Iterative Method of Maximum Likelihood for State Estimation with Inequality Constraints
ICCSEE), 2012 International Conference(2012)
Abstract
An iterative method of incorporating state inequality constraints in kalman filter is proposed. The constrained filter is derived as the maximum posteriori solution to the constraints, a penalty function is used to transform the inequality constraints, and the solution to the set of estimates is obtained by using Gaussian Newton method. At each time step the unconstrained kalman filter solution is projected onto the state Constraint surface. A target tracking example is presented demonstrating the efficiency of the algorithm.
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Key words
kalman filters,newton method,maximum likelihood estimation,state estimation,target tracking,gaussian newton method,kalman filter,iterative method,maximum posteriori solution,penalty function,state constraint surface,state inequality constraint,inequality linearly constraints,unscented kalman filter (ukf),mathematical model
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