Al-Dabaa Mansoor M, Laslo Eugen, Emran Ahmed A, Yahya Ahmed, Aboshosha Ashraf
Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt.
Department of Mathematics and Computer Science, Faculty of Science, University of Oradea, 410087 Oradea, Romania.
Sensors (Basel). 2025 Apr 20;25(8):2613. doi: 10.3390/s25082613.
Maintaining a Constant False Alarm Rate (CFAR) in the presence of K-distributed sea clutter is vital due to the dynamic and unpredictable nature of maritime environments. However, conventional CFAR detectors suffer significant performance degradation in multi-target scenarios, primarily due to the masking effect caused by interfering targets. To address this challenge, this paper introduces an advanced detection scheme that integrates Linear Density-Based Spatial Clustering for Applications with Noise (Lin-DBSCAN) with CFAR processing. Lin-DBSCAN is specifically tailored to efficiently identify and isolate interfering targets and sea spikes, which typically manifest as outliers in the symmetric reference windows surrounding the Cell Under Test (CUT). By leveraging Lin-DBSCAN, the proposed Lin-DBSCAN-CFAR method effectively filters out anomalous signals from the background clutter, resulting in enhanced detection accuracy and robustness, especially under complex sea clutter conditions. Extensive simulations under varying conditions, including multiple target environments, varying false alarm rates, and different clutter shape parameters, demonstrate that Lin-DBSCAN-CFAR significantly outperforms conventional CFAR approaches. It is noteworthy that the proposed method achieves detection performance comparable to the more computationally intensive DBSCAN-CFAR while significantly reducing computational complexity. Simulation results reveal that Lin-DBSCAN-CFAR requires a 1 to 2 dB lower SNR to reach a detection probability of 0.8 compared with the nearest traditional CFAR techniques, confirming its superiority in both accuracy and efficiency.
由于海洋环境具有动态性和不可预测性，在存在K分布海杂波的情况下保持恒定虚警率（CFAR）至关重要。然而，传统的CFAR检测器在多目标场景中性能会显著下降，主要是由于干扰目标引起的遮蔽效应。为应对这一挑战，本文介绍了一种先进的检测方案，该方案将基于线性密度的空间聚类应用于噪声（Lin-DBSCAN）与CFAR处理相结合。Lin-DBSCAN经过专门设计，能够有效地识别和隔离干扰目标以及海尖峰，这些通常在被测单元（CUT）周围的对称参考窗口中表现为异常值。通过利用Lin-DBSCAN，所提出的Lin-DBSCAN-CFAR方法有效地从背景杂波中滤除异常信号，从而提高了检测精度和鲁棒性，特别是在复杂海杂波条件下。在包括多目标环境、不同虚警率和不同杂波形状参数等各种条件下进行的大量仿真表明，Lin-DBSCAN-CFAR明显优于传统的CFAR方法。值得注意的是，所提出的方法在显著降低计算复杂度的同时，实现了与计算量更大的DBSCAN-CFAR相当的检测性能。仿真结果表明，与最接近的传统CFAR技术相比，Lin-DBSCAN-CFAR在达到0.8的检测概率时所需的信噪比低1至2 dB，证实了其在准确性和效率方面的优越性。
