03466nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153050002000167072001700187072002300204072001600227100007800243245012100321250001800442264007200460300006600532336002600598337002600624338003600650347002400686505083000710520098701540650002502527650001402552650001602566650002702582650001802609650002302627650003502650650004302685650004202728710003402770773002602804776003602830776003602866776003602902856004602938978-981-99-8063-5DE-He21320251030111801.0cr nn 008mamaa240401s2024    si |    s    |||| 0|eng d  a97898199806357 a10.1007/978-981-99-8063-52doi 4aTJ212-225 4aTJ210.2-211.495 7aTJFM2bicssc 7aTEC0070002bisacsh 7aTJFM2thema1 aFadali, M. Sami.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aIntroduction to Random Signals, Estimation Theory, and Kalman Filteringh[electronic resource] /cby M. Sami Fadali.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXXI, 480 p. 118 illus., 79 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aReview of Probability Theory -- Random Variables -- Random Signals (autocorrelation, power spectral density) -- Response of Linear Systems to Random Inputs (continuous, discrete) -- Estimation and Estimator Properties (small sample and large sample properties of estimators, CRLB) -- Least Square Estimation Likelihood (likelihood function, detection) -- Maximum Likelihood Estimation -- Minimum Mean-Square Error Estimation (Kalman Filter, information filter, filter stability) -- Generalizing the Basic Kalman Filter (colored noise, correlated noise, reduced-order estimator, Schmidt Kalman filter sequential computation) -- Prediction and Smoothing -- Nonlinear Filtering (Extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, particle filter) -- The Expectation Maximization Algorithm -- Markov Models.  aThis book provides first-year graduate engineering students and practicing engineers with a solid introduction to random signals and estimation. It includes a statistical background that is often omitted in other textbooks but is essential for a clear understanding of estimators and their properties. The book emphasizes applicability rather than mathematical theory. It includes many examples and exercises to demonstrate and learn the theory that makes extensive use of MATLAB and its toolboxes. Although there are several excellent books on random signals and Kalman filtering, this book fulfills the need for a book that is suitable for a single-semester course that covers both random signals and Kalman filters and is used for a two-semester course for students that need remedial background. For students interested in more advanced studies in the area, the book provides a bridge between typical undergraduate engineering education and more advanced graduate-level courses. 0aControl engineering. 0aRobotics. 0aAutomation. 0aAerospace engineering. 0aAstronautics. 0aTelecommunication.14aControl, Robotics, Automation.24aAerospace Technology and Astronautics.24aCommunications Engineering, Networks.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981998062808iPrinted edition:z978981998064208iPrinted edition:z978981998065940uhttps://doi.org/10.1007/978-981-99-8063-5