An innovative ultrasonic time of flight method based on extended Kalman filter for wind speed measurement

Lucena, Daiana Correia de; Freire, R.C.S.; Villanueva, Juan Moisés Maurício; Luciano, Benedito Antonio

doi:10.1088/1748-0221/15/04/p04024

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Papers [27][28][29][30][31] define the Kalman filter algorithm as a recursive mathematical method used to generate an estimate or approximation of a variable based on a series of measurements that are imperfect or disturbed by noise. In various applications, sensors such as paper [27] applied in healthcare and engineering [28] are used to estimate parameter values (states) such as distance parameters as a result of ultrasonic sensor output. In addition, the paper [30] describes the use of portable sensor technology to monitor environmental factors and the effects of health problems affected by measurement system design.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Distance parameters are discrete, from one point to a certain point and constants can affect the values of fluctuating and dynamic distance states listed in the paper, resulting in uncertainty in measurements [29]. In the papers [27][28][29][30][31] the application of control systems using sensors has been applied in various fields, but in this study, it was only applied to a limited scope for the benefit of physics education. The Kalman filter method as in the paper [31] can reduce noise as an uncertainty factor by minimizing the expected value of the Mean Squared Mean (MSE).…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…MSE is a method error function to measure the accuracy of the Kalman filter method as an estimation process using previously obtained sensor outputs. Application of the Kalman filter algorithm in the papers [27,28] estimates the distance measurement process of ultrasonic sensor modules such as the HC-SR04 model for more precise results. The Kalman filter algorithm application solution can be applied to Hooke's law demonstration system by involving the ultrasonic sensor output in its measurement.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

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The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

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“…Ultrasonic gas flow meters are one of the fastest growing technologies within the general field of instruments for process monitoring, measurement, and control [1,2]. Compared with electromagnetic, vortex, turbine, and other types of flow meters, ultrasonic gas flow meters possess unique measurement advantages, such as high accuracy, bidirectional measurement, and no pressure loss [3].…”

Section: Introductionmentioning

confidence: 99%

A novel differential time-of-flight algorithm for high-precision ultrasonic gas flow measurement

Li¹,

Yin²,

Dong³

et al. 2022

J. Inst.

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The use of ultrasonic gas flow meters has expanded in industrial and household fields owing to their unique advantages. The transit-time method is applied to measure the gas flow rate by determining the absolute time-of-flight (TOF). However, it is inevitable that there is a contradiction between long time scale and high time precision of absolute TOF. In this paper, a novel differential TOF (ΔT) algorithm is proposed for high-precision ultrasonic gas flow measurements. The experimental results proved that ΔT plays a decisive role in the actual flow rate calculation. Therefore, when the absolute TOF is obtained, furthermore, the high-precision ΔT is calculated by the cross-power spectrum method separately. The two echo signals are aligned and compared to obtain ΔT, instead of subtracting the absolute TOFs. Either of the two echo signals is the reference signal of each other, which also avoids the problem of finding reference signals. The results of the calibration experiment indicate that the algorithm improves the stability of zero flow drift under static conditions and the accuracy of the dynamic flow rates. The algorithm allows the adjustment of parameters to be applied to pipes of different diameters. The parameters of the algorithm can be adjusted for application to ultrasonic flow metering systems of different diameters.

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“…The Kalman filter is an important estimation technique largely used in control theory, signal analysis, navigation, traffic control, and in many other fields [3,4]. Born as a dynamical version of the least square method, it needs a model of the evolution in time of the physical system, an 'a priori' information from a previous estimation or measurement, some knowledge of the noises superimposed on the model, and the measurements.…”

Section: Introductionmentioning

confidence: 99%

A robust Kalman filter time scale algorithm with data anomaly

Song¹,

Dong²,

Qu³

et al. 2021

J. Inst.

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Kalman filtering is utilized in many fields because of its capability to separate data from white phase noise. In time and frequency domain, employing Kalman filter is particularly important because of its use in building time scales. The studied time scale algorithms have been usually based on an ensemble of clocks without data anomaly, or the anomaly data is processed in advance to secure the reliability of the data used in Kalman filter algorithm. This increases the amount of computation and affects the real-time performance of the algorithm. In this study a robust Kalman filter is employed to present a method of time scale calculation. It extends a previously published Kalman filter algorithm that is useful for an ensemble of clocks without phase anomalies. In the algorithm, the inflation factor and the optimal adaptive factor are applied to the clock ensemble. The introduced algorithm may be useful for an ensemble of clocks with measurement outliers and phase jumps. The effectiveness of the proposed method can be verified through simulation and experimental analysis. The analysis result shows that the robust Kalman filter algorithm can resist the influence of measurement outliers and phase jumps on time scale performance. So, the accuracy and the stability of time scale can be improved.

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An innovative ultrasonic time of flight method based on extended Kalman filter for wind speed measurement

Cited by 4 publications

References 20 publications

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

A novel differential time-of-flight algorithm for high-precision ultrasonic gas flow measurement

A robust Kalman filter time scale algorithm with data anomaly

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