Download or read book Comparison of Batch and Kalman Filtering for Radar Tracking written by Haywood Satz and published by . This book was released on 2001 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radar tracking performance was compared among two choices of statistical filtering algorithms for the noisy measurements of exo-atmospheric objects in ballistic motion. Such motion is characteristic of satellites and missiles. Object position and velocity were governed by the nonlinear dynamics of body motion in a central force field, and measurements were modeled as nonlinear observations of those object motions in Cartesian coordinates. The two choices of statistical filtering algorithms were distinguished by their method of handling a sequence of noisy measurements. The first processed measurements, one-at-a-time, in a sequential recursive estimation using the Extended Kalman Filter (EKF), and the second processed that same sequence of measurements, simultaneously, in a batch-least-squares (BLS) estimation algorithm. Both algorithms used first-variation approximations of the nonlinear observations and error dynamics of object motion. Taylor series expansions were centered about the current best estimates of the state vector. The EKF used those approximations to implement the often referenced linear-minimum-variance (Kalman) estimation formulas. The BLS processed those same measurements simultaneously in a least-squares search over object trajectories spanning the tracking interval, and initial state estimation was based on convergence to the best object path. Results were obtained for both algorithms performing in a desktop program with a reasonable degree of radar systems modeling, and in a comprehensive mission simulator where radar system errors were represented in greater detail. Those included radar-cross-section fluctuations, scan angle loss, antenna gain patterns, radar signal-to-noise sensitivity, monopulse measurement errors, and front-end noise. The BLS algorithm was seen to converge faster, and predict more accurate 1-sigma values, than the EKF in all comparisons.
Download or read book Kalman Filtering Techniques for Radar Tracking written by K.V. Ramachandra and published by CRC Press. This book was released on 2018-03-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: A review of effective radar tracking filter methods and their associated digital filtering algorithms. It examines newly developed systems for eliminating the real-time execution of complete recursive Kalman filtering matrix equations that reduce tracking and update time. It also focuses on the role of tracking filters in operations of radar data processors for satellites, missiles, aircraft, ships, submarines and RPVs.
Download or read book Comparison of Four Filtering Options for a Radar Tracking Problem written by and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Four different filtering options are considered for the problem of tracking an exoatmospheric ballistic target with no maneuvers. The four filters are an alpha-beta filter, an augmented alpha-beta filter, a decoupled Kalman filter, and a fully-coupled extended Kalman filter. These filters are listed in the order of increasing computational complexity. All of the filters can track the target with some degree of accuracy. While the pure alpha-beta filter appreciably lags the other filters in performance for this problem, its augmented version is very competitive with the extended Kalman filter under benign conditions. Perhaps the most surprising result is that under all conditions examined, the decoupled (linear) Kalman filter, which is at least an order of magnitude less computationally complex, performs nearly identical to the coupled, extended Kalman filter. Four different filtering options are considered for the problem of tracking an exoatmospheric ballistic target with no maneuvers. The four filters are an alpha-beta filter, an augmented alpha-beta filter, a decoupled Kalman filter, and a fully-coupled extended Kalman filter. These filters are listed in the order of increasing computational complexity. All of the filters can track the target with some degree of accuracy. While the pure alpha-beta filter appreciably lags the other filters in performance for this problem, its augmented version is very competitive with the extended Kalman filter under benign conditions. Perhaps the most surprising result is that under all conditions examined, the decoupled (linear) Kalman filter, which is at least an order of magnitude less computationally complex, performs nearly identical to the coupled, extended Kalman filter.
Download or read book Beyond the Kalman Filter Particle Filters for Tracking Applications written by Branko Ristic and published by Artech House. This book was released on 2003-12-01 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
Download or read book Comparison of an alpha beta and Kalman Filter in Track While Scan Radars written by Dimitrios Emmanuel Mayiatis and published by . This book was released on 1979 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comparison of an Alpha Beta and Kalman Filter in Track While Scan Radars written by and published by . This book was released on 1979 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: The efficient use of a search-surface radar or sonar, in which one or more targets appear on the screen intermittently usually demands a device for tracking the targets automatically. Such a device, called a track while scan system, must make an estimate of each target's instantaneous position from the sampled-data information provided by the radar. For this purpose, an alpha-beta filter and an optimal Kalman filter, that must track maneuvering targets, are analyzed here and compared in terms of tracking accuracy for tactical applications.
Download or read book Tracking and Kalman Filtering Made Easy written by Eli Brookner and published by Wiley-Interscience. This book was released on 1998 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRACKING, PREDICTION, AND SMOOTHING BASICS. g and g-h-k Filters. Kalman Filter. Practical Issues for Radar Tracking. LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAY PROCESSING, AND EXTENDED KALMAN FILTER. Least-Squares and Minimum-Variance Estimates for Linear Time-Invariant Systems. Fixed-Memory Polynomial Filter. Expanding- Memory (Growing-Memory) Polynomial Filters. Fading-Memory (Discounted Least-Squares) Filter. General Form for Linear Time-Invariant System. General Recursive Minimum-Variance Growing-Memory Filter (Bayes and Kalman Filters without Target Process Noise). Voltage Least-Squares Algorithms Revisited. Givens Orthonormal Transformation. Householder Orthonormal Transformation. Gram--Schmidt Orthonormal Transformation. More on Voltage-Processing Techniques. Linear Time-Variant System. Nonlinear Observation Scheme and Dynamic Model (Extended Kalman Filter). Bayes Algorithm with Iterative Differential Correction for Nonlinear Systems. Kalman Filter Revisited. Appendix. Problems. Symbols and Acronyms. Solution to Selected Problems. References. Index.
Download or read book Comparison of Batch Means and Independent Replications Techniques to Applications of the Kalman Filter for Simulation Output Analysis written by Charles H. Porter and published by . This book was released on 1990 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this thesis was twofold. First, to develop software which automated the current simulation output analysis techniques of batch means and independent replications. Second, to determine the applicability of employing Kalman filtering for simulation output analysis on an M/M/1 queue. The Output Analysis Program incorporates batch means and independent replications into a menu driven, micro-computer program. The program allows the user to quickly analyze the simulation output graphically, discard the transient, and then perform the two techniques. The Kalman Filter proved valuable in providing insight into the underlying system variance in steady state, and appeared to offer several avenues for further research, particularly in the area of transient identification. (kr).
Download or read book Kalman Filtering written by Charles K. Chui and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: In addition to making a number of minor corrections and updat ing the references, we have expanded the section on "real-time system identification" in Chapter 10 of the first edition into two sections and combined it with Chapter 8. In its place, a very brief introduction to wavelet analysis is included in Chapter 10. Although the pyramid algorithms for wavelet decompositions and reconstructions are quite different from the Kalman filtering al gorithms, they can also be applied to time-domain filtering, and it is hoped that splines and wavelets can be incorporated with Kalman filtering in the near future. College Station and Houston Charles K. Chui September 1990 Guanrong Chen Preface to the First Edition Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fire control. With the recent development of high-speed computers, the Kalman filter has become more use ful even for very complicated real-time applications.
Download or read book The Extended Preferred Ordering Theorem for Radar Tracking Using the Extended Kalman Filter written by Donald Myron Leskiw and published by . This book was released on 2011 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Radar Data Processing With Applications written by He You and published by John Wiley & Sons. This book was released on 2016-08-01 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radar Data Processing with Applications Radar Data Processing with Applications He You, Xiu Jianjuan, Guan Xin, Naval Aeronautical and Astronautical University, China A summary of thirty years’ worth of research, this book is a systematic introduction to the theory, development, and latest research results of radar data processing technology. Highlights of the book include sections on data pre-processing technology, track initiation, and data association. Readers are also introduced to maneuvering target tracking, multiple target tracking termination, and track management theory. In order to improve data analysis, the authors have also included group tracking registration algorithms and a performance evaluation of radar data processing. Presents both classical theory and development methods of radar data processing Provides state-of-the-art research results, including data processing for modern radars and tracking performance evaluation theory Includes coverage of performance evaluation, registration algorithm for radar networks, data processing of passive radar, pulse Doppler radar, and phased array radar Features applications for those engaged in information engineering, radar engineering, electronic countermeasures, infrared techniques, sonar techniques, and military command Radar Data Processing with Applications is a handy guide for engineers and industry professionals specializing in the development of radar equipment and data processing. It is also intended as a reference text for electrical engineering graduate students and researchers specializing in signal processing and radars.
Download or read book Comparison of Batch Means and Independent Replications Techniques to Applications of the Kalman Filter for Simulation Output Analysis written by Charles H. Porter (CAPT, USAF.) and published by . This book was released on 1989 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Extended Preferred Ordering Theorem for Radar Tracking Using the Extended Kalman Filter written by Donald Myron Leskiw and published by . This book was released on 2019-08-08 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: A certain problem in nonlinear estimation exists in radar tracking. Usually radar detections provide instantaneous position measurements in radar (polar) coordinates at discrete times, while the tracks (estimated positions and motions over continuous time) are determined in rectangular coordinates using the Kalman filter, which is a linear estimator. And so most radar tracks tend to be biased and their covariance matrices inconsistent with the true ones. Of course, some techniques have been proposed for "debiasing" them. It is shown here, however, that the leading one can make the biases worse; a remedy for that is provided. But the focus here is upon the extended Kalman filter, which is a locally linearized estimator. In an earlier work by this author - dubbed the Preferred Ordering Theorem (POT) - it was shown that the linearization errors in the range direction can be virtually eliminated by using the measurement components of a detection recursively in the order azimuth first, range last. But that has a fundamental limitation, namely, that "preferred" order, and a range measurement component is required. So here a new version is provided, dubbed the Extended-POT (EPOT). Under it, not only is the update more efficient than the POT's, the measurements may be used in any order with virtually the same results.
Download or read book Kalman Filtering written by Mohinder S. Grewal and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a solid introduction to the theoretical and practical aspects of Kalman filtering. It has been updated with the latest developments in the implementation and application of Kalman filtering, including adaptations for nonlinear filtering, more robust smoothing methods, and developing applications in navigation. All software is provided in MATLAB, giving readers the opportunity to discover how the Kalman filter works in action and to consider the practical arithmetic needed to preserve the accuracy of results. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department -- to obtain the manual, send an email to [email protected].
Download or read book Theory and Applications of Kalman Filtering written by Cornelius T. Leondes and published by . This book was released on 1970 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents: Linear estimation theory; Further comments on the derivation of Kalman filters; Computational techniques in Kalman filtering; Modeling errors in Kalman filters; Suboptimal Kalman filter techniques; Comparison of Kalman, Bayesian and maximum likelihood estimation techniques; Nonlinear filtering and comparison with Kalman filtering; Linear smoothing techniques (post-flight data analysis); Nonlinear smoothing techniques; General questions on Kalman filtering in navigation systems; Application of Kalman filtering theory to augmented inertial navigation systems; Application of Kalman filtering to Baro/inertial height systems; Application of Kalman filtering to the C-5 guidance and control system; Application of Kalman filtering techniques to the Apollo program; Some applications of Kalman filtering in space guidance; Application of Kalman filtering for the alighnment of carrier aircraft inertial navigation systems; Navigation at sea using the invariants form of Kalman filtering; Marine applications of Kalman filtering; Optimal use of redundant information in an inertial navigation; Application of Kalman filtering techniques to strapdown system initia-alignment; and A Kalman filter augmented marine navigation system.
Download or read book FUSION written by and published by . This book was released on 2000 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Progress In Astronautics and Aeronautics written by Paul Zarchan and published by AIAA. This book was released on 2005 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt:
