lec-34 - Administration Midterm regrades due by November...

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Unformatted text preview: Administration Midterm regrades due by November 28th after lecture. CS70: Satish Rao: Lecture 34. 1. Kalman filter. Kalman filtering. Land Spacecraft on moon. Kalman filtering. Land Spacecraft on moon. Control!!! Kalman filtering. Land Spacecraft on moon. Control!!! How high? Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! ???) Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! ???) ... Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! ???) ... General problem: recover “trajectory” from noisy observations. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! ???) ... General problem: recover “trajectory” from noisy observations. Inference problem: given data, what is parameter. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! ???) ... General problem: recover “trajectory” from noisy observations. Inference problem: given data, what is parameter. Given radar bounces, what is path of missile. Kalman filtering. Land Spacecraft on moon. Control!!! How high? Noisy Sensors. Control. Missile tracking. Signal processing. Computer Vision. Finance. (The 1%! ???) ... General problem: recover “trajectory” from noisy observations. Inference problem: given data, what is parameter. Given radar bounces, what is path of missile. Apollo mission: Kalman filter. Model. Missile: ( x 1 , y 1 , z 1 ) , ( x 2 , y 2 , z 2 ) ,... Model. Missile: ( x 1 , y 1 , z 1 ) , ( x 2 , y 2 , z 2 ) ,... Model: X 1 , X 2 ,... Model. Missile: ( x 1 , y 1 , z 1 ) , ( x 2 , y 2 , z 2 ) ,......
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This note was uploaded on 02/29/2012 for the course COMPSCI 70 taught by Professor Rau during the Fall '11 term at Berkeley.

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lec-34 - Administration Midterm regrades due by November...

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