Indoor Localization via BLE-Inertial Sensor Fusion
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Solution Overview
Problem
Current indoor localization methods using inertial and Bluetooth Low Energy (BLE) sensors face inaccuracies due to stride-length errors in Pedestrian Dead Reckoning and magnetic noise, as well as spatial errors in BLE-based distance measurements, leading to high localization errors within 2 meters from beacons.
Innovation Solution
A system that divides the indoor area into grids with beacons at each corner, using binary search techniques to identify the grid and assign weights based on distance from beacons, and fuses inertial and BLE data to improve localization accuracy through spatial error modeling and trilateration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Pedestrian Dead Reckoning (PDR) is used for indoor localization, then localization can be performed using inertial sensors, but stride-length inaccuracies and magnetic noise cause high localization errors
Solution Approach 1:
The patent combines inertial sensors (accelerometer, gyroscope, magnetometer) with Bluetooth Low Energy (BLE) sensors to fuse data from multiple sources. This merging compensates for the weaknesses of individual sensors: inertial data provides short-term accuracy while BLE data corrects long-term drift, resolving the contradiction between measurement precision and reliability
Solution Approach 2:
The system implements feedback through continuous sensor data fusion where BLE distance measurements are used to correct accumulated inertial navigation errors. The particle filter algorithm processes incoming sensor data and adjusts position estimates in real-time, creating a closed-loop system that maintains both precision and reliability
2Measurement precision
If Bluetooth Low Energy (BLE) distance measurements are used for localization, then distance can be estimated from signal strength, but spatial errors and multipath effects lead to high localization errors within 2 meters from beacons
Solution Approach 1:
The patent dynamically changes the weighting parameter in the data fusion algorithm based on the user's distance from beacons. When the user is within 2 meters of a beacon where spatial errors are significant, the system reduces the weight of BLE measurements and increases the weight of inertial measurements, thereby compensating for the harmful spatial errors
Solution Approach 2:
The system employs dynamic weight adjustment in the particle filter algorithm, where the contribution of each sensor type varies continuously based on environmental conditions and distance from beacons. This dynamic adaptation allows the system to optimize localization accuracy by emphasizing the more reliable sensor data under different spatial conditions
3Measurement precision
If data fusion is performed to improve localization accuracy, then location information can be obtained with better granularity, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the data fusion process into distinct modules: inertial navigation module, BLE distance calculation module, particle filter integration module, and weight adjustment module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture, reducing the perceived complexity while maintaining high location granularity
Data Source
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AI summary
Sensor data fusing systems and methods are provided. The fusing system reads and parses a floor plan to obtain a location of a user, identifies a grid in the floor plan using the location, determines a distance between the user and beacons placed at every corner of identified grid, and further trilaterating the location using beacon identifiers. The system further assigns a weight to the trilaterated location based on the distance between the user and the beacons in the grid to obtain a first set of weights, and computes one or more weights using number of particles generated with respect to an inertial measurement obtained from an inertial sensor to obtain a second set of weights. The fusing system further fuses the first set of weights and the second set of weights to obtain a first and a second co-ordinate that indicates specific position of the user in the location.