Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

Narasimhappa, Mundla

doi:10.5772/intechopen.86735

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…Traditional denoising methods typically use noise modeling, subtraction, and data smoothing to suppress noise [2]- [4]. However, noisy signals often exhibit adverse frequency fluctuations similar to underlying scientific signals, making it difficult to achieve optimal noise filtering [5].…”

Section: Introductionmentioning

confidence: 99%

Accelerometer signals denoising without clean training data based on Noise2Noise approach

Zheng,

Li,

Cui

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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The accelerometer signal is capable of providing crucial information regarding object motion, posture, and vibration. Therefore, it is of great significance to investigate noise suppression techniques for accelerometer signals. However, in complex industrial environments and under various equipment conditions, the noise generated exhibits a diverse nature, leading to suboptimal noise reduction outcomes when employing conventional denoising algorithms. This paper proposes a neural network model (AgentUNet) for denoising accelerometer signals. The network consists of a combination of UNet and Agent Attention, enhancing its ability to learn contextual information and achieve superior denoising performance. Additionally, instead of using clean signals as labeled data, we train the neural network using pairs of noisy data, thereby alleviating the challenge of collecting clean signals in industrial environments. Finally, we conduct experiments on experiment dataset. The experimental results indicate that AgentUNet achieves superior denoising performance compared to other baseline models. Furthermore, the denoising effectiveness of the neural network trained without using clean signals as labeled data is similar to that of the network trained using supervised learning methods.

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Section: Introductionmentioning

confidence: 99%

Accelerometer signals denoising without clean training data based on Noise2Noise approach

Zheng,

Li,

Cui

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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“…In general, there are three different ways to deal with random noise of FOG signal. The first one is to establish autoregressive moving average (ARMA) model based on the random noise sequence, and then the model is optimized by filtering methods [ 8 , 9 ]. Although this method performs well under the premise of accurate noise model is established, it is difficult to get accurate random noise model in practical application, so the noise reduction effect will be greatly affected [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

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“…Based on fact, many authors formulate sensor calibration as an estimation one[56] or as an optimization problem[57]-[60].The noise modeling or analysis methods of the gyroscope can fall into the statistical methods and ML methods. Statistical methods include Allan Variance, Auto Regressive Moving Average[61] and Auto Regressive Integrated Moving Average model. Especially, many authors utilize the Allan Variance to analyze gyroscope noise composition contained in the raw output signals[62].…”

mentioning

confidence: 99%

Recent Advances in Pedestrian Navigation Activity Recognition: A Review

et al. 2022

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Pedestrian navigation activity recognition (PNAR) has a significant impact on positioning and tracking performance. Smartphone-based PNAR utilizes measurements from sensors embedded in smartphone to identify pedestrian motion mode and smartphone usage mode. Compared with other PNAR technologies, smartphone-based PNAR has the advantage of autonomy and practicality. Though various PNAR recognition methods based on smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PNAR are relatively less so far in the literature. This work aims to present an elaborated, timely, and valuable survey for different smartphone-based PNAR techniques, including sensor calibration, pedestrian modeling, pedestrian motion and smartphone usage modes definition, workflow, state-of-the-art research methods, public datasets, performance evaluation metrics, and relative competitions. Finally, applications, current challenges and future research trends are discussed. The work propels a better understanding of existing navigation activity recognition methods. It is helpful for researchers to further design PNAR methods, and develop more accurate and robust pedestrian positioning and navigation systems.

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Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

Cited by 5 publications

References 37 publications

Accelerometer signals denoising without clean training data based on Noise2Noise approach

Accelerometer signals denoising without clean training data based on Noise2Noise approach

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Recent Advances in Pedestrian Navigation Activity Recognition: A Review

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