Abstract

Radars are the cornerstones of modern integrated air defense systems. The decision on the present or absence of object, ensuring non-fluctuating false alarms, represents one of its fundamental concepts. This involves the implementation of constant false alarm rate (CFAR) strategy that updates the detection threshold in accordance with the inhomogeneous observation scenario. The hardness of finding a single CFAR variant, to deal with diverse noise situations, necessitates the development of composite technique. In this regard, fusion of particular decisions of single CFAR schemes provides better final detection through appropriate fusion rules. This paper is intended with the analysis of linear fusion (LF) of CA, OS, and TM structures. The target of interest and the fallacious ones are supposed to follow χ²-model with two-degrees of freedom in their fluctuation. The closed-form expression is derived for the detection performance. Our simulation results demonstrate that the LF model exhibits robust behavior on the presence or absence of interferers. Additionally, the LF ideal performance surpasses the Neyman–Pearson (N-P) detector one, which is the yardstick of the CFAR world. Moreover, the LF strategy has the capability to hold the unchanged false alarm rate in face of the presence of interferers.

中文翻译：

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部分相关χ2-目标自适应检测新方法的异构性能评估

摘要

雷达是现代综合防空系统的基石。确定物体是否存在，确保不波动的误报，是其基本概念之一。这涉及实施恒定误报率 (CFAR) 策略，该策略根据不均匀的观察场景更新检测阈值。很难找到单一的 CFAR 变体来处理各种噪声情况，因此需要开发复合技术。在这方面，单个 CFAR 方案的特定决策的融合通过适当的融合规则提供了更好的最终检测。本文旨在分析 CA、OS 和 TM 结构的线性融合 (LF)。感兴趣的目标和错误的目标应该遵循 χ ²-具有两个波动自由度的模型。为检测性能推导出封闭式表达式。我们的模拟结果表明，LF 模型在存在或不存在干扰时表现出稳健的行为。此外，LF 理想性能超过了作为 CFAR 世界标准的 Neyman-Pearson (NP) 检测器之一。此外，LF 策略能够在存在干扰时保持不变的误报率。