Someone who'd like to deepen his/her knowledge in Kalman filters would read this book. What is it? Kalman filters find applications at a majority of domains ranging from telecommunications systems, aerospace and naval engineering, statistics and econometrics, time-series, chemical operations, target monitoring, GPSs, satellites, ship navigation, tank liquid level, prediction of temperatures, etc.
Kalman filters are a few simple but very powerful mathematic equations and algorithms that make predictions - smoothing, estimation, prediction - based on past, present and future respectively, structured around a number of measurements!
They prove that quite simple math - for university students - entail complex phenomena and that it is the power of equations that give birth to huge .... lists of applications. Furthermore, measurements and advanced calculations are nowadays at the heart of AI and computational sciences, where no formula is memorized by heart and no book can be applied 100% on a mere single problem, as it actually revises and extends a part of previous education!
It seems that sending spacecrafts to the moon could finally turn into a reality that underlies the importance of ... math and science!
A moral lesson out of this, is that it's the measurement that's important even to economy and business and if you can't make educated guesses, you'd better not do it at all...
Book Summary
This graduate-level text augments and extends beyond undergraduate studies of signal processing, particularly in regard to communication systems and digital filtering theory. Vital for students in the fields of control and communications, its contents are also relevant to students in such diverse areas as statistics, economics, bioengineering, and operations research.
Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; time-invariant filters; properties of Kalman filters; computational aspects; and smoothing of discrete-time signals. Additional subjects encompass applications in nonlinear filtering; innovations representations, spectral factorization, and Wiener and Levinson filtering; parameter identification and adaptive estimation; and colored noise and suboptimal reduced order filters. Each chapter concludes with references, and four appendixes contain useful supplementary material.
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