A Fast Calibration Method for Sensors of Atmospheric Detection System
Source abstract
Abstract
To meet the needs of a large number of high-altitude meteorological detections, we need to perform fast, high-precision, and high-reliability calibrations of the sensors in the atmospheric detection system (ADS). However, using the traditional method to calibrate the sensor with high precision often takes a lot of time and increases the cost of workforce and material resources. Therefore, a method for realizing fast sensor calibration under the current system hardware conditions is required. A physical field model of Tube–Air–ADS is proposed for the first time, and the transfer function is obtained by combining the system identification, which provides the possibility for dynamic analysis of the calibration system. A Multi-Criteria Adaptive (MCA) PID controller design method is proposed, which provides a new idea for the parameter design of the controller. It controls the amplitude and switching frequency of the controller’s output signal, ensuring the safe and stable operation of the calibration system. Combined with the hardware parameters of the system, we propose the Variable Precision Steady-State Discrimination (VPSSD) method, which can further shorten the calibration time. Comparing and analyzing the current simulation results under Matlab/Simulink, the proposed MCA method, compared with other PID controller design methods, ensures the stable operation of the calibration system. At the same time, compared with the original system, the calibration time is shortened to 47.7%. Combined with the VPSSD method, the calibration time further shortens to 38.7 s.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Air Quality Monitoring and Forecasting · Aerospace and Aviation Technology · Advanced Sensor Technologies Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Institute for Frontier Science, Beihang University, Beijing 100191, China
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing 100081, China
Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314000, China
MIIT Key Laboratory of Complex-Field Intelligent Exploration, Beijing Institute of Technology, Beijing 100081, China
Funding
National Natural Science Foundation of China · The Fundamental Research Funds for the Central Universities
References (46)
Show source-provided references
1. Fu, W., Ma, J., Chen, P., and Chen, F. (2020). Remote sensing satellites for digital earth. Manual of Digital Earth, Springer.10.1007/978-981-32-9915-3_3 ↗
2. Peterson, T., Folland, C., Gruza, G., Hogg, W., Mokssit, A., and Plummer, N. (2001). Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs, Citeseer.
3. H-DrunkWalk: Collaborative and adaptive navigation for heterogeneous MAV swarm10.1145/3382094 ↗
4. Adaptive hybrid model-enabled sensing system (HMSS) for mobile fine-grained air pollution estimation10.1109/TMC.2020.3034270 ↗
5. Heat exposure and global air conditioning10.1038/s41893-019-0441-9 ↗
6. ilocus: Incentivizing vehicle mobility to optimize sensing distribution in crowd sensing
7. Lewis, A., Peltier, W.R., and von Schneidemesser, E. (2022, July 23). Low-Cost Sensors for the Measurement of Atmospheric Composition: Overview of Topic and Future Applications. Available online: https://eprints.whiterose.ac.uk/135994/1/WMO_Low_cost_sensors_post_review_final.pdf.
8. Yu, C., Luo, J., Shi, R., Liu, X., Dang, F., and Chen, X. (2022, January 17–21). ST-ICM: Spatial-Temporal Inference Calibration Model for Low Cost Fine-grained Mobile Sensing. Proceedings of the ACM MobiCom’22, Sydney, Australia.10.1145/3495243.3558280 ↗
9. Liu, L., Han, Y., Xia, Y., Guo, Q., Gao, W., and Guo, J. (2022). Investigation of Atmospheric Dynamic and Thermodynamic Structures of Typhoon Sinlaku (2020) from High-Resolution Dropsonde and Two-Way Rawinsonde Measurements. Remote Sens., 14.10.3390/rs14112704 ↗
10. Atmospheric observations of weather and climate10.1080/07055900.2022.2082369 ↗
11. Assessment and adaptive correction of observations in atmospheric sounding channels of the satellite microwave radiometer MTVZA-GY10.1007/s00024-018-1917-7 ↗
12. Iacovazzi, R., Lin, L., Sun, N., and Liu, Q. (2020). NOAA operational microwave sounding radiometer data quality monitoring and anomaly assessment using COSMIC GNSS radio-occultation soundings. Remote Sens., 12.10.3390/rs12050828 ↗
13. Toward the operational weather forecasting application of atmospheric stability products derived from NUCAPS CrIS/ATMS soundings10.1109/TGRS.2018.2824829 ↗
14. Chen, X., Purohit, A., Dominguez, C.R., Carpin, S., and Zhang, P. (2015, January 1–4). Drunkwalk: Collaborative and adaptive planning for navigation of micro-aerial sensor swarms. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, Republic of Korea.10.1145/2809695.2809724 ↗
15. Wang, C.C., Hou, Z.Y., and You, J.C. (2018). A high-precision CMOS temperature sensor with thermistor linear calibration in the (−5 ∘C, 120 ∘C) temperature range. Sensors, 18.10.3390/s18072165 ↗
16. Applicability of Bevis formula at different height levels and global weighted mean temperature model based on near-earth atmospheric temperature
17. Calibration of Air Thermometers in a Climatic Chamber and Liquid Baths
18. Liu, X., Xu, X., Chen, X., Mai, E., Noh, H.Y., Zhang, P., and Zhang, L. (2017, January 6–8). Individualized calibration of industrial-grade gas sensors in air quality sensing system. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, Delft, The Netherlands.10.1145/3131672.3136998 ↗
19. Nagahage, E.A.A.D., Nagahage, I.S.P., and Fujino, T. (2019). Calibration and validation of a low-cost capacitive moisture sensor to integrate the automated soil moisture monitoring system. Agriculture, 9.10.3390/agriculture9070141 ↗
20. Automated reviving calibration strategy for virtual in-situ sensor calibration in building energy systems: Sensitivity coefficient optimization10.1016/j.enbuild.2019.06.014 ↗
21. Automatic and rapid calibration method for temperature coefficient of fiber grating temperature sensor10.1088/1742-6596/1820/1/012141 ↗
22. On the calibration of sensor arrays for pattern recognition using the minimal number of experiments10.1016/j.chemolab.2013.10.012 ↗
23. Chen, X., Wang, H., Li, Z., Ding, W., Dang, F., Wu, C., and Chen, X. (2022, January 11–15). DeliverSense: Efficient Delivery Drone Scheduling for Crowdsensing with Deep Reinforcement Learning. Proceedings of the UbiComp/ISWC’22, Cambridge, UK.10.1145/3544793.3560412 ↗
24. Pas: Prediction-based actuation system for city-scale ridesharing vehicular mobile crowdsensing10.1109/JIOT.2020.2968375 ↗
25. Design experiences in minimalistic flying sensor node platform through sensorfly10.1145/3131779 ↗
26. Dynamic calibration of electrochemical sensor for accelerated analyte quantification10.1109/JSEN.2012.2231066 ↗
27. Low-cost outdoor air quality monitoring and sensor calibration: A survey and critical analysis10.1145/3446005 ↗
28. Self-calibration methods for uncontrolled environments in sensor networks: A reference survey10.1016/j.adhoc.2019.01.008 ↗
29. Multiscale Analysis to Determine the Sensitive Zones for Temperature Sensor Location in Tubular Reactors10.1002/ceat.202000053 ↗
30. A Controllable DCCS-Based PT temperature sensor in high precision molecular spectroscopy application10.1109/ACCESS.2020.2977163 ↗
31. Szolga, L.A., and Mudure, F. (2021, January 1–3). Temperature Sensor Using a Hybrid Structure with Plastic Optical Fiber and Bimetal Element. Proceedings of the 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, Romania.10.1109/ECAI52376.2021.9515030 ↗
32. Supervisory energy management of a hybrid battery/PV/tidal/wind sources integrated in DC-microgrid energy storage system10.1016/j.egyr.2021.11.056 ↗
33. Ullah, N., Sami, I., Jamal Babqi, A., Alkhammash, H.I., Belkhier, Y., Althobaiti, A., and Ibeas, A. (2021). Processor in the loop verification of fault tolerant control for a three phase inverter in grid connected PV system. Energy Sources, Part Recover. Util. Environ. Eff., 1–17.10.1080/15567036.2021.2015486 ↗
34. Interconnection and damping assignment passivity-based non-linear observer control for efficiency maximization of permanent magnet synchronous motor10.1016/j.egyr.2021.12.057 ↗
35. Tang, W., Wang, L., Gu, J., and Gu, Y. (2020). Single neural adaptive PID control for small UAV micro-turbojet engine. Sensors, 20.10.3390/s20020345 ↗
36. Low-cost quadrotor hardware design with PID control system as flight controller10.12928/telkomnika.v17i4.9529 ↗
37. Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems10.1016/j.heliyon.2022.e09399 ↗
38. Ziegler-Nichols tuning method10.1007/s12045-020-1058-z ↗
39. Fractional-order PID controller design for time-delay systems based on modified Bode’s ideal transfer function10.1109/ACCESS.2020.2996265 ↗
40. Core power control of a space nuclear reactor based on a nonlinear model and fuzzy-PID controller10.1016/j.pnucene.2020.103564 ↗
41. Nagrath, I. (2006). Control Systems Engineering, New Age International.
42. Constantin, P., and Foias, C. (2020). Navier-Stokes Equations, University of Chicago Press.
43. Transport phenomena10.1115/1.1424298 ↗
44. Quadrature-based vector fitting for discretized h_2 approximation10.1137/140961511 ↗
45. A review of PID control, tuning methods and applications10.1007/s40435-020-00665-4 ↗
46. Decentralized PID controller tuning based on nonlinear optimization to minimize the disturbance effects in coupled loops10.1109/ACCESS.2021.3127795 ↗