Principles of GNSS, inertial, and multi-sensor integrated navigation systems / by Groves, Paul D. (Paul David), PALCI EBSCO books

Table of Contents: “…-- Inertial Navigation -- Radio and Satellite Navigation -- Feature Matching -- The Complete Navigation System -- Navigation Mathematics -- Coordinate Frames, Kenematics, and the Earth -- Coordinate Frames -- Kinematics -- Earth Surface and Gravity Models -- Frame Transformations -- The Kalman Filter -- Introduction -- Algorithms and Models -- Implementation Issues -- Extensions to the Kalman Filter -- Navigation Systems -- Inertial Sensors -- Acceleromoters -- Gyroscopes -- Inertial Measurement Units -- Error Characteristics -- Inertial Navigation -- Inertial-Frame Navigation Equations -- Earth-Frame Navigation Equations -- Local-Navigation-Frame Navigation Equations -- Navigations Equations Precision -- initialization and Alignment -- INS Error Propagation -- Platform INS -- Horizontal-Plane Inertial Navigation -- Satellite Navigation Systems -- Fundamentals of Satellite Navigation -- Global Positioning System -- GLONASS -- Galileo -- Regional Navigation Systems -- GNSS Interoperability -- Satellite Navigation Processing, Errors, and Geometry -- Satellite Navigation Geometry -- Receiver Hardware and Antenna -- Ranging Processor -- Range Error Sources -- Navigation Processor -- Advanced Satellite Navigation -- Differential GNSS -- Carrier-Phase Positioning and Attitude -- Poor Signal-to-Noise Environments -- Multipath Mitigation -- Signal Monitoring -- Semi-Codeless Tracking -- Terrestrial Radio Navigation -- Point-Source Systems -- Loran -- Instrument Landing System -- Urban and Indoor Positioning -- Relative Navigation -- Tracking -- Sonar Transponders -- Dead Reckoning, attitude, and Height Measurement -- Height and Depth Measurement -- Odometers -- Pedestrian Dead Reckoning -- Doppler Radar and Sonar -- Other Dead-Reckoning Techniques -- Feature Matching -- Terrain-Referenced NAvigation -- Image Matching -- Map Matching -- Other Feature-Matching Techniques -- Integrated Navigation -- INS/GNSS Integration -- Integration Architectures -- System Model and State Selection -- Measurement Models -- Advanced INS/GNSS Integration -- INS Alignment and Zero Velocity Updates -- Transfer Alignment -- Quasi-Stationary Alignment with Unknown Heading -- Quasi-Stationary Fine Alignment and Zero Velocity Updates -- Multisensor Integrated Navigation -- Integration Architectures -- Terestrial Radio Navigation -- Dead Reckoning, Attitude, and Height Measurement -- Feature Mapping -- Fault Detection and Integrity Monitoring -- Failure Modes -- Range Checks -- Kalman Filter Measurement Innovations -- Direct Consistency Checks -- Certified Integrity Monitoring -- Vectors and Matrices -- Introduction to Vectors -- Introduction to Matrices -- Special Matrix Types -- Matrix Inversion -- Calculus -- Statistical Measures -- Mean, Variance, and Standard Deviation -- Probability Density Function -- Gaussian Distribution -- Chi-Square Distribution.…”
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Introduction to probability and statistics for engineers and scientists / by Ross, Sheldon M., PALCI EBSCO books

Table of Contents: “…Cover -- Contents -- Preface -- CHAPTER 1 INTRODUCTION TO STATISTICS -- 1.1 INTRODUCTION -- 1.2 DATA COLLECTION AND DESCRIPTIVE STATISTICS -- 1.3 INFERENTIAL STATISTICS AND PROBABILITY MODELS -- 1.4 POPULATIONS AND SAMPLES -- 1.5 A BRIEF HISTORY OF STATISTICS -- CHAPTER 2 DESCRIPTIVE STATISTICS -- 2.1 INTRODUCTION -- 2.2 DESCRIBING DATA SETS -- 2.2.1 Frequency Tables and Graphs -- 2.2.2 Relative Frequency Tables and Graphs -- 2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots -- 2.3 SUMMARIZING DATA SETS -- 2.3.1 Sample Mean, Sample Median, and Sample Mode -- 2.3.2 Sample Variance and Sample Standard Deviation -- 2.3.3 Sample Percentiles and Box Plots -- 2.4 CHEBYSHEV'S INEQUALITY -- 2.5 NORMAL DATA SETS -- 2.6 PAIRED DATA SETS AND THE SAMPLE CORRELATION COEFFICIENT -- CHAPTER 3 ELEMENTS OF PROBABILITY -- 3.1 INTRODUCTION -- 3.2 SAMPLE SPACE AND EVENTS -- 3.3 VENN DIAGRAMS AND THE ALGEBRA OF EVENTS -- 3.4 AXIOMS OF PROBABILITY -- 3.5 SAMPLE SPACES HAVING EQUALLY LIKELY OUTCOMES -- 3.6 CONDITIONAL PROBABILITY -- 3.7 BAYES' FORMULA -- 3.8 INDEPENDENT EVENTS -- CHAPTER 4 RANDOM VARIABLES AND EXPECTATION -- 4.1 RANDOM VARIABLES -- 4.2 TYPES OF RANDOM VARIABLES -- 4.3 JOINTLY DISTRIBUTED RANDOM VARIABLES -- 4.3.1 Independent Random Variables -- *4.3.2 Conditional Distributions -- 4.4 EXPECTATION -- 4.5 PROPERTIES OF THE EXPECTED VALUE -- 4.5.1 Expected Value of Sums of Random Variables -- 4.6 VARIANCE -- 4.7 COVARIANCE AND VARIANCE OF SUMS OF RANDOM VARIABLES -- 4.8 MOMENT GENERATING FUNCTIONS -- 4.9 CHEBYSHEV'S INEQUALITY AND THE WEAK LAW OF LARGE NUMBERS -- CHAPTER 5 SPECIAL RANDOM VARIABLES -- 5.1 THE BERNOULLI AND BINOMIAL RANDOM VARIABLES -- 5.1.1 Computing the Binomial Distribution Function -- 5.2 THE POISSON RANDOM VARIABLE -- 5.2.1 Computing the Poisson Distribution Function -- 5.3 THE HYPERGEOMETRIC RANDOM VARIABLE -- 5.4 THE UNIFORM RANDOM VARIABLE -- 5.5 NORMAL RANDOM VARIABLES -- 5.6 EXPONENTIAL RANDOM VARIABLES -- *5.6.1 The Poisson Process -- *5.7 THE GAMMA DISTRIBUTION -- 5.8 DISTRIBUTIONS ARISING FROM THE NORMAL -- 5.8.1 The Chi-Square Distribution -- 5.8.2 The t-Distribution -- 5.8.3 The F-Distribution -- *5.9 THE LOGISTICS DISTRIBUTION -- CHAPTER 6 DISTRIBUTIONS OF SAMPLING STATISTICS -- 6.1 INTRODUCTION -- 6.2 THE SAMPLE MEAN -- 6.3 THE CENTRAL LIMIT THEOREM -- 6.3.1 Approximate Distribution of the Sample Mean -- 6.3.2 How Large a Sample Is Needed? …”
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Spectrum and network measurements / by Witte, Robert A., PALCI EBSCO books

Table of Contents: “…Mean, Variance, and Standard Deviation -- 8.3. Power Spectral Density -- 8.4. …”
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