The problem of estimating parameters theta which determine the mean mu ( theta ) of a Gaussian-distributed observation X is considered. It is noted that the maximum-likelihood (ML) estimate, in this case the least-squares estimate, has desirable statistical properties but can be difficult to compute when mu ( theta ) is a nonlinear function of theta . An estimate formed by combining ML estimates based on subsections of the data vector X is proposed as a computationally inexpensive alternative. The main result is that this alternative estimate, termed here the divide-and-conquer (DAC) estimate, has ML performance in the small-error region when X is appropriately subdivided. As an example application, an inexpensive range-difference-based position estimator is derived and shown by means of Monte-Carlo simulation to have small-error-region mean-square error equal to the Cramer-Rao lower bound.<>
A divide and conquer approach to least-squares estimation
IEEE Transactions on Aerospace and Electronic Systems ; 26 , 2 ; 423-427
1990-03-01
314058 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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