Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive...

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Optimization Theory

Background:

• Cognitive radar systems require advanced waveform design for effective target estimation.
• Signal-dependent clutter and peak-to-average power ratio (PAR) constraints complicate traditional waveform optimization.

Purpose of the Study:

• To develop an efficient waveform design method for cognitive radar targeting extended targets.
• To address the non-convex optimization problem arising from PAR constraints and signal-dependent clutter.

Main Methods:

• A minimization-maximization (MM) technique is employed to transform the non-convex problem into a convex one.
• Matrix variables are converted to vector variables using Toeplitz matrix properties for efficient solution.
• A nearest neighbor method and an acceleration scheme are utilized to solve and speed up convergence.

Main Results:

• The proposed MM-based method successfully converts a complex non-convex problem into a solvable convex form.
• The method demonstrates superior estimation performance compared to existing techniques in simulations.
• The acceleration scheme significantly enhances the convergence speed of the waveform design process.

Conclusions:

• The developed waveform design method offers a robust solution for cognitive radar under challenging conditions.
• The approach provides enhanced estimation accuracy for extended targets in the presence of signal-dependent clutter.
• This work contributes to the advancement of intelligent radar systems through efficient waveform optimization.