A statistical approach for optimal order adjustment of a moving average filter

González, Rodrigo A.; Catania, Carlos

doi:10.1109/plans.2018.8373549

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…Euler angles, navigation states). [18] [19]- [21] Fuzzy logic [22], [23] Savitsky-Golay [24] [25], [26] [27] Wavelets [28], [29] [30] [31]- [33] [34], [35] [36], [37] Moving average (MA) techniques can be used as efficient smoothing filter, based on errors (residuals) from previous forecasts [6]. Other works elaborated this by combining a weighted regression term over the lagged values, namely autoregressive moving-average (ARMA) [7]- [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…1) Moving Average (MA): suppresses unstable signal noises by averaging measurements inside a rolling window [6] xMA,i = 1…”

Section: A Signal Processing Denoising Approachesmentioning

confidence: 99%

“…In our simulative setup, Euler angles are drawn within a bounded domain, and using small intervals, a rich combination of data points (orientations) is guaranteed. Using the transformation matrix ( 5), Euler angles dictate how gravity is projected on the sensor axes (6). Without loss of generality, the yaw angle has no influence on the gravity projection, thus it remains constant (ψ sim.…”

Section: A Simulated Datasetmentioning

confidence: 99%

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Data-Driven Denoising of Accelerometer Signals

Engelsman¹,

Klein²

2022

Preprint

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Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude. Both our code and dataset are publicly available @ GitHub.

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Section: Literature Reviewmentioning

confidence: 99%

“…1) Moving Average (MA): suppresses unstable signal noises by averaging measurements inside a rolling window [6] xMA,i = 1…”

Section: A Signal Processing Denoising Approachesmentioning

confidence: 99%

Section: A Simulated Datasetmentioning

confidence: 99%

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Data-Driven Denoising of Accelerometer Signals

Engelsman¹,

Klein²

2022

Preprint

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“…The determination of the moving average that is appropriate for the financial signal increases the efficiency in the analysis of the stock market. Gonzalez and Catania [31] adopted a moving average calculation method used with inexpensive sensors and compared the data values obtained from expensive sensors. The experimental results indicated that adjusting the moving average used with the inexpensive sensors had similar answer values as the expensive sensors.…”

Section: Moving Average Filtermentioning

confidence: 99%

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Palananda

Kimpan

2021

IEEE Access

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In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity. INDEX TERMSImage processing, turbidity level, moving average, coconut oil, Computer vision.

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“…15(b), measured ip(t) and in(t) waveforms noise are relatively large. To reduce the measurement error caused by this noise, the moving average method is used to suppress this noise [29]. The processed transient current waveforms are shown in Fig.…”

Section: Figure 13mentioning

confidence: 99%

Residual Flux Density Measurement Method of Single-Phase Transformer Core Based on Time Constant

Huo

Yang

et al. 2020

IEEE Access

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Residual flux density in the single-phase transformer core can dramatically increase inrush current when the transformer is energized. To reduce inrush current, it is necessary to study residual flux density measurement. This paper proposes a new residual flux density measurement method based on time constant. Firstly, the generation principle of residual flux density is analyzed under different magnetization states, and it is found that the positive relative differential permeability is smaller than the negative at different residual flux density. To obtain the relative differential permeability, when an appropriate DC excitation is applied, the measurement circuit is equivalent to a first-order RL circuit. Then, combining magnetic circuit and transient circuit analysis, the relationship between time constant and relative differential permeability is obtained. It is conclusion that the positive time constant is less than the negative. Residual flux density direction is determined by comparing the positive and negative time constant, and the magnitude of residual flux density is calculated by the relationship between residual flux density and the difference of the positive and negative time constant. Finally, the empirical formula between residual flux density and time constant difference of the square core is obtained in finite element method, and then verified on the experimental platform. Compared with other measurement methods, the relative error of proposed empirical formula is within 4.58 %, and it has higher accuracy in this paper. The proposed method in this paper can provide a reference for selecting the demagnetization voltage, which improves the effectiveness of demagnetization. INDEX TERMS Residual flux density, relative differential permeability, time constant difference, empirical formula.

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A statistical approach for optimal order adjustment of a moving average filter

Cited by 5 publications

References 2 publications

Data-Driven Denoising of Accelerometer Signals

Data-Driven Denoising of Accelerometer Signals

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Residual Flux Density Measurement Method of Single-Phase Transformer Core Based on Time Constant

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