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Seminar On Kalman Filter And Its Applications

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Barnali Dey
Barnali Dey

seminar1,introduction to kalman filter in communication system.

1  sur  13
Seminar on Kalman Filter and its applications Presented by: Ms. Barnali Dey M.Tech (DEAC) SMIT
Overview ,[object Object],[object Object],[object Object],[object Object]
The Estimation Problem ,[object Object],[object Object],Measuring Devices Estimator Measurement Error Sources System State (desired but not known) External Controls Observed Measurements Optimal Estimate of System State System Error Sources System Black Box
What is a Kalman Filter? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithm for discrete-time Kalman Filter Given system model in state space in discrete time Given measurement equation
For recursive algorithm to work, set following initial conditions ,[object Object],[object Object],[object Object],Set to very small value Set to high value
Theoretical Basis Prediction (Time Update) (1) Project the state ahead (2) Project the error covariance ahead Correction (Measurement Update) (2) Compute the Kalman Gain (3) Correct the estimate with measurement z(k+1) (4) Update Error Covariance matrix (1) Observe new measurement at (k+1)th instant Correction term
Block Diagram representation of Kalman Filter B U.D H B U.D H A A K(k) A Z(k) v(k) w(k) X(k+1) X(k) + - + +
Application –DSSS ( direct sequence spread spectrum) communication ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
General Channel Estimation Procedure
Why Channel Estimation ? ,[object Object],[object Object],[object Object]
Types of Channel Estimation Methods ,[object Object],[object Object],[object Object]
Algorithm Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Seminar On Kalman Filter And Its Applications

  • 1. Seminar on Kalman Filter and its applications Presented by: Ms. Barnali Dey M.Tech (DEAC) SMIT
  • 2.
  • 3.
  • 4.
  • 5. Algorithm for discrete-time Kalman Filter Given system model in state space in discrete time Given measurement equation
  • 6.
  • 7. Theoretical Basis Prediction (Time Update) (1) Project the state ahead (2) Project the error covariance ahead Correction (Measurement Update) (2) Compute the Kalman Gain (3) Correct the estimate with measurement z(k+1) (4) Update Error Covariance matrix (1) Observe new measurement at (k+1)th instant Correction term
  • 8. Block Diagram representation of Kalman Filter B U.D H B U.D H A A K(k) A Z(k) v(k) w(k) X(k+1) X(k) + - + +
  • 9.
  • 11.
  • 12.
  • 13.