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相关概念视频

Upsampling01:22

Upsampling

568
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...
568
Aliasing01:18

Aliasing

523
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...
523
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

661
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...
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Sampling Theorem01:15

Sampling Theorem

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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.
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Bandpass Sampling01:17

Bandpass Sampling

457
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
457
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

329
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....
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相关实验视频

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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基于SAR的HFPFM编码模型的高分辨率低侧叶波形设计.

Yu Gao1, Guodong Jin1,2, Xifeng Zhang1

  • 1Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211116, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
概括

本研究介绍了一种优化的高自由度参数化频率调制 (HFPFM) 波形,用于合成光圈雷达 (SAR) 系统. 新的波形显著减少侧叶,改善目标成像,而不牺牲分辨率或信号噪声比.

关键词:
在HFPFM编码模型中,在SAR成像中使用SAR成像.梯度下降的降落方式高分辨率的高分辨率显示器低侧面的洛贝尔波形优化设计波形优化设计

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科学领域:

  • 雷达系统工程 雷达系统工程
  • 信号处理 信号处理
  • 电磁学 电磁学 电磁学 电磁学

背景情况:

  • 合成光圈雷达 (SAR) 传统上使用线性频率调制 (LFM) 波形.
  • 使用LFM的窗口功能会降低信号噪声比 (SNR) 和用于侧叶抑制的分辨率.
  • 非线性频率调制 (NLFM) 波形提供侧叶抑制,没有SNR损失,但仍然可能导致分辨率损失.

研究的目的:

  • 为SAR系统开发先进的波形设计,克服LFM和NLFM的局限性.
  • 为了优化雷达波形,增强侧叶抑制,而不影响主叶宽度,分辨率或SNR.
  • 为波形优化引入一种新的高自由度参数化频率调制 (HFPFM) 编码模型.

主要方法:

  • 基于HFPFM编码模型构建了一个波形侧叶优化模型.
  • 在优化过程中应用约束来防止主叶扩大.
  • 使用梯度下降方法解决了优化模型.
  • 利用矩阵乘法和快速里叶变换 (FFT) /反向快速里叶变换 (IFFT) 进行高效的参数优化.

主要成果:

  • 与LFM波形相比,优化的HFPFM波形实现了超过9dB的侧叶减小.
  • 优化成功避免了大叶扩大,保持了分辨率.
  • 该方法同时减轻了与窗口功能权重相关的分辨率和SNR损失.
  • SAR点目标成像模拟表明,它能够清晰地图像强目标附近的弱目标.

结论:

  • 提出的HFPFM波形优化方法有效地提高了SAR成像性能.
  • 这种方法通过改善侧叶抑制并保持分辨率和SNR,为传统的LFM和NLFM波形提供了更好的替代方案.
  • 优化的波形使复杂的目标能够更清晰地成像,证明了该方法的实际有效性.