A New Estimation Algorithm for Frequency and Amplitude in Harmonic Signal Processing

Abstract

Low-rank matrix recovery is a large-scale data analysis and processing technology; its related theory has been widely used in image restoration, image denoising, video background modeling, signal recovery and other fields. This paper proposes an improved inexact augmented Lagrange multiplier (IALM) method to solve the harmonic recovery (HR) problem. After that, the performance of the original IALM and the improved IALM are compared when the sparse matrix is a diagonal sparse matrix satisfying certain conditions, and the results show that the improved IALM algorithm is more stable than the original algorithm. Then the improved strategy of the algorithm is extended to two kinds of occasions in which the positions of non-zero elements in sparse matrix are fixed or random, which provides a way to improve the algorithm in different application scenarios. Finally, the original IALM algorithm and the improved IALM algorithm are used to solve the HR problem, and the experimental results show that the improved IALM algorithm has better solution performance.

Keywords

• Low-rank matrix recovery
• IALM
• improved strategy
• HR
• diagonal sparse matrix