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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

117
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
117
Aliasing01:18

Aliasing

166
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...
166
Downsampling01:20

Downsampling

196
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
196
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Upsampling01:22

Upsampling

266
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
266
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

108
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,...
108

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相关实验视频

Updated: Jul 27, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

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压缩域中频率跳跃信号的参数估计算法 基于改进的原子字典

Weipeng Zhu1, Yourui Wang1, Hu Jin1

  • 1Electronic Countermeasure Institute, National University of Defense Technology, Hefei 230037, China.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括

本研究引入了一种新的算法,用于在非合作环境中估计频率跳跃信号参数. 该方法可以独立估计中心频率和跳跃时间,提高准确性和效率.

科学领域:

  • 信号处理 信号处理
  • 通信工程 通信工程
  • 电子战是一种电子战.

背景情况:

  • 非合作信号参数估计对于电子情报和频谱监测至关重要.
  • 现有的方法经常与独立参数估计和信号重建要求作斗争.
  • 频率跳跃信号由于其动态性质而带来了独特的挑战.

研究的目的:

  • 开发一个压缩域算法,用于对频率跳跃信号的独立参数估计.
  • 在非合作条件下提高中心频率和跳跃时间估计的准确性.
  • 为了避免用于高分辨率参数估计的信号重建.

主要方法:

  • 信号细分和收到信号的压缩采样.
  • 估计信号段的中心频率的最大点积.
  • 改进了原子字典,用于处理信号段和估计跳跃时间.
  • 在不依赖中心频率的情况下,独立估计跳跃时间.

主要成果:

  • 高分辨率的中心频率估计直接实现,没有信号重建.
  • 跳跃时间估计独立于中心频率估计过程.
  • 拟议的算法在数值模拟中显示出与现有方法相比更高的性能.
关键词:
压缩采样采样 压缩采样采样改进了原子字典的改进.最大的点点产品最大值.参数估计的参数估计.部分部分部分部分部分部分部分部分部分

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Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
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Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels

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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

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相关实验视频

Last Updated: Jul 27, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.0K
Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
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Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels

Published on: June 2, 2020

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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

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结论:

  • 开发的算法有效地独立估计频率跳跃信号参数.
  • 该方法在非合作情景的准确性和效率方面提供了显著的优势.
  • 这种方法推动了电子情报和信号分析领域的发展.