基于能量选择TQWT和自适应SVD的混沌信号消噪
1School of Science, Wuhan University of Science and Technology, Wuhan, 430000, China.
Scientific reports
|November 2, 2023
概括
一种新的混沌信号消噪算法结合了可调的Q因子波量变换 (TQWT) 和自适应单数值分解 (ASVD) 以有效降低噪声. 这种TQWT-ASVD方法显著改善了信号噪声比,并减少了混乱信号中的错误.
科学领域:
- 信号处理 信号处理
- 非线性动力学是一种非线性动力学.
- 数据分析 数据分析
背景情况:
- 混沌信号往往受到低信号对噪声比率 (SNR) 的影响.
- 未知的动态系统参数使传统的消噪方法复杂化.
- 对于分析混乱系统而言,有效的无线化是至关重要的.
研究的目的:
- 提出一种新的算法来消除具有低SNR和未知参数的混乱信号.
- 与现有方法相比,提高混乱信号消噪的性能.
- 在模拟和真实世界的噪音数据上验证拟议方法的有效性.
主要方法:
- 结合了可调整的Q因子波形变换 (TQWT) 来进行信号分解和可适应的单数值分解 (ASVD) 来消除噪音.
- 使用最大波段理论和能量值规则进行准确的子频段分解.
- 在ASVD中使用单数值子集的标准偏差来确定有效的重建顺序,以改善噪声抑制.
主要成果:
- 与SVD,TQWT,CEEMDAN-WT和MEEMD-LMS相比,TQWT-ASVD方法在消除混乱信号方面表现优越.
- 在信号与噪声比 (SNR) 中取得了显著的改进,根平均平方误差 (RMSE) 减少,并降低了排列 (PE) 和模糊 (FE).
- 具体来说,SNR增加了高达26.46%,RMSE下降了高达39.48%,PE下降了高达41.96%,FE下降了高达33.66%.
结论:
- 拟议的TQWT-ASVD算法在消除混乱信号方面非常有效,特别是那些SNR较低的信号.
- 该方法提供了强大的噪声抑制,并比传统技术更好地保留信号特征.
- 在各种应用中,TQWT-ASVD为分析复杂和杂的混乱系统提供了有价值的工具.
更多相关视频
相关概念视频
Energy and Power Signals
308
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
308
Reconstruction of Signal using Interpolation
209
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
209
Classification of Signals
482
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
482
Sampling Continuous Time Signal
255
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
255
Deconvolution
165
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
165
Basic Continuous Time Signals
216
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
216


