Relevant bibliographies by topics / Inertial navigation; Kalman filter

Academic literature on the topic 'Inertial navigation; Kalman filter'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Inertial navigation; Kalman filter"

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Zhang, Xing Zhi, Kun Peng He, and Chen Yang Wang. "Transfer Alignment for MEMS Integrated Navigation System Based on H∞ Filter." Applied Mechanics and Materials 490-491 (January 2014): 886–90. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.886.

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The transfer alignment of strapdown inertial units were proposed that use the H filter to estimate the misalignment of the slave INS (inertial navigation system) relative to the master INS. Characteristics of the H filter in transfer alignment were studied in detail by checking digital simulation results obtained by using the H and Kalman filters. The results shows that the misalignment angle obtained with the H filter converge faster and closer to the exact values than do those obtained with the Kalman filter. The H filter is more robust than the Kalman filter in transfer alignment for MEMS integrated navigation system.
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Wang, Ning. "Satellite/Inertial Navigation Integrated Navigation Method Based on Improved Kalman Filtering Algorithm." Mobile Information Systems 2022 (May 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/4627111.

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With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.
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Zhou, Weidong, Jiaxin Hou, Lu Liu, Tian Sun, and Jing Liu. "Design and Simulation of the Integrated Navigation System based on Extended Kalman Filter." Open Physics 15, no. 1 (April 17, 2017): 182–87. http://dx.doi.org/10.1515/phys-2017-0019.

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AbstractThe integrated navigation system is used to estimate the position, velocity, and attitude of a vehicle with the output of inertial sensors. This paper concentrates on the problem of the INS/GPS integrated navigation system design and simulation. The structure of the INS/GPS integrated navigation system is made up of four parts: 1) GPS receiver, 2) Inertial Navigation System, 3) Extended Kalman filter, and 4) Integrated navigation scheme. Afterwards, we illustrate how to simulate the integrated navigation system with the extended Kalman filter by measuring position, velocity and attitude. Particularly, the extended Kalman filter can estimate states of the nonlinear system in the noisy environment. In extended Kalman filter, the estimation of the state vector and the error covariance matrix are computed by steps: 1) time update and 2) measurement update. Finally, the simulation process is implemented by Matlab, and simulation results prove that the error rate of statement measuring is lower when applying the extended Kalman filter in the INS/GPS integrated navigation system.
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Fariz, Outamazirt, Muhammad Ushaq, Yan Lin, and Fu Li. "Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System." Applied Mechanics and Materials 332 (July 2013): 79–85. http://dx.doi.org/10.4028/www.scientific.net/amm.332.79.

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Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.
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Qian, Kun, Jian-Guo Wang, and Baoxin Hu. "Novel Integration Strategy for GNSS-Aided Inertial Integrated Navigation." GEOMATICA 69, no. 2 (June 2015): 217–30. http://dx.doi.org/10.5623/cig2015-205.

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The conventional integration mechanism in GNSS (Global Navigation Satellite Systems) aided inertial integrated positioning and navigation system is mainly based on the continuous outputs of the navigation mechanization, the associated error models for navigation parameters, the biases of the inertial measurement units (IMU), and the error measurements. Its strong dependence on the a priori error characteristics of inertial sensors may suffer with the low-cost IMUs, e.g. the MEMS IMUs due to their low and unstable performance. This paper strives for a significant breakthrough in a compact and general integration strategy which restructures the Kalman filter by deploying a system model on the basis of 3D kinematics of a rigid body and performing measurement update via all sensor data inclusive of the IMU measurements. This novel IMU/GNSS Kalman filter directly estimates navigational parameters instead of the error states. It enables the direct use of the IMU's raw outputs as measurements in measurement updates of Kalman filter instead of involving the free inertial navigation calculation through the conventional integration mechanism. This realization makes all of the sensors in a system no longer to be differentiated between core and aiding sensors. The proposed integration strategy can greatly enhance the sustainability of low-cost navigation systems in poor GNSS and/or GNSS denied environment compared to the conventional aided error-state-based inertial navigation integration mechanism. The post-processed solutions are presented to show the success of the proposed multisensor integrated navigation strategy.
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An, Shi Qi, and Jun Kai Zhang. "The Study of Kalman Filtering Algorithm in the Initial Alignment of Strapdown Inertial Navigation System." Applied Mechanics and Materials 740 (March 2015): 596–99. http://dx.doi.org/10.4028/www.scientific.net/amm.740.596.

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According to the principle and the method of initial alignment of strapdown inertial navigation system, proposed based on Sage-Husa adaptive kalman filter algorithm. The measured simulation data, compared with those of kalman filtering algorithm, show that the optimized algorithm can optimize the noise estimation, revise accumulated error of strapdown inertial navigation system, and greatly improve the navigation accuracy.
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Gopaul, N. S., J. G. Wang, and B. Hu. "Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2 (November 11, 2014): 61–66. http://dx.doi.org/10.5194/isprsannals-ii-2-61-2014.

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An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are <i>pairwise</i> correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch <i>k</i> is not only correlated with the one from epoch <i>k</i> &ndash; 1 but also with the ones before epoch <i>k</i> &ndash; 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.
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Svensson, A., and J. Holst. "Integration of Navigation Data." Journal of Navigation 48, no. 1 (January 1995): 114–35. http://dx.doi.org/10.1017/s0373463300012558.

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This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.
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Wang, Qi, Cheng Shan Qian, Zi Jia Zhang, and Chang Song Yang. "Application of Federated Filter to AUV Based on Terrain-Aided SINS." Applied Mechanics and Materials 711 (December 2014): 338–41. http://dx.doi.org/10.4028/www.scientific.net/amm.711.338.

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To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.
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Hide, Christopher, Terry Moore, and Martin Smith. "Adaptive Kalman Filtering for Low-cost INS/GPS." Journal of Navigation 56, no. 1 (January 2003): 143–52. http://dx.doi.org/10.1017/s0373463302002151.

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GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.
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Dissertations / Theses on the topic "Inertial navigation; Kalman filter"

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Rogers, Jonas Paul. "GNSS and Inertial Fused Navigation Filter Simulation." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1303.

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A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The envi- ronment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.
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Marquis, Carl W. "Integration of differential GPS and inertial navigation using a complementary Kalman filter /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273370.

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Thesis (M.S. in Aeronautical Engineering) Naval Postgraduate School, September 1993.
Thesis advisor(S): Kaminer, Isaac I. "September 1993." Includes bibliographical references. Also available online.
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Marquis, Carl W. III. "Integration of differential GPS and inertial navigation using a complementary Kalman filter." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/39974.

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Approved for public release; distribution is unlimited.
Precise navigation with high update rates is essential for automatic landing of an unmanned aircraft. Individual sensors currently available - INS, AHRS, GPS, LORAN, etc. - cannot meet both requirements. The most accurate navigation sensor available today is the Global Positioning System or GPS. However, GPS updates only come once per second. INS, being an on-board sensor, is available as often as necessary. Unfortunately, it is subject to the Schuler cycle, biases, noise floor, and cross-axis sensitivity. In order to design and verify a precise, high update rate navigation system, a working model of Differential GPS has been developed including all of the major GPS error sources - clock differences, atmospherics, selective availability and receiver noise. A standard INS system was also modeled, complete with the inaccuracies mentioned. The outputs of these two sensors - inertial acceleration and pseudoranges - can be optimally blended with a complementary Kalman filter for positioning. Eventually, in the discrete case, the high update rate and high precision required for autoland can be achieved.
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Abdul, Sattar H. L. "An adaptive U-D factorized Kalman filter for strap down inertial navigation system." Thesis, Cranfield University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.237549.

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Hartana, Pande. "Comparison of linearized and extended Kalman filter in GPS-aided inertial navigation system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57729.pdf.

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Hartana, Pande (Pande Putu Gde) Carleton University Dissertation Engineering Mechanical and Aerospace. "Comparison of linearized and extended Kalman filter in GPS aided inertial navigation system." Ottawa, 2000.

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Akcay, Emre Mustafa. "Land Vehicle Navigation With Gps/ins Sensor Fusion Using Kalman Filter." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610327/index.pdf.

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Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receivers are sensors that are widely used for land vehicle navigation. GPS receivers provide position and/or velocity data to any user on the Earth&rsquo
s surface independent of his position. Yet, there are some conditions that the receiver encounters difficulties, such as weather conditions and some blockage problems due to buildings, trees etc. Due to these difficulties, GPS receivers&rsquo
errors increase. On the other hand, IMU works with respect to Newton&rsquo
s laws. Thus, in stark contrast with other navigation sensors (i.e. radar, ultrasonic sensors etc.), it is not corrupted by external signals. Owing to this feature, IMU is used in almost all navigation applications. However, it has some disadvantages such as possible alignment errors, computational errors and instrumentation errors (e.g., bias, scale factor, random noise, nonlinearity etc.). Therefore, a fusion or integration of GPS and IMU provides a more accurate navigation data compared to only GPS or only IMU navigation data. v In this thesis, loosely coupled GPS/IMU integration systems are implemented using feed forward and feedback configurations. The mechanization equations, which convert the IMU navigation data (i.e. acceleration and angular velocity components) with respect to an inertial reference frame to position, velocity and orientation data with respect to any desired frame, are derived for the geographical frame. In other words, the mechanization equations convert the IMU data to the Inertial Navigation System (INS) data. Concerning this conversion, error model of INS is developed using the perturbation of the mechanization equations and adding the IMU&rsquo
s sensor&rsquo
s error model to the perturbed mechanization equation. Based on this error model, a Kalman filter is constructed. Finally, current navigation data is calculated using IMU data with the help of the mechanization equations. GPS receiver supplies external measurement data to Kalman filter. Kalman filter estimates the error of INS using the error mathematical model and current navigation data is updated using Kalman filter error estimates. Within the scope of this study, some real experimental tests are carried out using the software developed as a part of this study. The test results verify that feedback GPS/INS integration is more accurate and reliable than feed forward GPS/INS. In addition, some tests are carried out to observe the results when the GPS receiver&rsquo
s data lost. In these tests also, the feedback GPS/INS integration is observed to have better performance than the feed forward GPS/INS integration.
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Magree, Daniel Paul. "Monocular vision-aided inertial navigation for unmanned aerial vehicles." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53892.

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The reliance of unmanned aerial vehicles (UAVs) on GPS and other external navigation aids has become a limiting factor for many missions. UAVs are now physically able to fly in many enclosed or obstructed environments, due to the shrinking size and weight of electronics and other systems. These environments, such as urban canyons or enclosed areas, often degrade or deny external signals. Furthermore, many of the most valuable potential missions for UAVs are in hostile or disaster areas, where navigation infrastructure could be damaged, denied, or actively used against the vehicle. It is clear that developing alternative, independent, navigation techniques will increase the operating envelope of UAVs and make them more useful. This thesis presents work in the development of reliable monocular vision-aided inertial navigation for UAVs. The work focuses on developing a stable and accurate navigation solution in a variety of realistic conditions. First, a vision-aided inertial navigation algorithm is developed which assumes uncorrelated feature and vehicle states. Flight test results on a 80 kg UAV are presented, which demonstrate that it is possible to bound the horizontal drift with vision aiding. Additionally, a novel implementation method is developed for integration with a variety of navigation systems. Finally, a vision-aided navigation algorithm is derived within a Bierman-Thornton factored extended Kalman Filter (BTEKF) framework, using fully correlated vehicle and feature states. This algorithm shows improved consistency and accuracy by 2 to 3 orders of magnitude over the previous implementation, both in simulation and flight testing. Flight test results of the BTEKF on large (80 kg) and small (600 g) vehicles show accurate navigation over numerous tests.
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Gautam, Ishwor. "Quaternion based attitude estimation technique involving the extended Kalman filter." University of Akron / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1556196539847396.

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Eddy, Joshua Galen. "A Hardware-Minimal Unscented Kalman Filter Framework for Visual-Inertial Navigation of Small Unmanned Aircraft." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/77927.

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This thesis presents the development and implementation of a software framework for estimating the position of a drone during flight. This framework is based on an algorithm known as the Unscented Kalman Filter (UKF), a recursive method of estimating the state of a highly nonlinear system, such as an aircraft. In this thesis, we present a UKF formulation specially designed for a quadcopter carrying an Inertial Measurement Unit (IMU) and a downward-facing camera. The UKF fuses data from each of these sensors to track the position of the quadcopter over time. This work supports a number of similar efforts in the robotics and aerospace communities to navigate in GPS-denied environments with minimal hardware and minimal computational complexity. The software framework explored in this thesis provides a means for roboticists to easily implement similar UKF-based state estimators for a wide variety of systems, including surface vessels, undersea vehicles, and automobiles. We test the system's effectiveness by comparing its position estimates to those of a commercial motion capture system and then discuss possible applications.
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Books on the topic "Inertial navigation; Kalman filter"

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Weill, Lawrence R. (Lawrence Randolph), 1938-, Andrews Angus P, and Wiley online library, eds. Global positioning systems, inertial navigation, and integration. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2007.

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P, Andrews Angus, Bartone Chris, and ebrary Inc, eds. Global navigation satellite systems, inertial navigation, and integration. 3rd ed. Hoboken: John Wiley & Sons, 2013.

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North Atlantic Treaty Organization. Advisory Group for Aerospace Research and Development. Kalman filter integration of modern guidance and navigation systems. Neuilly sur Seine, France: AGARD, 1989.

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1938-, Weill Lawrence Randolph, and Andrews Angus P, eds. Global positioning systems, inertial navigation, and integration. New York: John Wiley, 2001.

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Leach, B. W. A Kalman filter integrated navigation design for the IAR twin otter atmospheric research aircraft. Ottawa: National Research Council of Canada, 1991.

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Applied mathematics in integrated navigation systems. 2nd ed. Reston, VA: American Institute of Aeronautics and Astronautics, 2003.

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Applied mathematics in integrated navigation systems. 3rd ed. Reston, VA: American Institute of Aeronautics and Astronautics, 2007.

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Carpenter, J. Russell. Progress in navigation filter estimate fusion and its application to spacecraft rendezvous. [Washington, D.C.]: National Aeronautics and Space Administration, 1994.

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Carpenter, J. Russell. Progress in navigation filter estimate fusion and its application to spacecraft rendezvous. [Washington, D.C.]: National Aeronautics and Space Administration, 1994.

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Leader, Daniel Eugene. Kalman filter estimation of underwater vehicle position and attitude using a Doppler velocity aided inertial motion unit. Springfield, Va: Available from National Technical Information Service, 1994.

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Book chapters on the topic "Inertial navigation; Kalman filter"

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Noureldin, Aboelmagd, Tashfeen B. Karamat, and Jacques Georgy. "Kalman Filter." In Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration, 225–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30466-8_7.

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Shi, Zhijian, Ruochen Feng, Rui Lin, and Gareth Peter Lewis. "A Novel Kalman Filter Algorithm Using Stance Detection for an Inertial Navigation System." In Lecture Notes in Electrical Engineering, 1968–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_260.

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Rawiel, Paul. "Positioning of Pedelecs for a Pedelec Sharing System with Free-Floating Bikes." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 51–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_5.

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AbstractFor intelligent mobility concepts in growing urban environments, positioning of transportation vehicles and generally moving objects is a fundamental prerequisite. Global Navigation Satellite Systems (GNSS) are commonly used for this purpose, but especially in urban environments under certain conditions, they offer limited accuracy due to buildings, tunnels, etc. that can deviate or mask the satellite signals. The use of existing built-in sensors of the vehicle and the installation of additional sensors can be utilized to describe the movement of the vehicle independently of GNSS. This conforms to the concept of dead reckoning (DR). Both systems (GNSS and DR) can be integrated and prepared to work together since they compensate their respective weaknesses efficiently. In this study, a method to integrate different inertial sensors (gyroscope and accelerometer) and GNSS is investigated. Pedelecs usually do not have many inbuilt additional sensors like it is the case in cars; therefore, additional low-cost sensors have to be used. An extended Kalman filter (EKF) is the base of calculations to perform data integration. Driving tests are realized to check the performance of the integration model. The results show that positioning in situations where GNSS data is not available can be done through dead reckoning for a short period of time. The weak point hereby is the calibration of the accelerometer. Inaccurate accelerometer data cause increasing inaccuracy of the position due to the double integration of the acceleration over time.
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Etzion, Joseph. "Steady-State Time Constant of the Kalman Filter." In Advances in Estimation, Navigation, and Spacecraft Control, 3–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44785-7_1.

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Unhelkar, Vaibhav V., and Hari B. Hablani. "Spacecraft Attitude Determination with Sun Sensors, Horizon Sensors and Gyros: Comparison of Steady-State Kalman Filter and Extended Kalman Filter." In Advances in Estimation, Navigation, and Spacecraft Control, 413–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44785-7_22.

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Contreras, Alberto Mañero, and Chingiz Hajiyev. "Integration of Baro-Inertial-GPS Altimeter via Complementary Kalman Filter." In Advances in Sustainable Aviation, 251–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67134-5_18.

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T.N, Ranjan, Arun Nherakkol, and Gajanan Navelkar. "Navigation of Autonomous Underwater Vehicle Using Extended Kalman Filter." In Communications in Computer and Information Science, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15810-0_1.

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Kulo, Nedim. "Effects of Kalman Filter in Pedestrian Navigation by Smartphone." In Advanced Technologies, Systems, and Applications VII, 581–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17697-5_44.

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Kamel, Ahmed A., Handol Kim, Dochul Yang, Chulmin Park, and Jin Woo. "Generalized Image Navigation and Registration Method Based on Kalman Filter." In Advances in Aerospace Guidance, Navigation and Control, 609–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65283-2_33.

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Li, Jing, Jiande Wu, Junfeng Hou, Yugang Fan, and Xiaodong Wang. "Fault-Tolerant Integrated Navigation Algorithm of the Federal Kalman Filter." In Advances in Intelligent and Soft Computing, 621–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29390-0_99.

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Conference papers on the topic "Inertial navigation; Kalman filter"

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Xin, Lu, Hu Bai-qing, Zhang Guang-jun, and Xue Bo-yang. "Robust sequential Kalman filter for inertial integrated." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9018644.

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Rogers, Robert. "Kalman filter inertial navigation system error model based on filter stability considerations." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-3547.

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Huang, Xianlin, and Zhenkai Wang. "Adaptive unscented Kalman filter in Inertial Navigation System alignment." In 2011 2nd International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2011. http://dx.doi.org/10.1109/icicip.2011.6008402.

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WU, Y., and R. ORNEDO. "Kalman filter formulation for transfer alignment of inertial reference units." In Guidance, Navigation and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-3364.

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He, Zilu, Xiongzhu Bu, Yihan Cao, and Miaomiao Xu. "An Inertial / Altimetric / Infrared / Geomagnetic Integrated Navigation Method for Unmanned Aerial Vehicles." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10948.

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Abstract Unmanned aerial vehicle (UAV), is an unmanned aircraft operated by radio remote control equipment and self-contained program control device. When Unmanned Aerial Vehicle is on mission, its location information needs to be acquired to navigate it. Based on the research background of Unmanned Aerial Vehicle navigation information measurement, a passive integrated navigation mode of Inertial / Altimeter / Infrared / Geomagnetism Navigation method is designed in this paper. Firstly, several methods for Unmanned Aerial Vehicle navigation, including geomagnetic navigation, infrared navigation, inertial navigation and altimeter measurement, are introduced, and the advantages and disadvantages of various navigation methods are presented. Secondly, aiming at the problem that the output of barometric altimeter is affected by temperature and pressure, a temperature and pressure compensation model of barometric altimeter is established. Then, the attitude angle, velocity and position measurement models of inertial, infrared and geomagnetic navigation are established respectively. Finally, the data information of inertial / altimeter / infrared / geomagnetism measurement model is fused by extended Kalman filter and Unscented Kalman filter, and the signals processed by the two filters are compared. The reliability of the integrated navigation method is verified by simulation. This method has remarkable effect on improving the navigation accuracy of UAV and can make UAV suitable for many applications. It is simple and effective, and has wide application prospects.
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Wang, Maosong, Wenqi Wu, Xiaofeng He, and Xianfei Pan. "State Transformation Extended Kalman Filter for SINS based Integrated Navigation System." In 2019 DGON Inertial Sensors and Systems (ISS). IEEE, 2019. http://dx.doi.org/10.1109/iss46986.2019.8943781.

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Rezaifard, Elahe, and Pouya Abbasi. "Inertial navigation system calibration using GPS based on extended Kalman filter." In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE, 2017. http://dx.doi.org/10.1109/iraniancee.2017.7985144.

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Gao Fuquan, Ding Chuanhong, and Liu Jianfeng. "Initial alignment of strap down inertial navigation system using Kalman filter." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620546.

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Mourikis, Anastasios I., and Stergios I. Roumeliotis. "A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.364024.

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Wang, Jun-Hou, and Jia-Bin Chen. "Adaptive unscented Kalman filter for initial alignment of strapdown inertial navigation systems." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580847.

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Reports on the topic "Inertial navigation; Kalman filter"

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Haak, Jeffrey W. Verification of Robustified Kalman Filters for the Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) Data,. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada288609.

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Kelly, Alonzo. A 3D State Space Formulation of a Navigation Kalman Filter for Autonomous Vehicles. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada282853.

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