Hybrid Indoor Positioning via Dynamic Mode Selection
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Solution Overview
Problem
Traditional indoor positioning methods face limitations such as inaccuracy and impracticality due to the scarcity of ultra-wideband (UWB) devices, while Wi-Fi-based solutions struggle with precision despite their ubiquity, necessitating a hybrid approach that leverages both sensor data and ranging measurements for improved accuracy.
Innovation Solution
A method and device that dynamically switch between multiple positioning modes based on motion event signals and ranging measurements, incorporating sensor data such as acceleration, orientation, and step size, to estimate an object's position using a combination of RTT distance measurements, trilateration, and step-and-heading algorithms, ensuring accurate positioning in varying indoor environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UWB technology is used for indoor positioning, then positioning accuracy is improved, but device availability deteriorates due to scarcity of UWB devices
Solution Approach 1:
The positioning system is segmented into multiple independent positioning modes (UWB-based mode, Wi-Fi-based mode, sensor-based mode) that can operate independently or in combination. Each mode handles specific scenarios, allowing the system to maintain high accuracy when UWB devices are available while falling back to alternative modes when they are not, thus resolving the contradiction between accuracy and availability.
Solution Approach 2:
The positioning device is designed with multi-functionality by integrating multiple positioning technologies (UWB, Wi-Fi, sensors) into a single unified system. The device can automatically select or combine different positioning methods based on environmental conditions and available resources, making it universally applicable whether UWB devices are present or absent, thereby resolving the availability issue while maintaining accuracy capabilities.
2Adaptability or versatility
If Wi-Fi based positioning is used, then device availability is improved, but positioning accuracy deteriorates
Solution Approach 1:
The system merges Wi-Fi positioning with sensor-based positioning (accelerometer, gyroscope, magnetometer) to create a hybrid positioning approach. The sensor data compensates for Wi-Fi's accuracy limitations by providing complementary information about device motion and orientation, thereby improving overall positioning accuracy while maintaining the high availability advantage of Wi-Fi infrastructure.
Solution Approach 2:
The positioning system uses a composite approach by combining multiple positioning technologies (Wi-Fi, sensors, and optionally UWB) into a unified hybrid system. This composite methodology allows the system to leverage the strengths of each individual technology while mitigating their weaknesses, achieving both high availability from Wi-Fi and improved accuracy through sensor fusion and selective UWB integration.
3Measurement precision
If hybrid positioning mode is used, then positioning accuracy is improved, but system complexity increases
Solution Approach 1:
The positioning system implements dynamic mode selection that automatically adapts to current environmental conditions and available resources. The system dynamically switches between different positioning modes (UWB-only, Wi-Fi-only, sensor-only, or hybrid combinations) based on real-time assessments of accuracy requirements and resource availability, thereby managing complexity through adaptive behavior rather than static configuration.
Solution Approach 2:
The system applies local quality by selecting specific positioning modes for specific scenarios rather than using a single complex hybrid approach universally. For example, UWB mode is activated only when UWB devices are available and high accuracy is required, while simpler Wi-Fi or sensor modes are used in other contexts. This localized application of different positioning strategies reduces overall system complexity while maintaining high accuracy where needed.
Data Source
AI summary
A method for estimating a position of an object comprises: receiving a motion event signal indicating a motion type of the object, the motion type being determined based on sensing data provided by one or more sensors; receiving one or more ranging measurements for distances between the object and one or more anchor points from a ranging device; determining a mode among a plurality of positioning modes based on the motion event and the one or more ranging measurements; and estimating a position of the object using the determined mode.