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Cognitive Radar Waveform Optimization Based on Mutual Information and Kalman filtering.

Yu Yao1, Junhui Zhao1, Lenan Wu2

  • 1School of Information Engineering, East China Jiaotong University, Nanchang 330031, China.

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

This study introduces a novel cognitive radar waveform optimization strategy to improve target estimation. The method enhances target scattering coefficient estimation and detection probability, outperforming traditional algorithms.

Keywords:
Kalman filteringcognitive radarmutual information (MI)target scattering coefficients (TSC)waveform optimization

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Intelligent Transportation Systems

Background:

  • Cognitive radar systems require optimized waveforms for efficient target estimation under power constraints.
  • Accurate target scattering coefficient (TSC) estimation is crucial for radar performance in complex environments like intelligent transportation systems (ITS).
  • Minimizing mean-square error (MSE) and mutual information (MI) are key objectives in waveform design for enhanced radar capabilities.

Purpose of the Study:

  • To present a new strategy for optimizing cognitive radar waveforms under transmitted power constraints.
  • To enhance target estimation performance by minimizing the MSE of TSC estimates and MI between successive radar echoes.
  • To address challenges in target detection and parameter estimation within ITS applications.

Main Methods:

  • A two-step waveform optimization approach: 1) Optimal transmission waveform design by minimizing MSE of TSC estimates using Kalman filtering. 2) Waveform selection by minimizing MI between successive radar target echoes.
  • Utilizing Kalman filtering for improved target scattering coefficient estimation.
  • Iterative waveform optimization and selection process.

Main Results:

  • The proposed approach demonstrates improved TSC estimation accuracy compared to traditional waveform optimization algorithms with increasing iterations.
  • Simulation results show a significant enhancement in target detection probability using the novel algorithm.
  • The method effectively extracts target information features from different sensors in ITS scenarios.

Conclusions:

  • The presented cognitive radar waveform optimization strategy effectively enhances target estimation and detection performance.
  • The proposed method offers a superior alternative to traditional algorithms for radar applications, particularly in ITS.
  • Future work could explore adaptive waveform adjustments in dynamic environments.