Selecting wavelengths for least squares range estimation

TL;DR

This paper develops an algorithm to optimally select wavelengths for least squares range estimation, improving accuracy by minimizing mean square error through lattice properties and Monte Carlo validation.

Contribution

It introduces a novel wavelength selection algorithm for least squares range estimators, enhancing accuracy over ad hoc methods.

Findings

Wavelength selection significantly impacts estimation accuracy.

The proposed algorithm outperforms ad hoc wavelength choices.

Monte Carlo simulations confirm improved accuracy.

Abstract

We consider the problem of estimating the distance, or range, between two locations by measuring the phase of multiple sinusoidal signals transmitted between the locations. Traditional estimators developed for optical interferometry include the beat wavelength and excess fractions methods. More recently, estimators based on the Chinese remainder theorem (CRT) and least squares have appeared. Recent research suggests the least squares estimator to be most accurate in many cases. The accuracy of all of these range estimators depends upon the wavelengths chosen. This leads to the problem of selecting wavelengths that maximise accuracy. Procedures for selecting wavelengths for the beat wavelength and excess fractions methods have previously been described, but procedures for the CRT and least squares estimators are yet to be developed. In this paper we develop an algorithm to automatically select wavelengths for use with the least square range estimator. The algorithm minimises an optimisation criterion connected with the mean square error. Interesting properties of a particular class of lattices simplify the criterion allowing minimisation by depth first search. Monte-Carlo simulations indicate that wavelengths that minimise the criterion can result is considerably more accurate range estimates than wavelengths selected by ad hoc means.