Bias Compensation for IMU Sensors in Indoor Localization
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Solution Overview
Problem
Conventional indoor localization and tracking systems face challenges due to noise, sampling rate limitations, and environmental impacts, particularly in GPS-denied indoor environments, where wireless-based systems are less accurate and raise privacy concerns, necessitating improved methods for stable localization.
Innovation Solution
A computer-implemented method using a Bluetooth localization signal receiver and inertial measurement unit (IMU) sensors, which determines motion parameters based on a probabilistic motion model without prior calibration, dynamically updates bias parameters using an iterative smoothing and mapping algorithm, and integrates with a particle filter loop to generate accurate localization data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional wireless-based positioning systems are used for indoor localization, then coverage can be achieved, but accuracy deteriorates due to shadowing and multipathing effects
Solution Approach 1:
The patent combines Bluetooth localization signals with IMU sensor readings to create a hybrid positioning system. The particle filter integrates data from both Bluetooth beacons and IMU sensors (accelerometer, gyroscope, magnetometer) to estimate device position, thereby compensating for the weaknesses of each individual system and achieving both coverage and accuracy.
Solution Approach 2:
The patent introduces Bluetooth Low Energy (BLE) beacons as intermediary reference points for localization. These beacons provide stable reference signals that mediate between the device and the environment, enabling accurate position estimation without relying solely on wireless communication signals that suffer from shadowing and multipathing effects.
2Productivity
If IMU sensors are used for motion tracking, then continuous tracking is achieved, but bias noise accumulates over time
Solution Approach 1:
The patent implements a feedback mechanism where the particle filter continuously updates the device position estimate based on both IMU sensor readings and Bluetooth beacon measurements. This feedback loop corrects accumulated bias noise by periodically anchoring the position estimate to the known locations of Bluetooth beacons, preventing drift while maintaining continuous tracking.
Solution Approach 2:
The patent dynamically adjusts the particle filter parameters and bias compensation parameters based on the reliability of Bluetooth signals and IMU data quality. When Bluetooth signals are strong and reliable, the system places more weight on beacon-based position correction; when signals are weak, it relies more on IMU integration, thereby adapting to changing conditions to maintain accuracy.
3Measurement precision
If camera-based localization systems are deployed, then visual tracking accuracy is improved, but privacy concerns increase
Solution Approach 1:
The patent replaces camera-based visual tracking systems with a combination of Bluetooth beacon signaling and IMU sensor-based tracking. This substitution eliminates the need for visual capture and processing, thereby maintaining localization accuracy through probabilistic motion modeling while completely avoiding the privacy concerns associated with camera-based systems.
4Measurement precision
If sensor calibration is performed to improve accuracy, then measurement precision increases, but system complexity and setup time increase
Solution Approach 1:
The patent implements self-service calibration where the system automatically estimates and compensates for IMU sensor bias during normal operation. The particle filter continuously updates bias parameters based on the difference between predicted and observed positions from Bluetooth beacons, eliminating the need for manual pre-calibration while maintaining high accuracy throughout the device's operational life.
Data Source
AI summary
A probabilistic motion model to calculate the motion parameters of a user's hand held device using a noisy low cost Inertial Measurement Unit (IMU) sensor. Also described is a novel technique to reduce the bias noise present in the aforesaid IMU sensor signal, which results in a better performance of the motion model. The system utilizes a Particle Filter (PF) loop, which fuses radio signal data accumulated from Bluetooth Low Energy (BLE) beacons using BLE receiver with the signal from IMU sensor to perform localization and tracking. The Particle Filter loop operates based on a Sequential Monte Carlo technique well known to persons of ordinary skill in the art. The described approach provides a solution for both the noise problem in the IMU sensor and a motion model utilized in the Particle Filter loop, which provides better performance despite the noisy IMU sensor.