基于子相关函数分解与重构的半整数CPM信号无二义采集算法

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Rui Xue, Mingming Xie
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引用次数: 0

摘要

半整数调制指数大于1的连续相位调制(CPM)具有频谱分裂、优越的跟踪性能和兼容性。然而,半整数CPM信号的自相关函数(ACF)存在多个侧峰,给信号采集带来了模糊威胁。为此,针对半整数CPM信号,提出了一种基于子相关函数分解与重构(DRSCF)的无二义采集算法。该算法对劳伦特分解后的第一脉冲调幅波形进行进一步分解,得到适合于CPM信号的子信号波形,并通过子相关函数的非线性组合重建无二义相关函数。随后,利用ACF进行能量损失补偿。理论分析和仿真结果表明,该算法以一定的检测性能损失为代价,有效地消除了半整数CPM信号中的模糊威胁,并保持了较窄的相关峰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel unambiguous acquisition algorithm based on decomposition and reconstruction of sub-correlation functions for semi-integer CPM signals
Continuous phase modulation (CPM) with a semi-integer modulation index greater than 1 exhibits spectral splitting, superior tracking performance, and compatibility. However, the multiple side peaks in the autocorrelation function (ACF) of the semi-integer CPM signals introduce ambiguity threats in signal acquisition. Therefore, a novel unambiguous acquisition algorithm based on decomposition and reconstruction of sub-correlation functions (DRSCF) is proposed for semi-integer CPM signals. The algorithm further decomposes the first pulse amplitude modulation waveform after Laurent decomposition to obtain sub-signal waveforms suitable for CPM signals and reconstructs the unambiguous correlation function by a nonlinear combination of sub-correlation functions. Subsequently, energy loss compensation is performed using ACF. Theoretical analysis and simulation results show that the proposed DRSCF algorithm effectively eliminates the ambiguity threat in the of semi-integer CPM signals at the expense of some detection performance loss, and maintaining the narrow correlation peak.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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