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Aliasing01:18

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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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Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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Modified Blind Equalization Algorithm Based on Cyclostationarity for Contaminated Reference Signal in Airborne PBR.

Shuai Guo1, Jun Wang1, Hui Ma1

  • 1National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|February 7, 2020
PubMed
Summary

Airborne passive bistatic radar (PBR) faces reference signal contamination from multipath signals with time delay and Doppler frequency. A modified blind equalization algorithm effectively suppresses multipath and restores a pure reference signal for improved target detection.

Keywords:
airborne passive bistatic radarcomplex value BP neural networkcyclostationaritymodified blind equalization algorithmmultipath signal

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Electromagnetics

Background:

  • Airborne passive bistatic radar (PBR) systems rely on a reference channel for direct-path signals.
  • Multipath interference in the reference signal, unique to airborne PBR due to Doppler frequency, degrades target detection performance.
  • Existing blind equalization algorithms are insufficient for airborne PBR's contaminated reference signals.

Purpose of the Study:

  • To propose a modified blind equalization algorithm for airborne PBR.
  • To suppress multipath signals and restore a pure reference signal.
  • To enhance the performance of target detection in airborne PBR systems.

Main Methods:

  • Exploiting high-order moment information and signal cyclostationarity to create a novel cost function for phase constraint.
  • Utilizing a complex-valued backpropagation (BP) neural network to solve the constrained optimization problem.
  • Developing a modified blind equalization algorithm tailored for airborne PBR.

Main Results:

  • The proposed algorithm effectively suppresses multipath signals in the reference channel.
  • Restoration of a pure reference signal is achieved, mitigating spatial-temporal clutter spectrum expansion.
  • Simulation experiments demonstrate the algorithm's feasibility and superiority over existing methods.

Conclusions:

  • The modified blind equalization algorithm significantly improves target detection performance in airborne PBR.
  • The approach successfully addresses the challenge of multipath interference with Doppler frequency.
  • This work offers a robust solution for enhancing airborne PBR system reliability.