Abstract:
We pose the estimation of the parameters of multiple superimposed exponential signals in additive Gaussian noise problem as a maximum likelihood (ML) estimation problem. ...Show MoreMetadata
Abstract:
We pose the estimation of the parameters of multiple superimposed exponential signals in additive Gaussian noise problem as a maximum likelihood (ML) estimation problem. The ML problem is non linear and hard to solve. Some previous works focused on finding alternative estimation procedures, for example by denoising. In contrast, we tackle the ML estimation problem directly. First, we use the same transformation as the first step of iterative quadratic maximum likelihood (IQML) and transform the ML problem into another optimization problem that gets rid of the amplitude coefficients. Second, we solve the remaining optimization problem with a gradient descent approach ("pseudo-quadratic maximum likelihood"). We also use this algorithm for ultra-wideband channel estimation and estimate ranging in non-line of sight environment.
Published in: 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications
Date of Conference: 03-07 September 2007
Date Added to IEEE Xplore: 04 December 2007
ISBN Information: