A minimum description length CFAR algorithm based on sparse regularization

Jinghao Yu; Renhong Xie; Peng Li; Yu Yang; Yuqing Feng

doi:10.1117/12.3022700

28 March 2024 A minimum description length CFAR algorithm based on sparse regularization

Jinghao Yu, Renhong Xie, Peng Li, Yu Yang, Yuqing Feng

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910V (2024) https://doi.org/10.1117/12.3022700
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Ground surveillance radar has the advantages of far-distance detecting, full-weather, and working condition not affected by day and night. However, the existence of natural and man-made interference, complex landforms and other factors seriously affect the target detection performance. As a key technology in radar signal processing system, constant false alarm detection technology directly determines the performance of radar detection, so it is of great value to study the ground radar constant false alarm detection algorithm. To solve the problem of performance degradation of minimum description length constant false alarm algorithm (MDL) in multi-target environment detection, the minimum description length constant false alarm algorithm based on sparse regularization is proposed. The non-convex regularization term is used to regularize the outliers, the indicator function is introduced to improve the maximum likelihood estimation process, and the robustness of outlier vector determination is improved based on the median idea. The algorithm solves the problem that the detection threshold is greatly raised, suppresses the “target masking effect”, and improves the detection performance in multi-target environment. Furthermore, the minimum description length variability index constant false alarm algorithm based on sparse regularization is proposed. Based on the improved mean ratio and the improved variability index statistic, combined with the minimum description length idea, the two-level edge detection and the first-level homogeneity judgment are used to evaluate the clutter environment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinghao Yu, Renhong Xie, Peng Li, Yu Yang, and Yuqing Feng "A minimum description length CFAR algorithm based on sparse regularization", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910V (28 March 2024); https://doi.org/10.1117/12.3022700

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Clutter

Detection and tracking algorithms

Windows

Environmental sensing

Target detection

Edge detection

Statistical analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years