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Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments.

Jeong Hoon Shin¹, Youngjin Choi²

¹1st Research and Development Institute, Agency for Defense Development, Daejeon 34060, Korea.

Sensors (Basel, Switzerland)

|July 17, 2020

Summary

This study introduces a robust control scheme to improve radar target detection in cluttered environments. The method adapts the constant false alarm rate (CFAR) process by estimating clutter density, enhancing performance in challenging conditions.

Keywords:

CFAR process clutter measurement density probability of target existence robust control

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Area of Science:

Radar Systems Engineering
Signal Processing
Statistical Detection Theory

Background:

Constant False Alarm Rate (CFAR) processes are crucial for radar target detection.
CFAR performance degrades significantly in unknown clutter environments due to unknown clutter characteristics.
Existing CFAR methods lack feedback for false alarm probability, limiting detection threshold adaptation.

Purpose of the Study:

To develop a robust control scheme for adjusting the CFAR detection threshold.
To estimate clutter measurement density (CMD) using finite time interval measurements.
To adapt to time-varying cluttered environments and improve target detection performance.

Main Methods:

A novel robust control scheme is proposed for CFAR threshold adjustment.
Clutter Measurement Density (CMD) is estimated using measurement sets over a finite time interval.
The probability of target existence for CMD estimation with finite measurement sets is derived.

Main Results:

The proposed method demonstrates improved performance in heterogeneous clutter scenarios.
The control scheme effectively adapts the detection threshold to varying clutter conditions.
Simulation experiments verified the enhanced detection capabilities.

Conclusions:

The developed robust control scheme enhances CFAR process performance in cluttered environments.
Estimating clutter measurement density enables adaptation to dynamic clutter.
This approach offers a significant improvement over traditional CFAR methods in complex scenarios.