Least-Squares Covariance Matrix Adjustment

Least-Squares Covariance Matrix Adjustment

We consider the problem of finding the smallest adjustment to a given symmetric n × n matrix, as measured by the Euclidean or Frobenius norm, so that it satisfies some given linear equalities and inequalities, and in addition is positive semidefinite. This least-squares covariance adjustment problem is a convex optimization problem, and can be efficiently solved using standard methods when the ...

Covariance shaping least-squares estimation

A new linear estimator is proposed, which we refer to as the covariance shaping least-squares (CSLS) estimator, for estimating a set of unknown deterministic parameters x observed through a known linear transformation H and corrupted by additive noise. The CSLS estimator is a biased estimator directed at improving the performance of the traditional least-squares (LS) estimator by choosing the e...

Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations

Geocoding Sar-interferograms by Least Squares Adjustment

Several geocoding methods for SAR interferometry are compared. The discussion results in the proposal of a new geocoding model. It is based on a least squares adjustment combining interferometric phase, range, Doppler centroid frequency, flight path and control point data. A complete mathematical framework for the computation of object space coordinates without approximations is presented. This...

Generalization Based on Least Squares Adjustment

The paper presents solutions for generalization problems using least squares adjustment theory. This concept allows for the introduction of several observations in terms of constraints and for a holistic solution of all these – possibly contrary and competing – constraints. Two examples are used to demonstrate the validity of this approach: the simplification of building ground plans and the di...