Channel Impulse Response Measurements-Based Location Estimation Using Kernel Principal Component Analysis

Zhigang CHEN  Xiaolei ZHANG  Hussain KHURRAM  He HUANG  Guomei ZHANG  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E99-A   No.10   pp.1876-1880
Publication Date: 2016/10/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E99.A.1876
Type of Manuscript: LETTER
Category: Digital Signal Processing
channel impulse response (CIR),  kernel principal component analysis (KPCA),  positioning,  support vector regression,  

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In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.