Constraint Sparse Maximum Correntropy Beamforming Algorithm against Impulsive Noise to Improve the Performance of GNSS Signal Acquisition

• Published in: International journal of antennas and propagation Vol. 2019; no. 2019; pp. 1 - 14
• Main Authors: Zhang, Haichuan, Zeng, Fangling
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; Hindawi Limited
• Subjects: Adaptive filters; Algorithms; Beamforming; Computer simulation; Convergence; Energy consumption; Global navigation satellite system; Methods; Noise; Outliers (statistics); Performance enhancement; Power consumption; Principal components analysis; Random variables; Receivers & amplifiers; Signal processing; Sparsity; System theory
• ISSN: 1687-5869; 1687-5877
• DOI: 10.1155/2019/6193049

This paper presents the new constraint correntropy beamforming method to improve the performance of global navigation satellite system (GNSS) receivers. Although beamforming-based GNSS system possesses the ability of nulling interference sources, the distortions to satellite signal induced by impulsive noises are always neglected. This paper addresses the satellite navigation signal acquisition problem against impulsive noises using the constraint maximum correntropy criterion in framework of the GNSS system. In addition, in order to decrease the number of active elements for avoiding overmuch energy consumption, we introduce the L1-norm penalty equation to the list of constraints of the adaptive filter that forces the coefficients with small magnitudes to zero. From the above, we propose the norm-constraint beamforming method to satisfy the conflicting requirements between the coefficients sparsity and the performance of the satellite signal acquisition. The proposed CSMC maintains the robustness against impulsive outliers and achieve better performance in conjunction with less power consumption. An analysis of the mean square convergence properties of the proposed algorithms is presented, and the stability condition of the convergence is derived. Our simulation results demonstrate the superiority of the proposed methods.