Optimal Sensors Deployment for Tracking Level Curve Based on Posterior Cramer-Rao Lower Bound in Scalar Field

摘要

This paper focuses on discussing the space distance of gliders in a group for level curve tracking task. A developed adaptive space distance algorithm for glider formation based on Posterior Cramer-Rao Lower Bound (PCRLB) is proposed. For a feature-tracking application with scalar sensors, gliders are adopted to track a level curve in 2D space. In this work, the white noise from the measurement process and oceanic background is taken into account, as well as the effect of omitting the higher order terms in the Taylor series and roughly estimated Hessian Matrix. Since the PCRLB is an effective criterion to quantify the performance of all unbiased nonlinear estimators of the target state, our adaptive space distance algorithm for gliders may be functional when implemented with many kinds of nonlinear filters together. Finally, the performance of the proposed algorithm in this study is evaluated on simulated platforms by applying it with the Extended Kalman Filter (EKF) and Particle Filter.