A signal-dependent quadratic time frequency distribution for neural source estimation
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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This correspondence is concerned with the problem of estimating the parameters of constant amplitude chirp signals embedded in noise. An estimation algorithm based on a simple iterative approach is proposed whose main characteristics include accuracy, reduction in the error propagation effect, and operation over a wide range of phase parameter values. The estimation is quite robust against additive noise and is shown to achieve the Cramer-Rao lower bound even at relatively low signal-to-noise ratios. Simulation results are presented that justify the viability of the new algorithm and demonstrate its better performance as compared with the existing techniques