GPS - đường dẫn quán tính và hội nhập Part 8

We now consider the following, practical aspects of Kalman ®ltering applications: 1. how performance of the Kalman ®lter can degrade due to computer roundoff errors and alternative implementation methods with better robustness against roundoff; 2. how to determine computer memory, word length, and throughput requirements for implementing Kalman ®lters in computers; 3. ways to implement real-time monitoring and analysis of ®lter performance; | Global Positioning Systems Inertial Navigation and Integration Mohinder S. Grewal Lawrence R. Weill Angus P. Andrews Copyright 2001 John Wiley Sons Inc. Print ISBN 0-471-35032-X Electronic ISBN 0-471-20071-9 8 Kalman Filter Engineering We now consider the following practical aspects of Kalman filtering applications 1. how performance of the Kalman filter can degrade due to computer roundoff errors and alternative implementation methods with better robustness against roundoff 2. how to determine computer memory word length and throughput requirements for implementing Kalman filters in computers 3. ways to implement real-time monitoring and analysis of filter performance 4. the Schmidt-Kalman suboptimal filter designed for reducing computer requirements 5. covariance analysis which uses the Riccati equations for performance-based predictive design of sensor systems and 6. Kalman filter architectures for GPS INS integration. MORE STABLE IMPLEMENTATION METHODS Effects of Computer Roundoff Computer roundoff limits the precision of numerical representation in the implementation of Kalman filters. It has been shown to cause severe degradation of filter performance in many applications and alternative implementations of the Kalman filter equations the Riccati equations in particular have been shown to improve robustness against roundoff errors. 229 230 KALMAN FILTER ENGINEERING Computer roundoff for floating-point arithmetic is often characterized by a single parameter i-ol1i1 Oit which is the largest number such that 1 roundoff 1 in machine precision. The following example due to Dyer and McReynolds 32 shows how a problem that is well conditioned as posed can be made ill-conditioned by the filter implementation. Example Let In denote the n x n identity matrix. Consider the filtering problem with measurement sensitivity matrix 1 1 1 1 1 1 3 H and covariance matrices Po I3 and R d2I2 where d2 roundoff but 3 roundoff. In this case although H clearly has .

Không thể tạo bản xem trước, hãy bấm tải xuống
TỪ KHÓA LIÊN QUAN
TÀI LIỆU MỚI ĐĂNG
82    57    1    28-03-2024
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.