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Extended target recognition in cognitive radar networks.

Yimin Wei¹, Huadong Meng, Yimin Liu

¹Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. weiym@mails.tsinghua.edu.cn

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This study introduces adaptive waveform design for cognitive radar networks to improve extended target recognition. A novel minimum correlation algorithm enhances target detection accuracy in fluctuating environments.

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Area of Science:

Electrical Engineering
Signal Processing
Radar Systems

Background:

Cognitive radar networks offer enhanced situational awareness and adaptability.
Extended target recognition is crucial for identifying complex objects in dynamic environments.
Traditional radar systems face challenges in adaptive waveform design for target recognition.

Purpose of the Study:

To develop an adaptive waveform design strategy for extended target recognition in cognitive radar networks.
To enhance the performance of active target recognition radar systems within a centralized network structure.
To address amplitude fluctuation challenges in target recognition scenarios.

Main Methods:

A generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework was employed.

Keywords:

cognitive radar network radar waveform design target recognition

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Doppler velocities from multiple radars were used to calculate target aspect angles.

A minimum correlation algorithm was proposed for adaptive transmit waveform design.

Joint probabilities of target hypotheses were updated using multi-line-of-sight (LOS) observations.

Main Results:

The cognitive radar network and adaptive waveform design demonstrated significant performance improvements.
The proposed minimum correlation algorithm outperformed eigen-waveform and non-cognitive approaches.
Accurate target aspect angle calculation and probability updates were achieved.
Effective waveform adaptation was shown under amplitude fluctuation conditions.

Conclusions:

The developed adaptive waveform design significantly enhances extended target recognition in cognitive radar networks.
The minimum correlation algorithm provides a superior approach compared to existing methods.
Centralized cognitive radar networks with adaptive waveforms offer a promising direction for advanced radar applications.