Estimation Procedures for Robust Sensor Control

TL;DR

This paper evaluates three estimation techniques—extended Kalman filter, discrete Bayes, and iterative Bayes—for robust sensor control in nonlinear measurement systems, highlighting their advantages and limitations through mathematical analysis and simulations.

Contribution

It provides a comparative analysis of estimation methods for sensor control in nonlinear systems, including new insights into their operational conditions and performance.

Findings

Extended Kalman filter may be unsuitable for certain sensor control scenarios.

Discrete Bayes approximation has specific limitations discussed.

Simulation results illustrate conditions favoring each estimation technique.

Abstract

Many robotic sensor estimation problems can characterized in terms of nonlinear measurement systems. These systems are contaminated with noise and may be underdetermined from a single observation. In order to get reliable estimation results, the system must choose views which result in an overdetermined system. This is the sensor control problem. Accurate and reliable sensor control requires an estimation procedure which yields both estimates and measures of its own performance. In the case of nonlinear measurement systems, computationally simple closed-form estimation solutions may not exist. However, approximation techniques provide viable alternatives. In this paper, we evaluate three estimation techniques: the extended Kalman filter, a discrete Bayes approximation, and an iterative Bayes approximation. We present mathematical results and simulation statistics illustrating operating conditions where the extended Kalman filter is inappropriate for sensor control, and discuss issues in the use of the discrete Bayes approximation.