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Programming Assignments

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Introduction   ::   System Model   ::   Expectation   ::   Part 1   ::   Filters   ::   Part 2   ::   Submission

Programming Assignment Part 2

  • Implement an LMS adaptive filter function in M ATLAB . You will want to write it so that the filter memory is passed in (so that you may have multiple matched filters in your code. It might be implement it so that it is declared as
                  [x,e, uvect,w] = function lms(u,d,uvectin,win,mu)
                 
    where:
    • u and d represent the filter input and desired signal, respectively,
    • uvectin represents $ \ubf(t-1)$ (the memory contents of the filter from the last time,
    • win represents $ \wbf^{[k]}$ , the filter weights from the last time,
    • mu represents $ \mu$ , the adaptive filter step size,
    • x and e represent the filter output and filter error, respectively,
    • uvect represents $ \ubf(t)$ (to be used next time around)
    • w represents $ \wbf^{[k+1]}$ (to be used next time around).
  • Use your adaptive filter function to replace the prediction block and filtering block of the system identification. (You will need two adaptive filters.) Try various values of $ \mu$ to see which give good convergence.
  • Make a plot of the filter output error as a function of time for the two filters for various values of $ \mu$ .
Copyright 2008, by the Contributing Authors. Cite/attribute Resource . admin. (2006, June 13). Programming Assignments. Retrieved January 07, 2011, from Free Online Course Materials — USU OpenCourseWare Web site: http://ocw.usu.edu/Electrical_and_Computer_Engineering/Stochastic_Processes/sysid_6.htm. This work is licensed under a Creative Commons License Creative Commons License