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Related Experiment Video

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Adaptive Waveform Design for Cognitive Radar in Multiple Targets Situation.

Xiaowen Zhang1, Xingzhao Liu1

  • 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200210, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

Cognitive radar (CR) waveform optimization improves target detection and estimation for multiple extended targets, even with unknown characteristics and interference. New algorithms enhance performance by balancing detection probability and estimation precision.

Keywords:
cognitive radarmultiple targetstarget detectiontarget estimationwaveform optimization

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Information Theory

Background:

  • Cognitive radar (CR) waveform optimization is crucial for target detection and estimation.
  • Existing methods often focus on single targets and lack robustness in complex environments.
  • Extended targets with unknown target impulse response (TIR) present significant challenges.

Purpose of the Study:

  • To investigate cognitive radar waveform optimization for detecting and estimating multiple extended targets.
  • To develop an improved algorithm addressing signal-dependent interference and unknown TIR.
  • To compare performance against traditional methods like linear frequency modulated (LFM) signals.

Main Methods:

  • An improved algorithm maximizing detection probability while ensuring TIR estimation precision.
  • Introduction of a weight vector for balancing trade-offs among multiple targets.
  • Iterative updates for TIR estimation and waveform design.
  • Consideration of an information-theoretical approach under waveform energy and bandwidth constraints.

Main Results:

  • The proposed algorithm enhances target detection and TIR estimation precision for multiple extended targets.
  • Waveforms designed using maximum detection probability and maximum mutual information (MI) criteria outperform LFM signals.
  • The developed method effectively handles signal-dependent interference and unknown target characteristics.
  • Simulation results validate the improved radar performance and information content in echoes.

Conclusions:

  • The study presents a novel approach to cognitive radar waveform design for complex multi-target scenarios.
  • The proposed methods offer significant improvements over traditional techniques.
  • This research contributes to advancing radar capabilities in challenging operational environments.