Title
Asymptotic properties of nonlinear weighted least squares in radar array processing
Abstract
This paper treats nonlinear weighted least squares parameter estimation of sinusoidal signals impinging on a sensor array. We give a consistency proof for a more general model than what has been previously considered in the analysis of two-dimensional (2-D) sinusoidal fields. Specifically, the array can have an arbitrary shape, and spatially colored noise is allowed. Further, we do not impose the restriction of unique frequencies within each dimension, and the number of samples is assumed large in only the temporal dimension. In addition to consistency, we establish that the parameter estimates are multivariate Gaussian distributed under a large class of noise distributions. The finite sample performance is investigated via computer simulations, which illustrate that a recommended two-step procedure yields asymptotically efficient estimates when the noise is Gaussian. The first step is necessary for estimating the weighting matrix, which has a dramatic influence on the performance in the studied scenarios. The number of samples required to attain the Crame´r-Rao lower bound is found to coincide with the point where the signal sources are separated by more than one discrete Fourier transform bin. This remains true even when the signals emanate from the same direction of arrival (DOA).
Year
DOI
Venue
2004
10.1109/TSP.2004.836459
IEEE Transactions on Signal Processing
Keywords
Field
DocType
sensor array,large class,consistency proof,sinusoidal signal,parameter estimate,finite sample performance,asymptotic property,radar array processing,noise distribution,squares parameter estimation,signals emanate,sinusoidal field,direction of arrival,noise,computer simulation,lower bound,discrete fourier transform,gaussian distribution,parameter estimation,colored noise,asymptotic analysis
Least squares,Array processing,Colors of noise,Mathematical analysis,Direction of arrival,Control theory,Sensor array,Gaussian,Discrete Fourier transform,Statistics,Gaussian noise,Mathematics
Journal
Volume
Issue
ISSN
52
11
1053-587X
Citations 
PageRank 
References 
3
0.50
0
Authors
2
Name
Order
Citations
PageRank
J. Eriksson130.50
M. Viberg2917188.13