دانلود کتاب Bayesian Estimation and Tracking: A Practical Guide – تخمین و ردیابی بیزی: یک راهنمای عملی

توضیحات

A practical approach to estimating and tracking dynamic systems in real-worl applications

Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices.

Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.

Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB toolbox of estimation methods.

Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.

Content:
Chapter 1 Introduction (pages 110):
Chapter 2 Preliminary Mathematical Concepts (pages 1141):
Chapter 3 General Concepts of Bayesian Estimation (pages 4255):
Chapter 4 Case Studies: Preliminary Discussions (pages 5669):
Chapter 5 The Gaussian Noise Case: Multidimensional Integration of Gaussian?Weighted Distributions (pages 7185):
Chapter 6 The Linear Class of Kalman Filters (pages 8692):
Chapter 7 The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter (pages 93114):
Chapter 8 The Sigma Point Class: The Finite Difference Kalman Filter (pages 115127):
Chapter 9 The Sigma Point Class: The Unscented Kalman Filter (pages 128139):
Chapter 10 The Sigma Point Class: The Spherical Simplex Kalman Filter (pages 140147):
Chapter 11 The Sigma Point Class: The GaussHermite Kalman Filter (pages 148163):
Chapter 12 The Monte Carlo Kalman Filter (pages 164167):
Chapter 13 Summary of Gaussian Kalman Filters (pages 168175):
Chapter 14 Performance Measures for the Family of Kalman Filters (pages 176198):
Chapter 15 Introduction to Monte Carlo Methods (pages 199217):
Chapter 16 Sequential Importance Sampling Particle Filters (pages 218246):
Chapter 17 The Generalized Monte Carlo Particle Filter (pages 247256):
Chapter 18 A Spherical Constant Velocity Model for Target Tracking in Three Dimensions (pages 257307):
Chapter 19 Tracking a Falling Rigid Body Using Photogrammetry (pages 308345):
Chapter 20 Sensor Fusion Using Photogrammetric and Inertial Measurements (pages 346365):

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ترجمه ماشینی :

یک رویکرد عملی برای برآورد و ردیابی سیستم های پویا در برنامه های واقعی-با توجه روشهای گاوسی اغلب فاقد مشتق منسجم است. برآورد و ردیابی بیزی شکاف موجود در این زمینه را از هر دو حساب می پردازد ، و به خوانندگان یک مرور کلی از روش های تخمین سیستم های پویا خطی و غیرخطی رانده شده توسط صداهای گاوسی و غیر گاوسی ارائه می دهد.