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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...
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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.
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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.
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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 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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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.
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An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing.

Yingxi Liu1,2, Qun Zhang1,2,3, Zhidong Liu1,2

  • 1The Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive anti-jamming method for inverse synthetic aperture radar (ISAR) against interrupted sampling repeater jamming (ISRJ). By adjusting measurement numbers using compressed sensing (CS), it effectively suppresses jamming signals while preserving true target data.

Keywords:
anti-jammingcompressed sensing (CS)interrupted sampling repeater jamming (ISRJ)inverse synthetic aperture radar (ISAR)

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Electronic Warfare

Background:

  • Interrupted sampling repeater jamming (ISRJ) poses a significant challenge to inverse synthetic aperture radar (ISAR) systems.
  • ISRJ generates dense false targets, degrading the performance of ISAR imaging.
  • Existing anti-jamming methods often struggle to effectively mitigate ISRJ's impact.

Purpose of the Study:

  • To propose a novel adaptive anti-jamming method for ISAR systems.
  • To effectively suppress ISRJ while preserving the integrity of the true target signal.
  • To leverage compressed sensing (CS) principles for enhanced anti-jamming capabilities.

Main Methods:

  • Developed an adaptive anti-jamming strategy by adjusting the number of measurements based on compressed sensing (CS).
  • Exploited the frequency domain characteristics of ISRJ, noting its energy concentration and segmented sparsity.
  • Derived a two-dimensional (2D) anti-jamming method and analyzed its performance under varying signal-to-noise ratios (SNR) and jam-to-signal ratios (JSR).

Main Results:

  • The proposed method effectively suppresses ISRJ by exploiting differences in measurement numbers required for target and jamming signal reconstruction.
  • Signal reconstruction performance is optimized by adjusting measurement counts according to the restricted isometry property (RIP) condition.
  • Simulations demonstrated the significant effectiveness of the adaptive anti-jamming approach across various operational parameters.

Conclusions:

  • The adaptive anti-jamming method based on CS offers a robust solution against ISRJ in ISAR systems.
  • Adjusting the number of measurements is a viable strategy for discriminating and suppressing jamming signals.
  • The proposed technique enhances ISAR's resilience in complex electronic warfare environments.