Distributed Sensor Calibration via Geospatial Reference
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Solution Overview
Problem
Calibrating sensors in distributed sensor systems is complex, especially when dealing with sensors of different types and manufacturers, and on-site or automated calibration methods can be cost-prohibitive, particularly for outdoor environments where sensors may decay or drift.
Innovation Solution
A method and apparatus for calibrating one sensor in a distributed sensor system using information from another sensor, based on geospatial location differences and relative data quality, involving frequency-based decomposition and filtering, and adjusting parameters like sensor gain and offset to improve data correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-site calibration or automated calibration systems are employed for each sensor, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent introduces a reference sensor as an intermediary element that mediates the calibration process between the sensor being calibrated and the calibration standard. The reference sensor, positioned at a known location, serves as a bridge to transfer calibration information to other sensors in the distributed system, eliminating the need for complex on-site calibration equipment at each sensor location.
Solution Approach 2:
The patent creates a virtual copy of the calibration process by using software-based calibration models and algorithms that replicate the functionality of physical calibration equipment. The calibration information is copied and transferred digitally across the distributed sensor network, replacing the need for physical calibration instruments at each sensor site.
2Measurement precision
If on-site calibration or automated calibration systems are employed for each sensor, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The reference sensor acts as a cost-effective intermediary that enables calibration information transfer without requiring expensive automated calibration systems at each sensor location. This intermediary approach significantly reduces the overall calibration cost while maintaining measurement precision.
Solution Approach 2:
The calibration system is designed to be universal across multiple sensors of different types and manufacturers. A single reference sensor and calibration platform can service the entire distributed sensor network, making the calibration process more cost-effective compared to dedicated calibration systems for each sensor.
3Adaptability or versatility
If sensors are deployed in outdoor environments, then adaptability is improved, but reliability deteriorates due to sensor decay and drifting
Solution Approach 1:
The system performs preliminary calibration actions using the reference sensor before the actual measurement process. By establishing calibration relationships in advance and continuously updating them, the system compensates for sensor drift and decay that occur in outdoor environments, maintaining data reliability.
Solution Approach 2:
The calibration system implements continuous feedback by repeatedly comparing sensor readings with the reference sensor and automatically adjusting calibration parameters. This feedback loop compensates for environmental effects and sensor degradation, maintaining reliability in outdoor deployment conditions.
Data Source
AI summary
Methods, systems, and devices are described for calibrating a sensor of a distributed sensor system. More specifically, the described features generally relate to calibrating one sensor of such a system using information from one or more other sensors of the system. A calibration model may be determined based at least in part on a difference in geospatial location of the sensors. Further, in the case of one or both sensors being mobile, the difference in geospatial location of the sensors may vary over time such that different calibration models may apply to different portions of the sensed data. Also, the relative quality of the data sensed by the sensors may be taken into account for calibration (e.g., directionality of the calibration).