An Indoor Unknown Radio Emitter Positioning Approach Using Improved RSSD Location Fingerprinting. (28th February 2023)

• Record Type: Journal Article
• Authors: Zhang, Liyang; Liu, Kunlei; Pan, Zhiyou; Pan, Lei; Gao, Rui; Zhang, Qian
• Other Names: Rouabah Khaled Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2023(2023)
• Publication Date: 2023-02-28
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2023/5462081
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : The accurate location of an unknown radio emitter (URE) is a critical task in wireless communication security. The URE localization method based on the received signal strength difference (RSSD) has become popular due to the identification of unknown transmitting power and frequency. However, high computational complexity and low positioning accuracy have been caused by the RSSD fingerprint data's redundancy and cross-correlation. In this article, an indoor RSSD-based positioning algorithm combining principal component analysis (PCA) and Pearson correlation coefficient (PCC), called RSSD-PCA-PCC, is proposed to realize efficient feature extraction and reduce false fingerprint matching. Firstly, to achieve reduction and decorrelation, the principal components of the RSSD fingerprint database are extracted by the singular value decomposition (SVD) method. Secondly, the PCC is applied to measure the relative distance between the principal component features. In particular, the PCC is used for selecting the reference points (RPs) in order to match the position accurately. The results show that the proposed algorithm can obtain a more superior performance compared with the conventional RSSD-based weighted k-nearest neighbor algorithm (RSSD-WKNN) and COS matching algorithm (RSSD-PCA-COS) in the case of different selected RP numbers, AP numbers, and grid distances.