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 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 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 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 10 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 Kalman Filtering written by Charles K. Chui and published by Springer Science & Business Media. This book was released on 2008-11-23 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Kalman Filtering with Real-Time Applications" presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. The last two topics are new additions to this third edition. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge.
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 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 Optimal Radar Tracking Systems written by George Biernson and published by Wiley-Interscience. This book was released on 1990-04-03 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a state-of-the-art presentation of optimal radar tracking systems based on the sophisticated Altair radar, which uses Kalman filtering to perform optimal long-range tracking of ballistic missile warheads. This engineering example offers a means for explaining Kalman filter theory and many other technical issues critical to the design of a modern optimal radar tracking system, all in arelatively simple manner. Material includes discussion of feedback control, modulation and demodulation of signals, digital sampled-data systems, digital computer simulation, statistical analysis of random signals, detection and tracking processes in a radar system. This study of Altair features a considerable amount of detail concerning the operation of a complex electronic system, thereby presenting a study that is unusual in the unclassified literature.
Download or read book Extended Kalman Filter for Integrating Tracking Data from Ground based Radar and Airborne Global Positioning System written by Mark Peter Green and published by . This book was released on 1998 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Precision Radar Pointing and Tracking Using an Adaptive Extended Kalman Filter written by Raymond Lee Kolibaba (1LT, USAF.) and published by . This book was released on 1973 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Extended Kalman Filter for the Guidance of Surface to Air Using Ground Based Radar written by K.E. Cottrell and published by . This book was released on 1985 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kalman Filtering written by Mohinder S. Grewal and published by John Wiley & Sons. This book was released on 2015-02-02 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Download or read book Estimation with Applications to Tracking and Navigation written by Yaakov Bar-Shalom and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: * Problems that apply theoretical material to real-world applications * In-depth coverage of the Interacting Multiple Model (IMM) estimator * Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators * Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.
Download or read book A Performance Comparison of an Extended Kalman and Second Order Filters Used for Tracking Task written by Y.S. Al-Masri and published by . This book was released on 1983 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Filtering written by Brian D. O. Anderson and published by Courier Corporation. This book was released on 2012-05-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graduate-level text extends studies of signal processing, particularly regarding communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; discrete-time Kalman filter; time-invariant filters; more. 1979 edition.
