Reducing geometric dilution of precision using ridge regression

Abstract

A signal processing technique is proposed for improving position-fix navigation system accuracy performance when the geometry of the navigation landmarks (e.g. sensors) are nearly collinear. In the navigation literature, the accuracy degradation associated with a nearly collinear measure geometry is termed the geometric dilution of precision (GDOP). Its presence causes not only the variance of the position estimates to be highly inflated but also any bias terms which may be present in the model. Since a nearly collinear predictor matrix is mathematically equivalent to GDOP, it is proposed to use the ridge regression technique in a navigation signal processor. A position-fix algorithm based on ridge regression reduces the bias and variance inflation caused by GDOP and the overall mean-squared position error as well. Ridge regression contains the GDOP-sensitive least-mean-square (LMS) estimator as a special case. Even with a matched model, GDOP can inflate the mean-square error (MSE) of the ordinary least-squares estimator, whereas the ridge regression technique chooses a suitable biased estimator that will reduce the MSE, which is the main goal. The ridge concept is extended to include GDOP-amplified bias errors. A simple range/range navigation system is analyzed to illustrate the underlying principles of ridge regression.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>