Title :
A Computationally Efficient Nonlinear Least Squares Method Using Random Basis Functions
Author_Institution :
Dept. of Electr., Comput., & Biomed. Eng., Univ. of Rhode Island, Kingston, RI, USA
Abstract :
A method to obtain parameter estimates in a nonlinear least squares problem is proposed. Its main advantage is computational efficiency and can be used when a direct grid search cannot. The approach is based on the theory of random basis functions. An example of its application to frequency estimation is given and other applications discussed.
Keywords :
frequency estimation; least mean squares methods; random functions; signal representation; frequency estimation; nonlinear least square method; parameter estimation; random basis function; signal representation; Bandwidth; Frequency estimation; Least squares approximations; Maximum likelihood estimation; Quantization (signal); Vectors; Estimation; signal representations; signal resolution;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2264808
