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Filtering and Estimation of Vehicular dead reckoning System Based on Hopfield Neural Network

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Journal of Beijing Institute of Technology 2003年第3期12卷 230-235页

作者：毕军付梦印张启鸿 Department of Automatic Control School of Information Science and TechnologyBeijing Institute of Technology

The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.

关键词： Hopfield neural network dead reckoning filtering and estimation vehicle navigation

Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

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Journal of Marine Science and Application 2007年第2期6卷 19-24页

作者： XIAO Kun FANG Shao-ji PANG Yong-jie College of Shipbuilding Engineering Harbin Engineering University Harbin150001 China

To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

关键词： dead reckoning underwater vehicle strong tracking kalman filter measurement noise

Research on an Improved dead reckoning for AUV Navigation

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Research on an Improved Dead Reckoning for AUV Navigation

Recurrent neural network for vehicle dead-reckoning

2010 Chinese Control and Decision Conference

作者： Zheping Yan,Shuping Peng,Jiajia Zhou,Jian Xu,Heming Jia College of Automation,Harbin Engineering University,Harbin,150001

＜正＞It is difficult for Autonomous Underwater Vehicle（AUV） to satisfy the demand of long time navigation because of the installation angle’s deviation error as well as the measurement *** data collected by GPS were filtered using Extended Kalman Filter（EKF） prior to being utilized in the ***,the installation angle’s deviation error of DVL was *** paper does some deep research on the improved dead reckoning method, and the performance is evaluated using real data collected by an *** presented experimental results verify that the improved dead reckoning method can significantly enhance the accuracy of navigation,which for the vehicle position was estimated to be less than 0.5%both in north and east.

关键词： Autonomous Underwater Vehicle EKF dead reckoning Autonomous Navigation

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Journal of Systems Engineering and Electronics 2008年第2期19卷 351-355页

作者： Ma Haibo Zhang Liguo Chen Yangzhou School of Electronic Control Engineering Beijing Univ. of Technology Beijing 100022 P. R. China

For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.

关键词： dead reckoning extended Kalman filter recurrent neural network vehicle integrated navigationsystems.

Geomagnetic /dead reckoning Integrated Navigation Method Considering Geomagnetic Error

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Geomagnetic /Dead Reckoning Integrated Navigation Method Con...

A regression model-based method for indoor positioning with compound location fingerprints

第24届中国控制与决策会议

作者： Dayuan Gao 1 , Chao Wang 1 , Hai Zhu 1 1. Navy Submarine Academy, Qingdao 266042, China

In geomagnetic navigation, the resulted position is not precise because of the influence of vehicle magnetic field. This paper used integrated navigation based on the geomagnetic measurement and dead reckoning. The geomagnetic error model is added to geomagnetic measurement model. The unscented Kalman filter is used to make two positioning method adjust each other. Simulation shows that the method is suitable for the integrated navigation of dead reckoning and geomagnetic measurement with error.

关键词： Geomagnetic Navigation Integrated Navigation Unscented Kalman Filter dead reckoning

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Geo-Spatial Information Science 2019年第2期22卷 107-113,I0003页

作者： Tomofumi Takayama Takeshi Umezawa Nobuyoshi Komuro Noritaka Osawa Hewlett-Packard Japan LtdTokyoJapan Graduate School of Engineering Chiba UniversityChibaJapan

This paper proposed and evaluated an estimation method for indoor *** method combines location fingerprinting and dead reckoning differently from the conventional *** uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead *** avoid errors accumulated from dead reckoning,the method uses short-range dead *** method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture *** Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between *** Forests(RF)was used to build regression models to estimate positions from location *** root mean square error of position estimation was 0.87 m using 16 Bluetooth *** error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one *** results suggest that the proposed method is effective for indoor positioning.

关键词： Indoor positioning integrated estimation radio fingerprinting dead reckoning machine learning non-linear regression

Indoor navigation: state of the art and future trends

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Satellite Navigation 2021年第1期2卷 88-110页

作者： Naser El-Sheimy You Li Department of Geomatics Engineering University of Calgary2500 University Drive N.WCalgaryAB T2N 1N4Canada

This paper reviews the state of the art and future trends of indoor Positioning,Localization,and Navigation(PLAN).It covers the requirements,the main players,sensors,and techniques for indoor *** than the navigation sensors such as Inertial Navigation System(INS)and Global Navigation Satellite System(GNSS),the environmental-perception sensors such as High-Definition map(HD map),Light Detection and Ranging(LiDAR),camera,the fifth generation of mobile network communication technology(5G),and Internet-of-Things(IoT)signals are becoming important aiding sensors for *** PLAN systems are expected to be more intelligent and robust under the emergence of more advanced sensors,multi-platform/multi-device/multi-sensor information fusion,self-learning systems,and the integration with artificial intelligence,5G,IoT,and edge/fog computing.

关键词： Navigation Indoor positioning Information fusion Wireless localization dead reckoning Database matching

Precise navigation for a 4WS mobile robot

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第2期7卷 185-193页

作者：何波 School of Information Science and Engineering Ocean University of China Qingdao 266071 China

Position and orientation estimation with high accuracy based on GPS and encoders for a four-wheel-steering vehicle (4WS) mobile robot is presented. A GPS receiver working in Real-Time Kinematics (RTK) mode can offer centimeter-level accuracy for our vehicle. In addition to GPS, the vehicle is equipped with four incremental encoders and two absolute encoders to provide information on wheels for estimation of velocity and sideslip angle of vehicle. The proposed architecture of position and orientation estimation consists of two extended Kalman filters and a processing unit of Runga-Kutta based dead reckoning. The first EKF fuses data from six encoders to estimate the vehicle velocity and the sideslip angle. The second EKF is applied to the estimation of position and orientation based on the measurement from precise RTK GPS data and output from first EKF. To obtain higher accuracy of estimation, an arbitrator is designed to switch between EKF2 and dead reckoning. The results and analysis of experiments are presented to show the effectiveness of the proposed approach. Limitations of the proposed approach and future works are also pointed out and discussed in this paper.

关键词： Extended Kalman filter Navigation dead reckoning GPS

Study of synthetic aperture radar and automatic identification system for ship target detection

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Journal of Ocean Engineering and Science 2019年第2期4卷 173-182页

作者： Sudhir Kumar Chaturvedi Department of Aerospace Engineering University of Petroleum&Energy StudiesDehradun 248007India

In the past,the main goals of Synthetic Aperture Radar(SAR)systems were the study of the interaction of electromagnetic waves with the earth ***,the development of multi-channel SAR systems has enabled the development of more sophisticated techniques for the surveillance *** is the most efficient instrument,which provides high-resolution data for wide ocean area surveillance under all weather *** intrinsic capability of this instrument is to provide a quick view of the oceanic surface features such as vessels,waves and currents,oil spills,laver facilities and wind *** ship detection or recognition is achieved in two steps:the first step is to identify the target in SAR images of a busy traffic,which corresponds to Automatic Identification System(AIS)signals by the“dead-reckoning(DR)position”,and the second step is to estimate the position,size and speed of the ship from SAR images and compare these results with the AIS“true”*** paper presents the fundamentals of SAR and its integration with the AIS data for the ship target detection.

关键词： SAR AIS dead reckoning Target detection.