Radial basis function Kalman filter for attitude estimation in GPS‐denied environment

Assad, Ammar; Khalaf, Wassim; Chouaib, Ibrahim

doi:10.1049/iet-rsn.2019.0467

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Various neural network models are adopted in AI-aided integrated navigation. Assad adopted the radial basis function neural network (RBF) to improve the attitude estimation accuracy in GPS-denied environments [30]. Yao estimated the pseudo GPS position with the MLP network when GPS signal was unavailable [28].…”

Section: Neural Network Model: Grumentioning

confidence: 99%

A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage

et al. 2022

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The integrated navigation system consisting of an inertial navigation system (INS) and Global Navigation Satellite System (GNSS) provides continuous high-accuracy positioning whereas the navigation accuracy during a GNSS outage inevitably degrades owing to INS error divergence. To reduce such degradation, a gated recurrent unit (GRU) and adaptive Kalman filter (AKF)-based hybrid algorithm is proposed. The GRU network, which has advantages of high accuracy and efficiency, is constructed to predict the position variations during GNSS outage. Furthermore, this paper takes the GRU-predicted error accumulation into consideration, and introduces AKF as a supplementary methodology to improve the navigation performance. The proposed hybrid algorithm is trained and tested by practical road datasets and compared with four algorithms, including the standard KF, Multi-Layer Perceptron (MLP)-aided KF, Long Short Time Memory (LSTM) aided KF, and GRU-aided KF. Periods of 180 and 120 s GNSS outage are employed to test the performance of the proposed algorithm in different time scales. The comparison result between the standard KF and neural network-aided KF indicates that the neural network is an effective methodology for bridging GNSS outages. The performance comparison between three kinds of neural networks demonstrate that both recurrent neural networks surpass the MLP in prediction position variation, and the GRU transcends the LSTM in prediction accuracy and training efficiency. Furthermore, it is concluded that the adaptive estimation theory is an effective complement to neural network-aided navigation, as the GRU-aided AKF reduced the horizontal error of GRU-aided KF by 31.71% and 16.12% after 180 and 120s of GNSS outage, respectively.

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Section: Neural Network Model: Grumentioning

confidence: 99%

A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage

et al. 2022

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“…The usage of neural network (the basic thing in CNN model) has increased remarkably lately, Many studies on neural networks in new fields is are rising every day. In Compacting Concrete as in [ 14 ], In navigation fields as in [ 15 ]. In quantum physics as in [ 16 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation

2021

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Person Recognition based on Gait Model (PRGM) and motion features is are indeed a challenging and novel task due to their usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with Image Augmentation (IA) technique depending on gait features. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters Adaptation, the design of CNN model itself was adapted to get best model structure; Adaptation in the design was affected the type, the number of layers in CNN and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase the size of train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices by the convolution, so dataset size may be bigger by hundred times to make the problem a big data issue. In this project, results show that adaptation has improved the accuracy of person recognition using gait model comparing to model without adaptation. In addition, dataset contains images of person carrying things. IA technique improved the model to be robust to some variations such as image dimensions (quality and resolution), rotations and carried things by persons. Results for 200 persons recognition, validation accuracy was about 82% without IA and 96.23 with IA. For 800 persons recognition, validation accuracy was 93.62% without IA.

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“…e ANNs (artificial neural networks) were widely employed to mimic the error pattern of the INS during GNSS outages in recent years, due to their ability for the nonlinear mapping between inputs and outputs without the predefined mathematical model [11]. Different ANNs, such as MLP (multiplayer perceptron) [12], RBF (radial basis function) [13,14], and ANFIS (adaptive neuro-fuzzy inference system) [15,16], were investigated for navigations in previous studies, and they have been proved to be able to reduce the navigation errors. e limitation of MLP is that it is time-consuming to obtain the optimal number for the hidden layer and neurons [12].…”

Section: Introductionmentioning

confidence: 99%

“…e limitation of MLP is that it is time-consuming to obtain the optimal number for the hidden layer and neurons [12]. In contrast, the RBF could dynamically generate the best internal structure due to its dynamic property [13]. e ANFIS provides superior performance in dealing with the randomness of the inputs [16].…”

Section: Introductionmentioning

confidence: 99%

The Integration of Rotary MEMS INS and GNSS with Artificial Neural Networks

Gan

Zhang

et al. 2021

Mathematical Problems in Engineering

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The rotary INS (inertial navigation system) has been applied to compensate the navigation errors of the MEMS (micro-electro-mechanical-systems) INS recently. In such system, the PVA (position, velocity, and attitude) errors can be compensated through IMU (inertial measurement unit) carouseling. However, the navigation errors are only partially compensated due to the intrinsic property of the inertial system and the randomness of the IMU errors. In this paper, we present an integrated rotary MEMS INS/GNSS (global navigation satellite systems) system based on the ANN (artificial neural networks) technique. The ANFIS (adaptive neuro-fuzzy inference system) is applied to eliminate the residual PV (position and velocity) errors of the rotary MEMS INS during GNSS outages. A cascaded velocity-position structure is designed to recognize the pattern of the rotary MEMS INS PV errors and to reduce them of the rotary inertial system in standalone mode. The road tests are conducted with artificial GNSS outages to evaluate the ability of the integrated system to predict the PV errors. Compared to the position errors of the integrated rotary INS/GNSS system based on an EKF (extended Kalman filtering), they are reduced by 79.98% in the proposed system.

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Radial basis function Kalman filter for attitude estimation in GPS‐denied environment

Cited by 7 publications

References 19 publications

A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage

A GRU and AKF-Based Hybrid Algorithm for Improving INS/GNSS Navigation Accuracy during GNSS Outage

Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation

The Integration of Rotary MEMS INS and GNSS with Artificial Neural Networks

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