287,72 €
319,69 €
-10% with code: EXTRA
Intuitive Understanding of Kalman Filtering with MATLAB(R)
Intuitive Understanding of Kalman Filtering with MATLAB(R)
287,72
319,69 €
  • We will send in 10–14 business days.
The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follo…
  • Publisher:
  • ISBN-10: 0367191350
  • ISBN-13: 9780367191351
  • Format: 15.6 x 23.4 x 1.6 cm, hardcover
  • Language: English
  • SAVE -10% with code: EXTRA

Intuitive Understanding of Kalman Filtering with MATLAB(R) (e-book) (used book) | bookbook.eu

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The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm

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  • Author: Armando Barreto
  • Publisher:
  • ISBN-10: 0367191350
  • ISBN-13: 9780367191351
  • Format: 15.6 x 23.4 x 1.6 cm, hardcover
  • Language: English English

The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm

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