The CRLB for target localization using Gaussian process regression with distance-dependent noise

Abstract

The variance of distance-dependent noise exhibits different correlations with distance when the measurement method or device cost varies due to environmental interference. To efficiently model this correlation with less time cost, Gaussian Process Regression (GPR), which is characterized by its ability to learn nonlinear relationships with a small number of training samples, is utilized. Then, using the chain rule, the general CRLB can be applied under different measurement methods. It is represented by a function containing the GPR-based model and the relative position of the target and anchor. This means that the corresponding CRLB can be obtained quickly only by modeling the distance-dependent noise and determining measurement methods. Finally, to demonstrate the effectiveness of the GPR-based model and the validity of the derivation in this paper, two examples of time-of-arrival-based (TOA-based) and received signal strength-based (RSS-based) experiments are presented.