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Proceedings ArticleDOI

Implementation of an intelligent SINS navigator based on ANFIS

23 Mar 2009-pp 1-7

TL;DR: The results show that the intelligent navigator based on ANFIS more powerful compared with other (traditional and intelligent navigators based on ANN).
Abstract: In this work an intelligent navigator developed to overcome the limitations of existing Strapdown Inertial Navigation Systems (SINS) algorithm. This system is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). As in previous work, which is based on Artificial Neural Network, the window based weight updating strategy was used, and the intelligent navigator evaluated using several SINS hypothetical field tests data. And the results show that the intelligent navigator based on ANFIS more powerful compared with other (traditional and intelligent navigator based on ANN).

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Implementation of an intelligent SINS navigator based on ANFIS
ABSTRACT
In this work an intelligent navigator developed to overcome the limitations of existing
Strapdown Inertial Navigation Systems (SINS) algorithm. This system is based on Adaptive
Neuro-Fuzzy Inference System (ANFIS).
As in previous work, which is based on Artificial Neural Network, the window based weight
updating strategy was used, and the intelligent navigator evaluated using several SINS
hypothetical field tests data. And the results show that the intelligent navigator based on
ANFIS more powerful compared with other (traditional and intelligent navigator based on
ANN).
Keyword: Inertial navigation systems; Adaptive fuzzy system; Intelligent navigator
Citations
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Journal ArticleDOI
Tao Zhang1, Xiaosu Xu1Institutions (1)
01 May 2012-Measurement
TL;DR: By re-training NN withWMRA, the system accuracies improved to the level of using normal GPS signal, and NN trained with WMRA improved the approximation to the actual model, further enhancing alignment accuracy.
Abstract: For the last few years, integrated navigation systems have been widely used to calculate positions and attitudes of vehicles. The strapdown inertial navigation system (SINS) provides velocity, attitudes and position information, whereas the global positioning system (GPS) provides velocity and position information. A method using neural network (NN) and wavelet-based de-noising technology is introduced into the SINS/GPS/magnetometer integrated navigation system, because system accuracy may decrease during GPS outages. When the GPS is working well, NN is trained using the velocity and position information provided by SINS as input and the corresponding errors as output. Wavelet multi-resolution analysis (WMRA) is also introduced to de-noise the errors, the desired output of NN. Test results showed that velocity accuracies improved using NN, but other accuracies remained poor. By re-training NN with WMRA, the system accuracies improved to the level of using normal GPS signal. In addition, NN trained with WMRA also improved the approximation to the actual model, further enhancing alignment accuracy.

80 citations


Journal ArticleDOI
Salam Ismaeel1, Raed Karim1, Ali Miri1Institutions (1)
01 Dec 2018-
TL;DR: This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption and presents a general framework that can be used on multiple phases of a complete consolidation process.
Abstract: Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

36 citations


Additional excerpts

  • ...Combining fuzzy and NN improves the modeling and prediction process, even ANFIS has better performance than NN [12, 87], but both of them require training before use....

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Journal ArticleDOI
01 Jan 2020-Gps Solutions
TL;DR: A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages.
Abstract: In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages.

5 citations


Journal ArticleDOI
Qian Li1, Feng Sun1, Fei Yu1, Wei Gao1Institutions (1)
TL;DR: A kind of marine strapdown attitude and heading reference system (AHRS) based on the principle of strapdown inertial navigation system (INS) and an adaptive network-based fuzzy inference system to control the damping ratio automatically in terms of the vessel maneuvers conditions is discussed here.
Abstract: A kind of marine strapdown attitude and heading reference system (AHRS) based on the principle of strapdown inertial navigation system (INS) is discussed here. With an electromagnetic (EM) log aided, the oscillations included in the attitude and heading errors are bounded by damping network. Furthermore, in order to decrease attitude and heading errors aroused by EM log measurements, we introduce an adaptive network-based fuzzy inference system to control the damping ratio automatically in terms of the vessel maneuvers conditions. The results of test demonstrate the validity of proposed method.

2 citations



References
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Book
01 Jan 1999-
TL;DR: The Science of Navigation.
Abstract: The Science of Navigation. Coordinate Frames and Transformations. Systems Concepts. Discrete Linear and Non-Linear Kalman Filtering Techniques. The Global Positioning System. Inertial Navigation. Navigation Examples and Case Studies. Appendices: A: Notation, Symbols, and Constants. B: Matrix Review.

886 citations


"Implementation of an intelligent SI..." refers background in this paper

  • ...Strapdown Inertial Navigation System algorithms are the mathematical definition of processes, which convert the measured outputs of inertial sensors that are fixed to a vehicle body axis into quantities, which can be used to control the vehicle [2]....

    [...]


Journal ArticleDOI
Kai-Wei Chiang1, Yun-Wen Huang1Institutions (1)
01 Jan 2008-
TL;DR: The results presented in this article strongly indicate the potential of including the intelligent navigator as the core algorithm for INS/GPS integrated land vehicle navigation systems.
Abstract: The Kalman filter (KF) has been implemented as the primary integration scheme of the global positioning system (GPS) and inertial navigation systems (INS) for many land vehicle navigation and positioning applications. However, it has been reported that KF-based techniques have certain limitations, which reflect on the position error accumulation during GPS signal outages. Therefore, this article exploits the idea of incorporating artificial neural networks to develop an alternative INS/GPS integration scheme, the intelligent navigator, for next generation land vehicle navigation and positioning applications. Real land vehicle test results demonstrated the capability of using stored navigation knowledge to provide real-time reliable positioning information for stand-alone INS-based navigation for up to 20min with errors less than 16m (as compared to 2.6km in the case of the KF). For relatively short GPS outages, the KF was superior to the intelligent navigator for up to 30s outages. In contrast, the intelligent navigator was superior to the KF when the length of GPS outages was extended to 90s. The average improvement of the intelligent navigator reached 60% in the latter scenario. The results presented in this article strongly indicate the potential of including the intelligent navigator as the core algorithm for INS/GPS integrated land vehicle navigation systems.

88 citations


"Implementation of an intelligent SI..." refers methods in this paper

  • ...[5] S. A. Ismaeel and A. M. Hassan, “GPS/INS System Integration Based on Neuro-Wavelet Techniques,” The 2006 International Conference on Artificial Intelligence (ICAI’06 Las Vegas, USA), June 26-29, 2006,. [6] S. A. Ismaeel and K. M. Al-Jebory, “Adaptive Fuzzy System Modeling,” Eng. Technology, vol. 20, no. 4, pp. 201-212, 2001....

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  • ...[3] Kai-Wei, C., Yun, H., “An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications,” Applied Soft Computing 8, 722-733, 2008....

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  • ...X. REFERENCES [1] D. Jwo and H. Huang, “GPS Navigation Using Fuzzy Neural Network Aided Adaptive Extended Kalman Filter,” Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain, December 12-15, 2005....

    [...]

  • ...These algorithms suffer from unbounded error due to integration process in it, most articles overcome this problem by integration the INS system with other system like global positioning system (GPS) [3], or by using Kalman filter to overcome such problems [4]....

    [...]


Proceedings ArticleDOI
26 Aug 2004-
TL;DR: This paper analyzes and compares the attitude error and velocity error of the above algorithm with other algorithms' and shows that the synchro-updating algorithm is more effective than the others for the angular rate outputs and the specific force outputs.
Abstract: This paper proposes an approach to update the attitude and velocity synchronously utilizing the fourth order Runge-Kutta in the strapdown inertial navigation system. The synchro-updating algorithm is designed for the condition, in which the outputs of gyroscope are the angular rate and the outputs of accelerometer are the specific force acceleration. The synchro-updating algorithm is based on the quaternion. Under the improved classical coning motion and the generalized vibrational motion respectively, this paper analyzes and compares the attitude error and velocity error of the above algorithm with other algorithms'. The simulation results show that the synchro-updating algorithm is more effective than the others for the angular rate outputs and the specific force outputs.

10 citations


Proceedings ArticleDOI
Dah-Jing Jwo1, Hung-Chih HuangInstitutions (1)
12 Dec 2005-
TL;DR: The fuzzy method combined with NN to identify the noise covariance matrix is proposed and Numerical simulations show that the adaptation accuracy based on the proposed approach is substantially improved.
Abstract: GPS navigation state processing using the extended Kalman filter provides optimal solutions (in the mean square sense) if the noise statistics for the measurement and system are completely known. Covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. This innovation-based adaptive estimation shows noisy result if the window size is small. To overcome the problem, the fuzzy method combined with NN to identify the noise covariance matrix is proposed. The structure of FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the network using back-propagation algorithm. Numerical simulations show that the adaptation accuracy based on the proposed approach is substantially improved.

8 citations


"Implementation of an intelligent SI..." refers methods in this paper

  • ...The noncommutativity of finite rotations is one of the major error sources in numerical solutions of the SINS calculation [1]....

    [...]


Proceedings Article
01 Jan 2006-
TL;DR: A new method for error estimation in a GPS/INS augmented system based on Artificial Neural Network (ANN) and Wavelet Transform (WT) was offered and it was found that the proposed technique reduces the standard deviation error in the position by about 91% and in velocity it was reduced by about 94%.
Abstract: Global Positioning System (GPS) and Strapdown Inertial Navigation System (SDINS) can be integrated together to provide a reliable navigation system. This paper offers a new method for error estimation in a GPS/INS augmented system based on Artificial Neural Network (ANN) and Wavelet Transform (WT). An ANN was adopted in this paper to model the GPS/INS position and velocity errors in real time to predict the error in the integrated system and provide accurate navigation information for a moving vehicle. It was found that the proposed technique reduces the standard deviation error in the position by about 91% for X, Y, and Z axes, while in velocity it was reduced by about 94% for North, East, and Down directions.

2 citations


"Implementation of an intelligent SI..." refers methods in this paper

  • ...TRAINING PROCEDURES The training samples acquired for the window based parameters updating strategy can be arranged through using the following two procedures [5]: One step training procedure: The training samples acquired for each INS window during navigation are the combination of stored training samples and available training samples obtained at the end of each INS window....

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  • ...While in reference [5], the artificial neural network (ANN) was used....

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  • ...Unlike the work described in [5], that uses ANN....

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  • ...As depicted in [5], the procedure of the window-based parameters updating method is given below: Parameters initialization: The initial parameters can be obtained using previously stored parameters that are stored in navigation database (NAViBASE) or random initialization....

    [...]


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20202
20181
20141
20121