into corrected data that accurately reflects user terminal measurements without requiring physical calibration of each terminal.
Data Source
AI summary
Disclosed herein are a location estimation apparatus and method using a combination of different positioning resources. The location estimation apparatus includes a motion model generation unit for generating a motion model corresponding to a state variable of a terminal based on a current time, a weight calculation unit for extracting a dynamic location database (DB) from a location DB based on multiple pieces of measurement information received from the terminal, and calculating a weight proportional to a likelihood of the multiple pieces of measurement information based on the dynamic location DB, and a location estimation unit for estimating a location and direction of the terminal by applying the weight to at least one sample value corresponding to the motion model.