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EEWORLD大学堂
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嵌入式开发
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数字信号处理器（DSP）
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卡尔曼滤波器
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课程目录
课程笔记
课时1：The Kalman Filter (1 of 55) What is a Kalman Filter
课时2：The Kalman Filter (2 of 55) Flowchart of a Simple Example (Single Measured Value)
课时3：The Kalman Filter (3 of 55) The Kalman Gain- A Closer Look
课时4：The Kalman Filter (4 of 55) The 3 Calculations of the Kalman Filter
课时5：The Kalman Filter (5 of 55) A Simple Example of the Kalman Filter
课时6：The Kalman Filter (6 of 55) A Simple Example of the Kalman Filter (Continued)
课时7：The Kalman Filter (7 of 55) The Multi-Dimension Model 1
课时8：The Kalman Filter (8 of 55) The Multi-Dimension Model 2-The State Matrix
课时9：The Kalman Filter (9 of 55) The Multi-Dimension Model 3- The State Matrix
课时10：The Kalman Filter (10 of 55) 4- The Control Variable Matrix
课时11：The Kalman Filter (11 of 55) 5- Find the State Matrix of a Falling Object
课时12：The Kalman Filter (12 of 55) 6- Update the State Matrix
课时13：The Kalman Filter (13 of 55) 7- State Matrix of Moving Object in 2-D
课时14：The Kalman Filter (14 of 55) 8- What is the Control Variable Matrix
课时15：The Kalman Filter (15 of 55) 9- Converting from Previous to Current State 2-D
课时16：The Kalman Filter (16 of 55) 10- Converting from Previous to Current State 3-D
课时17：The Kalman Filter (17 of 55) 11- Numerical Ex. of Finding the State Matrix 1-D
课时18：The Kalman Filter (18 of 55) What is a Covariance Matrix
课时19：The Kalman Filter (19 of 55) What is a Variance-Covariance Matrix
课时20：The Kalman Filter (20 of 55) Example of Covariance Matrix and Standard Deviation
课时21：The Kalman Filter (21 of 55) Finding the Covariance Matrix, Numerical Ex. 1
课时22：The Kalman Filter (22 of 55) Finding the Covariance Matrix, Numerical Ex. 2
课时23：The Kalman Filter (23 of 55) Finding the Covariance Matrix, Numerical Example
课时24：The Kalman Filter (24 of 55) Finding the State Covariance Matrix- P=
课时25：The Kalman Filter (25 of 55) Explaining the State Covariance Matrix
课时26：The Kalman Filter (26 of 55) Flow Chart of 2-D Kalman Filter - Tracking Airplane
课时27：The Kalman Filter (27 of 55) 1. The Predicted State - Tracking Airplane
课时28：The Kalman Filter (28 of 55) 2. Initial Process Covariance - Tracking Airplane
课时29：The Kalman Filter (29 of 55) 3. Predicted Process Covariance - Tracking Airplane
课时30：The Kalman Filter (30 of 55) 4. Calculate the Kalman Gain - Tracking Airplane
课时31：The Kalman Filter (31 of 55) 5. The New Observation - Tracking Airplane
课时32：The Kalman Filter (32 of 55) 6. Calculate Current State - Tracking Airplane
课时33：The Kalman Filter (33 of 55) 7. Update Process Covariance - Tracking Airplane
课时34：The Kalman Filter (34 of 55) 8. Current Becomes Previous - Tracking Airplane
课时35：The Kalman Filter (35 of 55) 1, 2, 3 of Second Iteration - Tracking Airplane
课时36：The Kalman Filter (36 of 55) 4. Kalman Gain Second Iteration - Tracking Airplane
课时37：The Kalman Filter (37 of 55) 5, 6 of Second Iteration - Tracking Airplane
课时38：The Kalman Filter (38 of 55) 7, 8 of Second Iteration - Tracking Airplane
课时39：The Kalman Filter (39 of 55) Part 1 of Third Iteration - Tracking Airplane
课时40：The Kalman Filter (40 of 55) Part 2 of Third Iteration - Tracking Airplane
课时41：The Kalman Filter (41 of 55) Graphing 1st 3 Iterations (t vs x) - Tracking Airplane
课时42：The Kalman Filter (42 of 55) Graphing 1st 3 Iterations (t vs v) - Tracking Airpl
时长：6分20秒
日期：2019/10/07
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木犯001号
课程介绍
解释什么是卡尔曼
滤波器
并如何使用它
上传者： 木犯001号
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