Incremental learning of LSTM framework for sensor fusion in attitude estimation

This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Gyroscope, accelerometer, and magnetometer are few widely used sensors in attitude estimation applications. Traditionally, multi-sensor fusion methods such as the Extended Kalman Filter and Complementary Filter are employed to fuse the measurements from these sensors. However, these methods exhibit limitations in accounting for the uncertainty, unpredictability, and dynamic nature of the motion in real-world situations. In this paper, the inertial sensors data are fed to the LSTM network which are then updated incrementally to incorporate the dynamic changes in motion occurring in the run time. The robustness and efficiency of the proposed framework is demonstrated on the dataset collected from a commercially available inertial measurement unit. The proposed framework offers a significant improvement in the results compared to the traditional method, even in the case of a highly dynamic environment. The LSTM framework-based attitude estimation approach can be deployed on a standard AI-supported processing module for real-time applications.

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Compounded Calibration Based on FNN and Attitude Estimation Method Using Intelligent Filtering for Low Cost MEMS Sensor Application

Mathematical Problems in Engineering ◽

10.1155/2019/4514873 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Lei Wang ◽

Ying Guan ◽

Xuedong Hu

Keyword(s):

Inertial Sensors ◽

Symmetric Matrix ◽

Estimation Method ◽

Dynamic Environment ◽

Attitude Estimation ◽

Measurement Unit ◽

Calculation Error ◽

Coupling Coefficients ◽

Attitude Error ◽

High Dynamic

Micro electro mechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption. The performances of Micro Inertial measurement unit (IMU), which is composed of MEMS inertial sensors, degrade, and error, will become larger in high dynamic environment. In order to solve the problem, a novel combined calibration method for compensating the deterministic error of MEMS sensors is proposed. Considering the rotation of different sensitive axes in high dynamic and low dynamic environment, the compounded calibration based on fuzzy neural network (FNN) is adopted to identify the coupling coefficients to eliminate the adverse coupling effects between different rotation axes. Furthermore, the self-developed Micro IMU and magnetometer are applied in attitude estimation system. Considering the large attitude error occurred in most cases, the approach utilizing the estimation of error quaternion vector could avoid the calculation error due to inaccurate modeling in the skew symmetric matrix that comprises attitude error vector components. The intelligent Kalman filter (IKF) based on complexity state equation of error quaternion is designed to improve the performance by adjusting the parameters of filter on line. The experimental results show that the proposed approach could have a higher level of stability and accuracy in comparison to other attitude estimation algorithms.

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An approach towards data change rule based classification of driving maneuver with LSTM network

10.36227/techrxiv.12891662 ◽

2020 ◽

Author(s):

Supriya Sarker ◽

Md Mokammel Haque

Keyword(s):

Time Series ◽

Sensor Fusion ◽

Short Term Memory ◽

Extraction Process ◽

Test Accuracy ◽

Fusion Data ◽

Time Series Dataset ◽

Lstm Network ◽

Multi Class Classification

The proposed work develops a Long Short Term Memory (LSTM) model for multi class classification of driving maneuver from sensor fusion time series dataset. The work also analyzes the significance of sensor fusion data change rule and utilized the idea with deep learning time series multi class classification of driving maneuver. We also proposed some hypotheses which are proven by the experimental results. The proposed model provides Train Accuracy: 99.98, Test Accuracy: 97.2021, Precision: 0.974848, Recall: 0.960154 and F1 score: 0.967028. The Mean Per Class Error (MPCE) is 0.01386. The significant rules can accelerate the feature extraction process of driving data. Moreover, it helps in automatic labeling of unlabeled dataset. Our future approach is to develop a tool for generating categorical label for unlabeled dataset. Besides, we have plan to optimize the proposed classifier using grid search. <br>

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Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network

Sensors ◽

10.3390/s19173740 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3740 ◽

Cited By ~ 1

Author(s):

Shucong Liu ◽

Dezhi Zheng ◽

Rui Li

Keyword(s):

Oil And Gas ◽

Short Term Memory ◽

Compensation Method ◽

Sensor Data ◽

Measurement Unit ◽

Main Method ◽

Position Errors ◽

Multi Sensor Data Fusion ◽

Lstm Network ◽

Energy Transportation

The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips.

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An approach towards data change rule based classification of driving maneuver with LSTM network

10.36227/techrxiv.12891662.v1 ◽

2020 ◽

Author(s):

Supriya Sarker ◽

Md Mokammel Haque

Keyword(s):

Time Series ◽

Sensor Fusion ◽

Short Term Memory ◽

Extraction Process ◽

Test Accuracy ◽

Fusion Data ◽

Time Series Dataset ◽

Lstm Network ◽

Multi Class Classification

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IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes

Journal of Healthcare Engineering ◽

10.1155/2021/9601420 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Jing Ye ◽

Hui Wang ◽

MeiJie Li ◽

Ning Wang

Keyword(s):

Short Term Memory ◽

Inertial Sensors ◽

Inertial Sensor ◽

Wearable Sensors ◽

Motion Prediction ◽

Proposed Model ◽

Long Short Term Memory ◽

Lstm Network ◽

The Times ◽

The Internet Of Things

Aerobics is the fusion of gymnastics, dance, and music; it is a body of a sports project, along with the development of the society. The growing demand for aerobics inevitably increases the demand for aerobics coach and teacher and has opened elective aerobics class which is an effective way of cultivating professional talents relevant to aerobics. Aerobics has extended fixed teaching mode and cannot conform to the development of the times. The motion prediction of aerobics athletes is a new set of teaching aid. In this paper, a motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of Things and the bidirectional long short term memory (BiLSTM) network. Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes. The inertial sensors were used for data collection and transmission of the data to the cloud platform through Ethernet. Then, the movement of aerobics athletes is recognized and predicted by the BiLSTM network. Based on the BiLSTM network and the attention mechanism, this paper proposes to solve the problem of low classification accuracy caused by the traditional method of directly summing and averaging the updated output vectors corresponding to each moment of the BiLSTM layer. The simulation experiment is also carried out in this paper. The experimental results show that the proposed model can recognize aerobics effectively.

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Optimized Processing in IMU-Aided GPS Signal Tracking

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.62 ◽

2012 ◽

Vol 157-158 ◽

pp. 62-65

Author(s):

Lin Zhao ◽

Yong Hao ◽

Li Shu Guo

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Moving Average ◽

Dynamic Environment ◽

Doppler Frequency ◽

Search Space ◽

Estimation Accuracy ◽

Measurement Unit ◽

Signal Tracking ◽

Local Code

A GPS signal tracking method utilizing optimized processing in inertial measurement unit (IMU) aided GPS receiver is studied. In order to enhance the sensitivity of baseband processing, low-cost inertial sensors are applied in the GPS and strapdown inertial navigation system (SINS) integration commonly, where the estimation accuracy of Doppler frequency would influence the whole performance of the tracking loops. Stochastic errors of carrier Doppler estimation caused by inertial sensors and local oscillators are corrected utilizing auto regressive moving average (ARMA) method in this paper. And then the accuracy Doppler information is used to correct the local code phase and local carrier frequency to further determine the search space of frequency domain which is unlike to the design of traditional aided loop. Simulation results indicate that the bandwidth of carrier loop and code loop could be decreased significantly in high dynamic environment.

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Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽

10.3390/s21144767 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4767

Author(s):

Karla Miriam Reyes Leiva ◽

Milagros Jaén-Vargas ◽

Benito Codina ◽

José Javier Serrano Olmedo

Keyword(s):

Inertial Measurement Unit ◽

Visually Impaired ◽

Inertial Sensors ◽

Assistive Technologies ◽

Biomechanical Analysis ◽

Measurement Unit ◽

Inertial Measurement ◽

Visually Impaired People ◽

Impaired People ◽

Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

Sustainability ◽

10.3390/su13126953 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6953

Author(s):

Yixing Du ◽

Zhijian Hu

Keyword(s):

Power Systems ◽

Power System ◽

Transient Stability ◽

Short Term Memory ◽

Risk Function ◽

Stability Margin ◽

Risk Level ◽

Stability Assessment ◽

Lstm Network ◽

Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

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A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽

10.3390/s21041181 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1181

Author(s):

Chenhao Zhu ◽

Sheng Cai ◽

Yifan Yang ◽

Wei Xu ◽

Honghai Shen ◽

...

Keyword(s):

Kalman Filter ◽

Standard Deviation ◽

Error Compensation ◽

Random Vibration ◽

Short Term Memory ◽

Combined Method ◽

Short Term ◽

Mems Gyroscope ◽

Long Short Term Memory ◽

Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

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Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

SN Computer Science ◽

10.1007/s42979-021-00507-w ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Kate Highnam ◽

Domenic Puzio ◽

Song Luo ◽

Nicholas R. Jennings

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Traffic ◽

Short Term Memory ◽

Domain Names ◽

Control Networks ◽

Detection Techniques ◽

Lstm Network ◽

And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

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