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Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
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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.
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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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Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
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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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Related Experiment Video

Updated: Jan 13, 2026

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
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Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

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Novel Amplitude-Based Approach for Reducing Sidelobes in Persistent Scatterer Interferometry Processing Using

Natascha Liedel1, Jonas Ziemer1, Jannik Jänichen1

  • 1Department for Earth Observation, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

This study presents an amplitude-based Spatially Variant Apodization (SVA) filter to reduce Synthetic Aperture Radar (SAR) sidelobes. The method preserves interferometric phase, enhancing Persistent Scatterer Interferometry (PSI) for accurate dam deformation monitoring.

Keywords:
PSIS-1SARSNAP2StaMPSSVAStaMPSactive reflectoramplitude-based filteringdam monitoringsidelobes

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Area of Science:

  • Remote Sensing
  • Geophysics

Background:

  • Synthetic Aperture Radar (SAR) data often suffers from sidelobe artifacts, which can degrade the accuracy of deformation monitoring.
  • Persistent Scatterer Interferometry (PSI) is a valuable technique for measuring ground deformation, but it is sensitive to amplitude variations and phase distortions.

Purpose of the Study:

  • To introduce and evaluate an amplitude-based Spatially Variant Apodization (SVA) method for reducing sidelobes in SAR data.
  • To integrate the SVA filter into the Sentinel Application Platform to Stanford Method for Persistent Scatterers (SNAP2StaMPS) workflow for dam monitoring.
  • To assess the impact of SVA filtering on Persistent Scatterer (PS) detection and deformation measurement accuracy.

Main Methods:

  • An amplitude-based SVA filter was developed and applied to the In-phase (I) and Quadrature (Q) components of coregistered SAR data.
  • The SVA filter was implemented using SNAP-Python (snappy) within the SNAP2StaMPS workflow.
  • The method was tested on Sentinel-1 data from the Sorpe Dam, Germany, focusing on sidelobe reduction and phase preservation.

Main Results:

  • SVA filtering successfully reduced sidelobe artifacts in SAR data, leading to a 39.26% decrease in sidelobe-affected Persistent Scatterers (PS).
  • The amplitude-based approach preserved the original interferometric phase, ensuring accurate deformation values with a Root Mean Square Error (RMSE) of approximately 0.38 mm.
  • The integration of SVA filtering into the SNAP2StaMPS workflow improved PS detection and dam deformation monitoring capabilities.

Conclusions:

  • The novel amplitude-based SVA method effectively reduces sidelobes in SAR data while preserving crucial phase information.
  • This approach enhances the accuracy and reliability of PSI for dam deformation monitoring, particularly in the presence of strong sidelobes.
  • The developed SVA filter extends the SNAP2StaMPS workflow, offering a valuable tool for geoscientific applications requiring precise SAR interferometry.