A 2D-CFAR Target Detection Method in Sea Clutter Based on Copula Theory Using Dual-Observation Channels
Year: 2025
Type: article
Abstract: The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily relies on the signal-to-clutter ratio (SCR) in a single observational channel, while feature space methods are overly sensitive to the number of pulse accumulations and rigidly apply outlier classifiers to define detection regions, without theoretical derivation. To address these limitations, this paper proposes a two-dimensional (2D) CFAR target detection method based on echo data from dual-polarization observational channels. First, statistical models of the amplitude distribution for horizontal–horizontal (HH) and vertical–vertical (VV) polarization sea clutter radar echoes are validated under identical observation conditions using measured data, and their correlations are analyzed. Then, the Copula function is introduced as a theoretical foundation to rigorously derive and extend the cell-averaging CFAR detector through strict mathematical formulations, transitioning from single statistics to 2D detection statistics. This leads to the proposed target detection method. Testing with measured data from publicly available datasets demonstrates that the proposed method effectively achieves adaptive false alarm control and significantly improves the detection performance compared to existing single-pulse one-dimensional CFAR detection methods.
Source: Remote Sensing
DOI: 10.3390/rs17233885
Authors: Xingyu Jiang, Jiyuan Tan, Yunlong Dong, Juan Li, Jian Guan, Guoqing Wang, Ningbo Liu
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
Awards: G5365930406, G5640477920
OpenAlex ID: W7108319600