Indoor Positioning With Wi-Fi RTT and IMU Drift Correction
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Solution Overview
Problem
Conventional indoor positioning techniques suffer from inaccuracy, impracticality, and the uncommonness of UWB transceivers, making Wi-Fi positioning a strong but imperfect contender, particularly due to issues like low precision, measurement rate reduction, and sensor drift in Wi-Fi RTT measurements.
Innovation Solution
A method and apparatus that utilize Wi-Fi ranging and inertial measurement units (IMUs) for indoor positioning, employing filtering techniques and sensor-driven motion models to enhance accuracy by fusing RTT ranging measurements with sensor data, using methods like Extended Kalman Filter (EKF) and particle filtering to improve positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Wi-Fi RTT ranging is used for indoor positioning, then positioning coverage is improved due to widespread Wi-Fi infrastructure, but measurement precision deteriorates compared to UWB
Solution Approach 1:
The patent combines Wi-Fi RTT ranging measurements with IMU sensor data (accelerometer, gyroscope, magnetometer) to create a fused positioning solution. The sensor data compensates for Wi-Fi ranging errors through dead reckoning, while Wi-Fi provides periodic position corrections, achieving both wide coverage and improved precision
Solution Approach 2:
The positioning system uses a composite approach by integrating multiple positioning technologies (Wi-Fi RTT and inertial sensing) into a unified system. This composite system leverages the strengths of each technology while mitigating their individual weaknesses, similar to how composite materials combine different materials to achieve superior properties
2Adaptability or versatility
If Wi-Fi RTT ranging is used, then positioning availability is improved, but measurement rate deteriorates due to protocol limitations
Solution Approach 1:
The system maintains continuous positioning by running IMU-based dead reckoning continuously while periodically updating with Wi-Fi RTT measurements. The sensor data fills the gaps between Wi-Fi ranging updates, ensuring uninterrupted position estimation despite Wi-Fi's lower measurement rate
Solution Approach 2:
The IMU sensors act as an intermediary that bridges the time gaps between Wi-Fi RTT measurements. The inertial data provides continuous position estimates during intervals when Wi-Fi ranging cannot be performed, maintaining measurement continuity
3Measurement precision
If IMU sensor data is used for positioning, then short-term accuracy is improved, but drift accumulates over time deteriorating long-term accuracy
Solution Approach 1:
The system uses Wi-Fi RTT measurements as feedback to correct accumulated drift in the IMU-based dead reckoning. Periodic Wi-Fi position updates reset the drift accumulation, while the sensor data continues to provide high-rate tracking between corrections
Solution Approach 2:
The positioning system performs periodic Wi-Fi RTT measurements to reset drift accumulation, while maintaining continuous IMU-based tracking between periodic updates. This periodic correction approach prevents unbounded drift while preserving high measurement rates
4Measurement precision
If UWB transceivers are deployed for high-precision positioning, then measurement precision is improved, but device complexity and cost increase due to uncommon hardware
Solution Approach 1:
The system uses widely available, low-cost Wi-Fi transceivers and standard IMU sensors instead of expensive UWB hardware. By fusing these common components, the patent achieves UWB-like positioning accuracy without requiring uncommon or expensive transceivers
Data Source
AI summary
An indoor positioning method includes scanning for registered Wi-Fi nodes with known coordinates to generate a list of the registered Wi-Fi nodes. The method also includes performing a ranging operation by (i) selecting nodes to range with from the list of the registered Wi-Fi nodes, and (ii) processing ranging responses from the selected nodes to generate a series of distance measurements. The method further includes obtaining a series of sensor readings generated by one or more inertial measurement units (IMUs) of a device. The method also includes estimating a position of the device based on the series of distance measurements and the series of sensor readings using first and second filtering operations that are performed in parallel.


