Parameter estimation for superimposed chirp signals

Parameter estimation for superimposed chirp signals is a difficult signal processing problem that shows up in many applications. Cramer-Rao lower bounds are derived here for the error variance in the parameter estimates. The approach reported uses global Hankel rank reduction to estimate instantaneous frequencies followed by total least squares fitting to obtain initial estimates of the parameters. These estimates are used to initialize a search for the maximum likelihood estimates. Results on synthetic data are compared with the Cramer-Rao lower bounds.<
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