The ordered statistics (OS) scheme is an effective constant false alarm rate (CFAR) technique deployed in many radar systems. It is widely deployed because of its simplicity and effectiveness under conditions of both homogeneous and non-homogeneous radar returns. However, the problem of inaccurate censoring typically degrades its performance since it is often difficult to accurately determine the actual number of interfering targets and clutter edges in the reference window per time. In this article, we address this problem based on the principle of discriminant analysis (DA) towards automatically and effectively estimating the $k$^th rank ordered element of the OS scheme. Our scheme, termed the DA-OS scheme, works without requiring a priori knowledge about the statistical characteristics of the input radar returns. The results obtained via Monte Carlo simulation indicate that the DA-OS scheme achieves a small CFAR loss of about 0.392 dB relative to the cell averaging (CA) scheme under conditions of homogeneous radar returns at a probability of detection of 0.5. It outperforms other notable traditional schemes, including the CA, smallest-of CA, greatest-of CA, and the fixed OS schemes under conditions of non-homogeneous radar returns. Finally, it provides a number of desirable qualitative characteristics as against other existing censoring techniques.

有序统计（OS）方案是部署在许多雷达系统中的有效恒定误报率（CFAR）技术。由于其在同质和非同质雷达回波条件下的简单性和有效性，它得到了广泛的部署。但是，检查不准确的问题通常会降低其性能，因为通常很难准确确定每次参考窗口中干扰目标和杂波边缘的实际数量。在本文中，我们将基于判别分析（DA）的原理来解决此问题，以自动有效地估算出$ķ$^个等级排序的OS计划的元素。我们的计划被称为DA-OS计划，其运作无需先验知识即可了解输入雷达回波的统计特性。通过蒙特卡洛模拟获得的结果表明，相对于小区平均（CA）方案，DA-OS方案在检测到雷达概率为0.5的情况下相对于小区平均（CA）方案实现了约0.392 dB的小CFAR损耗。它优于其他著名的传统方案，包括CA，最小CA，最大CA和在非均匀雷达回波条件下的固定OS方案。最后，与其他现有的检查技术相比，它提供了许多理想的定性特征。