基于Matlab读取标准RINEX格式的GPS星历数据
基于Matlab读取标准RINEX格式的GPS星历数据，采用Matlab直接读取Rinex文件张妮,等基于 Matlab读取标准 RINEX格式的GPS星历数据navdata(i). day str2num(line(10: 12));Le Edit yie seb finds Helpnavdata(i). hour=str2num (line(13: 15));navdata(i). minute str2num(line(16: 18))navdata (i).second str2num (line(19: 22));navdata(i). af0= str2num(line(23: 41))2:s10122-0041.4e012]【5.917e012]t30e95a-012navdata(i). afl str2num (line(42: 60));4.480-00navdata(i). af2 str2num (line(61: 79));4.600T+00619.8348←[6.419e007line fgetl(fid); %o second linem…2107007]navdata(i). aode str2num(line(4: 22))205-00【--007[7.2643c0090.9161【0.922】navdata().crs str2num(line(23: 41))navdata (i).dn= str2num(line(42: 60))图3读取星历数据结果navdata(i) Anomaly str2num (line(61: 79))Fig 3 Reading result of ephemeris dat4)关闭文件 status= fclose(fid)。骤,利用 Matlab矩阵的计算优势,很方便地计算不同时刻卫22星历数据的读取星的坐标,此外,还可方便查看卫星导航表层信息,判断导航星历数据的读取采用结构体数组显示的相关命令读取,数据的质量。若要获取某个卫星的相关参数可输入如示例命令:>>navdata(1)。获取结果如图2所示,为1号卫星参数读取结果3结论利用 Matlab以矩阵为单位进行计算的优势对 RINEX文件进行读取,较其他语言简单易行,结果精确,程序可移植性好,便于后续数据处理,同时还可利用 Matlab的仿真功能,实现卫星动态变化的实时模拟afl:1.47T9e-012参考文献:[1]陈东银,刘立龙,陈雷.GPS导航定位技术中面向对象读取 RINEX格式数据!测绘与空间地理信,2009(6):41-43.chen DorInCHen Lei. An ohmethod of reading rineX formatdat in GPS navigation tech-图21号卫星参数读取结果nology [J]. Geomatics and Spatial Information TechnologyFig. 2 Reading result of parameter of satliete NO.2009(6):41-43也可利用 Matlab元包数组,将数据存放并显示出来,具|2 I Gurtner W. RINEX: The receiver independent exchange for体实现代码如下mat: Version 2.10[M] Canada: Astronomical Institute UniverFN=fieldnamesnavdata)sity of Berne, 2002size=size(FN)3]孟广祥,郭标明.CPS接收机(OEM)二进制文件向 RINEXnavdata year;文件的转换[,测绘工程,2009(10:18-21.nav data=cell(oh+1);MENG Guang-xiang. Guo Biao-ming. The transformationsIzefrom GPS receiver (OEM) binary data to RINEX file[J]. Enfor i= 2: noph +1gineering of Surveyying and Mapping, 2009(10): 18-21onavcell=char(navdata year)[4]陈桂珍,戴建军.GPS-OEM原始数据向 RINEX格式转换nav_data(n, 1= char(FN(n));的方法[测绘技术装备,2006(4):26-27nav_data n, i= navdata(i-1).(char(FN(n)))CHENG Gui-zhen, DAI Jian-jun. The transformation fromGPS-OEM orignal data to RINEX Format[J]. Surveying techendnical equipment, 2006(4): 26-27读取结果如图3所示。5]张志涌.精通 Matlab6.5[M]北京:北京航空航天大学出版采用 Matlab软件读 RINEX导航文件,可以将文件所有社,2004的数据用矩阵保存,数据的显示精度不仅不会影响计算精6 Chapman Stephen J. Matlab Programming for engineers [M度,而且可以随时修改,并可根据卫星坐标的计算公式和步北京:科学出版社,200325基于Ma1ab读取标准RINX格式的GPS星历数据旧万据WANFANG DATA文献链接作者张妮,王标标, ZHANG NI, WANG Biao-biao作者单位张妮, ZHANG Ni(西安工业大学北方信息工程学院,陕西,西安,710025),王标标, WANG Biaobiao(中国人民解放军96275部队,河南洛阳,471003)刊名:电子设计工程sTc英文刊名ELECTRONIC DESIGN ENGINEERING年,卷(期):2010,18(8参考文献(6条)1. Chapman Stephen J Matlab Programming for engineers 20032.张志涌精通 Matlab6.520043.陈桂珍;戴建军 GPS-OEM原始数据向 RINEX格式转换的方法[期刊论文]测绘技术装备2006(044.孟广祥;郭标明GPS接收机(OEM二进制文件向 RINEX文件的转换2009(10)5. Gurtner W RINEX: The receiver independent exchange format: Version 2.10 2002陈东银;刘立龙;陈雷GPS导航定位技术中面向对象读取 RINEX格式数据2009(06)本文链接http://d.g.wanfangdata.comcn/periodiCaldzsjgc201008007.aspx
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MATLAB在卡尔曼滤波器中应用的理论与实践Kalman
MATLAB在卡尔曼滤波器中应用的理论与实践KalmanKALMAN FILTERINGTheory and Practice Using MATLABThird editionMOHINDER S GREWALCalifornia State University at FullertonANGUS P. ANDREWSRockwell Science Center (retired)WILEYA JOHN WILEY & SONS, INC. PUBLICATIONCopyright 2008 by John Wiley sons, Inc. All rights reservedPublished by John Wiley sons, InC, Hoboken, New JerseyPublished simultaneously in CanadaNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or byany means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permittedunder Section 107 or 108 of the 1976 United States Copyright Act, without either the prior writtenpermission of the Publisher, or authorization through payment of the appropriate per-copy fee to theCopyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA 01923,(978)750-8400, fax(978)750-4470,oronthewebatwww.copyright.com.RequeststothePublisherforpermissionshouldbe addressed to the Permissions Department, John Wiley Sons, Inc, lll River Street, Hoboken, NJ07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permissionimit of liability Disclaimer of Warranty: While the publisher and author have used their best efforts inpreparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability orfitness for a particular purpose. No warranty may be created or extended by sales representatives orwritten sales materials. The advice and strategies contained herein may not be suitable for your situationYou should consult with a professional where appropriate. Neither the publisher nor author shall be liablefor any loss of profit or any other commercial damages, including but not limited to special, incidentalconsequential, or other damagesFor general information on our other products and services or for technical support, please contact ourCustomer Care Department within the United States at(800)762-2974, outside the United States at(317)572-3993 or fax(317)572-4002Wiley also publishes its books in a variety of electronic formats. Some content that appears in print maynot be available in electronic format. For more information about wiley products, visit our web site atwww.wiley.comLibrary of Congress Cataloging- in-Publication DataGrewal. Mohinder sKalman filtering: theory and practice using MATLAB/Mohinder S. GrewalAngus p. andrews. 3rd edIncludes bibliographical references and indexISBN978-0-470-17366-4( cloth)1. Kalman filtering. 2. MATLAB. I. Andrews, Angus P. II. TitleQA402.3.G69520086298312—dc22200803733Printed in the United States of america10987654321CONTENTSPrefaceAcknowledgmentsXIIIList of abbreviationsXV1 General Information1.1 On Kalman Filtering1.2 On Optimal Estimation Methods, 51. 3 On the notation Used In This book 231. 4 Summary, 25Problems. 262 Linear Dvnamic Systems2. 1 Chapter focus, 312.2 Dynamic System Models, 362. 3 Continuous Linear Systems and Their Solutions, 402.4 Discrete Linear Systems and Their Solutions, 532.5 Observability of Linear Dynamic System Models, 552.6 Summary, 61Problems. 643 Random Processes and Stochastic Systems3.1 Chapter Focus, 673.2 Probability and random Variables (rvs), 703.3 Statistical Properties of RVS, 78CONTEN3.4 Statistical Properties of Random Processes(RPs),803.5 Linear rp models. 883.6 Shaping Filters and State Augmentation, 953.7 Mean and Covariance propagation, 993.8 Relationships between Model Parameters, 1053.9 Orthogonality principle 1143.10 Summary, 118Problems. 1214 Linear Optimal Filters and Predictors1314.1 Chapter Focus, 1314.2 Kalman Filter. 1334.3 Kalman-Bucy filter, 1444.4 Optimal Linear Predictors, 1464.5 Correlated noise Sources 1474.6 Relationships between Kalman-Bucy and wiener Filters, 1484.7 Quadratic Loss Functions, 1494.8 Matrix Riccati Differential Equation. 1514.9 Matrix Riccati Equation In Discrete Time, 1654.10 Model equations for Transformed State Variables, 1704.11 Application of Kalman Filters, 1724.12 Summary, 177Problems. 1795 Optimal Smoothers5.1 Chapter Focus, 1835.2 Fixed-Interval Smoothing, 1895.3 Fixed-Lag Smoothing, 2005.4 Fixed-Point Smoothing, 2135.5 Summary, 220Problems. 226 Implementation Methods2256. 1 Chapter Focus, 2256.2 Computer Roundoff, 2276.3 Effects of roundoff errors on Kalman filters 2326.4 Factorization Methods for Square-Root Filtering, 2386. 5 Square-Root and UD Filters, 2616.6 Other Implementation Methods, 2756.7 Summary, 288Problems. 2897 Nonlinear Filtering2937.1 Chapter Focus, 2937.2 Quasilinear Filtering, 296CONTENTS7.3 Sampling Methods for Nonlinear Filtering, 3307.4 Summary, 345Problems. 3508 Practical Considerations3558.1 Chapter Focus. 3558.2 Detecting and Correcting Anomalous behavior, 3568.3 Prefiltering and Data Rejection Methods, 3798.4 Stability of Kalman Filters, 3828. 5 Suboptimal and reduced- Order Filters, 3838.6 Schmidt-Kalman Filtering, 3938.7 Memory, Throughput, and wordlength Requirements, 4038.8 Ways to Reduce Computational requirements 4098.9 Error Budgets and Sensitivity Analysis, 4148.10 Optimizing Measurement Selection Policies, 4198.11 Innovations analysis, 4248.12 Summary, 425Problems. 4269 Applications to Navigation4279.1 Chapter focus, 4279.2 Host vehicle dynamics, 4319.3 Inertial Navigation Systems(INS), 4359. 4 Global Navigation Satellite Systems(GNSS), 4659.5 Kalman Filters for GNSS. 4709.6 Loosely Coupled GNSS/INS Integration, 4889.7 Tightly Coupled GNSS /INS Integration, 4919. 8 Summary, 507Problems. 508Appendix A MATLAB Software511A 1 Notice. 511A 2 General System Requirements, 511A 3 CD Directory Structure, 512A 4 MATLAB Software for Chapter 2, 512A. 5 MATLAB Software for Chapter 3, 512A6 MATLAB Software for Chapter 4, 512A. 7 MATLAB Software for Chapter 5, 513A 8 MATLAB Software for Chapter 6, 513A 9 MATLAB Software for Chapter 7, 514A10 MATLAB Software for Chapter 8, 515A 11 MATLAB Software for Chapter 9, 515A 12 Other Sources of software 516CONTENAppendix b A Matrix Refresher519B. 1 Matrix Forms. 519B 2 Matrix Operations, 523B 3 Block matrix Formulas. 527B 4 Functions of Square Matrices, 531B 5 Norms. 538B6 Cholesky decomposition, 541B7 Orthogonal Decompositions of Matrices, 543B 8 Quadratic Forms, 545B 9 Derivatives of matrices. 546Bibliography549Index565PREFACEThis book is designed to provide familiarity with both the theoretical and practicalaspects of Kalman filtering by including real-world problems in practice as illustrativeexamples. The material includes the essential technical background for Kalman filter-ing and the more practical aspects of implementation: how to represent the problem ina mathematical model, analyze the performance of the estimator as a function ofsystem design parameters, implement the mechanization equations in numericallystable algorithms, assess its computational requirements, test the validity of resultsitor the filteThetant attributes ofthe subject that are often overlooked in theoretical treatments but are necessary forapplication of the theory to real-world problemsIn this third edition, we have included important developments in the implemen-tation and application of Kalman filtering over the past several years, including adaptations for nonlinear filtering, more robust smoothing methods, and develelopingapplications in navigationWe have also incorporated many helpful corrections and suggefrom ourreaders, reviewers, colleagues, and students over the past several years for theoverall improvement of the textbookAll software has been provided in MatLab so that users can take advantage ofits excellent graphing capabilities and a programming interface that is very close tothe mathematical equations used for defining Kalman filtering and its applicationsSee Appendix a for more information on MATLAB softwareThe inclusion of the software is practically a matter of necessity because Kalmanfiltering would not be very useful without computers to implement it. It provides aMATLAB is a registered trademark of The Mathworks, IncEFACEbetter learning experience for the student to discover how the Kalman filter works byobserving it in actionThe implementation of Kalman filtering on computers also illuminates some of thepractical considerations of finite-wordlength arithmetic and the need for alternativealgorithms to preserve the accuracy of the results. If the student wishes to applywhat she or he learns, then it is essential that she or he experience its workingsand failings--and learn to recognize the differenceThe book is organized as a text for an introductory course in stochastic processes atthe senior level and as a first-year graduate-level course in Kalman filtering theory andapplicationIt can also be used for self-instruction or for purposes of review by practi-cing engineers and scientists who are not intimately familiar with the subject. Theorganization of the material is illustrated by the following chapter-level dependencygraph, which shows how the subject of each chapter depends upon material in otherchapters. The arrows in the figure indicate the recommended order of study. Boxesabove another box and connected by arrows indicate that the material represented bythe upper boxes is background material for the subject in the lower boxAPPENDIX B: A MATRIX REFRESHERGENERAL INFORMATION2. LINEAR DYNAMIC SYSTEMSRANDOM PROCESSES AND STOCHASTIC SYSTEMS4. OPTIMAL LINEAR FILTERS AND PREDICTORS5. OPTIMAL SMOOTHERS6. IMPLEMENTATIONMETHODS7. NONLINEAR8. PRACTICAL9. APPLICATIONSFILTERINGCONSIDERATIONSTO NAVIGATIONAPPENDIX A: MATLAB SOFTWAREChapter l provides an informal introduction to the general subject matter by wayof its history of development and application. Chapters 2 and 3 and Appendix b coverthe essential background material on linear systems, probability, stochastic processesand modeling. These chapters could be covered in a senior-level course in electricalcomputer, and systems engineeringChapter 4 covers linear optimal filters and predictors, with detailed examples ofapplications. Chapter 5 is a new tutorial-level treatment of optimal smoothing
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