Dynamic Sensor Selection for Remote Device Location Accuracy
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Solution Overview
Problem
Conventional RTLS systems face limitations in accurately determining the location of remote devices due to issues such as multipath fading and communication bandwidth, which affect the ability to communicate between object devices and remote devices.
Innovation Solution
A system and method that dynamically selects a subset of sensors or anchors on an object, such as a vehicle, to conduct ranging procedures based on their operational status and performance metrics, ensuring only acceptably operational devices participate in location determination, using communication protocols like UWB and Bluetooth LE, and employing backchannel interfaces for sensor communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensors are used for ranging procedures, then the system has high redundancy and reliability, but the accuracy deteriorates due to inclusion of non-operational sensors affected by multipath fading and communication bandwidth limitations
Solution Approach 1:
The system segments the sensor network into operational and non-operational subsets. The control system evaluates each sensor's operational status individually and selectively includes only operational sensors in ranging procedures, thereby eliminating the negative impact of non-operational sensors on location accuracy while maintaining system reliability through the availability of multiple sensors.
Solution Approach 2:
The system applies different operational criteria to different sensors based on their individual performance characteristics. Each sensor is evaluated locally for its operational status using metrics such as signal strength, communication quality, and ranging procedure success, allowing the system to optimize location determination accuracy by selecting sensors with locally superior performance.
2Measurement precision
If operational status verification is implemented, then the location determination accuracy improves by excluding non-operational sensors, but the system complexity increases due to additional control mechanisms and communication protocols
Solution Approach 1:
Sensors perform self-evaluation of their operational status by monitoring their own performance metrics such as communication quality and ranging procedure success. This self-service mechanism reduces the need for complex external verification systems, as each sensor autonomously determines its suitability for participation in location determination procedures.
Solution Approach 2:
The control system implements a feedback mechanism where sensors report their operational status and performance metrics to the control system, which then makes informed decisions about sensor selection. This feedback loop enables accurate location determination by continuously updating the set of operational sensors based on real-time performance data.
3Measurement precision
If dynamic sensor selection is performed, then the location accuracy improves by optimizing communication pathways, but the time consumption increases due to sensor evaluation and selection processes
Solution Approach 1:
The system performs preliminary evaluation of sensor operational status before initiating ranging procedures. By pre-screening sensors and identifying operational ones in advance, the system avoids time-consuming evaluations during active ranging, thus reducing the time loss while maintaining location accuracy through selective sensor usage.
Solution Approach 2:
The control system periodically re-evaluates sensor operational status and updates the set of operational sensors at scheduled intervals. This periodic action balances the need for accurate location determination with time efficiency, as the system does not continuously evaluate all sensors but rather at appropriate intervals to maintain accuracy without excessive time consumption.
Data Source
AI summary
A system and method are provided a selection, optionally dynamic, of a subset of sensors in a system for determining a location of a remote device relative to an object.