Interference detection algorithm based on adaptive subspace tracking and RAIM for GNSS receiver. Issue 9 (25th July 2018)

• Record Type: Journal Article
• Authors: Wu, Qiang; Zheng, Jiansheng; Dong, Zhicheng; Su, Mingkun; Liang, Hexi; Zhang, Ping
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 9(2018)
• Page Start: 1028
• Page End: 1037
• Publication Date: 2018-07-25
• Subjects: time‐frequency analysis -- eigenvalues and eigenfunctions -- covariance matrices -- Fourier transforms -- Wigner distribution -- satellite navigation -- radio receivers -- radiofrequency interference; interference detection algorithm -- RAIM -- GNSS receiver -- global navigation satellite system receivers -- adaptive subspace tracking technique -- receiver autonomous integrity monitoring -- interference sources -- interference frequency -- narrowband interference environment -- subspace tracking algorithm -- eigenvalues statistic characteristics -- sample covariance matrix -- noise level -- time–frequency domain -- short‐time Fourier transform -- smooth pseudo‐Wigner‐Ville distribution; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2018.5175
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : The authors aim at providing an interference detection algorithm for global navigation satellite system (GNSS) receivers based on the adaptive subspace tracking technique and receiver autonomous integrity monitoring (RAIM). A complete framework, including an estimator used to determine the number of interference sources and a method for detecting the interference frequency, is presented in the narrowband interference environment. The estimator, applying both the eigenvalues' statistic characteristics of sample covariance matrix and the noise level of GNSS receiver, is more accurate than the conventional information theoretic criteria with simulation verification. Furthermore, the proposed subspace tracking algorithm outperforms the current frequency detection algorithms in time–frequency domain, such as short‐time Fourier transform and smooth pseudo‐Wigner‐Ville distribution. The theoretical analysis is verified by the real experiments under a GPS L1 scenario.