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The problem addressed is the design of a filter that minimizes the trace of the estimation error variance.
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Subsequently, filter parameters are determined by minimizing the trace of the derived upper bound.
By adopting the structure of the extended Kalman filter, the gain matrix is determined by minimizing the trace of the prescribed upper bound matrix.
By minimizing the trace of estimation error covariances, estimator׳s parameters are derived, which are the solutions to discrete Riccati difference equations.
The least-squares methods which minimize the trace of eigenmode matrix suggested by Pešek and Lallement, respectively, for self-adjoint systems are extended to general non-defective systems in this paper.
Famous examples for such scalar objectives are the A-criterion, the E-criterion, or the D-criterion, which aim at minimizing the trace, maximum eigenvalue, or determinant of the variance covariance matrix.
Our proposed measurement matrix optimization method is based on minimizing the trace of the Cramer Rao lower bound (CRLB) matrix in the presence of signal-dependent interference and receiver noise, which leads to a nonlinear and non-convex optimization problem.
Specifically, the goal is to choose (at design-time) a subset of sensors (satisfying certain budget constraints) from a given set in order to minimize the trace of the steady state a priori or a posteriori error covariance produced by a Kalman filter.
This is equivalent to minimizing the trace of x ^ x ^ *.
Given initial value of β and α, one can estimate α by minimizing the trace of (hat {Omega } alpha)).
First, we consider this problem by minimizing the trace of the constrained biased CRB, which is referred to as the Trace-opt criterion [7].
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