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相关概念视频

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

348
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...
348
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

337
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...
337
Classification of Signals01:30

Classification of Signals

889
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...
889
Sampling Theorem01:15

Sampling Theorem

763
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
763
Aliasing01:18

Aliasing

227
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
227
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

125
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
125

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使用时间频率重建网络和多项式 chirplet 变换进行中断采样近距离探测器信号的自动调制分类.

Guanghua Yi1, Xinhong Hao1,2, Xiaopeng Yan3,4

  • 1School of Mechatronics Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Scientific reports
|August 9, 2025
PubMed
概括

一种名为PCT-TFRNet的新方法增强了中断采样信号的自动调制分类 (AMC). 它即使在有限的数据和低的信号噪声比率下也能达到高准确度,从而改善了电子对策.

关键词:
自动调制分类自动调制分类被打断的采样近距离探测器信号.多项式的Chirplet转换是一个多项式的Chirplet转换.时间频率重建网络

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科学领域:

  • 信号处理 信号处理
  • 机器学习 机器学习
  • 电子战是一种电子战.

背景情况:

  • 自动调制分类 (AMC) 对电子对策至关重要.
  • 由于信号扭曲和数据丢失,中断采样 (IS) 条件严重挑战AMC.

研究的目的:

  • 提出一种新的AMC方法,PCT-TFRNet,适应IS条件和有限的数据.
  • 为了提高AMC的准确性和概括性,用于在具有挑战性的环境下接近探测器信号.

主要方法:

  • 使用多项式切尔普莱特转换 (PCT) 进行信号预处理和时间频率 (TF) 功能增强.
  • 采用一个时间频率重建网络 (TFRNet) 与一个不对称的编码器-解码器和适应性随机掩盖用于TF表示重建.
  • 实施自我监督的预培训策略,然后进行转移学习,以优化有限的标记IS样本的性能.

主要成果:

  • 在IS条件下,PCT-TFRNet方法显示了高分类准确性.
  • 在信号与噪声比 (SNR) 为-14dB的情况下,达到89%的准确性,在低SNR和小样本场景中展示了稳定性.
  • 该方法有效地从不完整的IS数据中重建完整的TF表示.

结论:

  • PCT-TFRNet提供了一种有效的解决方案,用于在中断采样下接近探测器信号的AMC.
  • 拟议的方法显示了在具有挑战性的信号环境中提高电子对策性能的重大前景.
  • PCT,TFRNet和高级学习策略的结合导致了卓越的稳定性和概括能力.