Kernel principal component analysis for UWB-based ranging

Abstract

Abstract-Accurate positioning in harsh environments can enable many application, such as search-and-rescue in emergency situations. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, it still faces a problem in non-line-of-sight (NLOS) environments, in which range estimates based on time-of-arrival (TOA) are positively biased. There are many techniques that try to address this problem, mainly based on NLOS identification and NLOS error mitigation. However, these techniques do not exploit all available information from the UWB channel impulse response. In this paper, we propose a novel ranging technique based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto nonlinear orthogonal highdimensional space, and a subset of these projections is then used for ranging. We tested this technique using UWB measurements obtained in a basement tunnel of Linköping university, and found that it provides much better ranging performance comparing with standard techniques based on PCA and TOA.

NLOS Identification for UWB Body Communications

“…Principal Component Analysis (PCA) is a well-known linear method for feature extraction and dimensionality reduction; it reduces the redundancy by calculating the eigenvectors of the covariance matrix of the input [20]. The PCA is for objective only allows linear dimensionality reduction.…”

Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity

“…The goal is to perform ranging using all available channel parameters from the PDP given by (1). Since some of the kernel methods operate with centered and dimensionless data, we first transform the channel parameters α k (k = 1, .…”

“…However, in some applications it may be too computationally complex, especially, if there are many training samples. In this section, we describe an alternative approach based on kPCA, which is proposed in a preliminary form in [1]. 5 A variation of SVM can provide a measure of uncertainty, but in an adhoc way, as pointed out in [23,Section 6.4].…”

“…This work was supported by the project Cooperative Localization (CoopLoc) funded by the Swedish Foundation for Strategic Research (SSF), and Security Link. A part of this work was presented at IEEE SPAWC workshop, June 2014 [1]. careful action is taken.…”

“…This paper is a comprehensive extension of our conference paper[1]. New material includes: a detailed description of GPR for ranging, two new hybrid methods (kPCA+ and kPCA+GPR), and many additional experiments.…”