Enhanced RSSD-based unknown radio emitter localization via weighted grey correlation degree

Abstract Accurate tracking of indoor unknown radio emitter (URE) is a key task in the field of wireless communication security. Fingerprint positioning methods based on received signal strength (RSS) difference (RSSD) perform well on URE localization challenges with unknown transmitting power and frequency. Since the noise level of RSSD parameters is amplified compared to RSS, the matching accuracy between RSSD signal features and geographical locations is reduced. This paper presents a RSSD-based positioning algorithm using improved singular value decomposition (SVD) and weighted grey correlation degree (GCD), called ISVD-WGCD, to increase noise robustness and positioning accuracy. Firstly, local least squares curve fitting is applied to optimize the selection of singular value truncation point to reduce the noise level of RSSD offline database. Secondly, combining the offline RSSD singular value variation coefficient and online RSSD standard deviation, an access point (AP) weight allocation scheme is designed to set the trust degrees of different APs reasonably. Finally, a RSSD-based weighted GCD method is proposed by AP weight allocation to improve the matching accuracy between offline and online RSSDs, and to select the optimal reference points. Compared with the conventional weighted K -nearest neighbors, SVD-KNN, Bayes, convolutional neural network and FUZZY-GREY algorithms, simulation and experimental results show that the proposed method can obtain superior positioning performance under different AP numbers, grid distances and noise levels.