Starting from an initial guess of the parameters, the method consists in finding iteratively a sequence of estimates of θ, where each estimate is based on the previous one.
This is an iterative technique that generates a sequence of estimates X k, k=0,1,2, … that will converge to a solution provided that the initial estimate X 0 is itself close enough to the intersection.
The G-N approach performs iterative gradient descent, starting with an initial estimate X 0. It generates a sequence of estimates as follows: begin{array}rcl@ boldsymbol{X}_{k+1} &=& boldsymbol{X}_{k} - boldsymbol{delta}_{k} end{array} (18).
In addition, although target tracking per se is not considered in this paper, performance is evaluated both under the assumption that sequential location estimates are not aggregated as well as under the assumption that some sort of tracker is available to aggregate a sequence of estimates.
The expectation-maximization (EM) algorithm [12], which is tailored to deal with a nuisance parameter such as c, produces a sequence of estimates ν Ì‚ ( i ), i = 1,2,... according to ν Ì‚ ( i ) = arg max ν E ln p ( r | ν, c ) p | r, ν Ì‚ ( i âˆ' 1 ), (10).
As discussed in Wu [ 9], if the likelihood function is unimodal and satisfies some differentiability conditions, EM algorithm produces a sequence of estimates that converge to the actual MLE.
The course error grows in time since the estimate is the sum of a long sequence of estimated changes in course, see (16), and there are no course measurements available.
We develop a complete convergence theory for the Maximum Entropy method based on moment matching for a sequence of approximate statistical moments estimated by the Multilevel Monte Carlo method.
Output: a sequence of the estimated hidden states ({hat {mathbf {y}}_{1 T}}).
