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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array

Bing Ren¹, Tong Wang¹

¹National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

This study introduces a novel knowledge-aided space-time adaptive processing (STAP) method for airborne conformal arrays. The technique enhances clutter suppression by utilizing sparse learning and prior knowledge, improving target detection performance.

Keywords:

Laplace prior clutter suppression conformal array space-time adaptive processing sparse learning via iterative minimization

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Area of Science:

Radar Signal Processing
Array Signal Processing
Electromagnetics

Background:

Conventional space-time adaptive processing (STAP) struggles with airborne conformal arrays due to range-dependent clutter and non-uniform steering vectors.
Existing methods often require user parameter selection, limiting their practical application.

Purpose of the Study:

To propose a knowledge-aided STAP method for conformal arrays that improves clutter suppression.
To enhance the performance of slow-moving target detection in complex clutter environments.

Main Methods:

A novel knowledge-aided STAP approach is presented, combining sparse learning via iterative minimization (SLIM) with Laplace distribution.
The method constructs a dictionary matrix using prior knowledge of the range cell under test (CUT) within the clutter ridge.
It estimates sparse parameters and noise power to compute an accurate clutter plus noise covariance matrix (CNCM).

Main Results:

The proposed method achieves superior clutter suppression for conformal arrays.
It effectively addresses the limitations of conventional STAP in airborne platforms.
Simulation results validate the enhanced performance and effectiveness of the new approach.

Conclusions:

The developed knowledge-aided STAP method significantly improves clutter suppression in conformal array radar systems.
This technique offers a robust solution for slow-moving target detection, overcoming challenges posed by complex clutter.
The method's ability to avoid user parameter selection enhances its practical utility.