SMI算法在幅度异构杂波中的杂波归零性能分析

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本文研究了在幅度异构杂波环境下，样本矩阵求逆（Sample Matrix Inversion, SMI）算法的杂波归零性能。作者基于随机矩阵理论，深入探讨了平均信号干扰加噪声比（Signal-to-Interference plus Noise Ratio, SINR）在处理非均匀分布的次要样本（也被称作被测单元，CUT）时的变化规律。相比于次要样本的功率分布假设为独立且均匀分布（IID）的情况，研究发现，当使用拥有较强杂波功率的次级样本进行自适应权重设定时，SMI的输出SINR可能会有所提升。然而，这种性能增强是有限度的，因为过强的杂波会导致性能受限。相反，如果优先选择杂波功率较低的样本，性能反而会下降。文中引用了多个文献作为理论支撑，如： 1. Weiner (1991) 的工作探讨了二进制融合中波动目标的集成问题，这对于理解SMI在杂波环境中的行为提供了背景。 2. Frey Jr. (1996) 提出了对Swerling II目标的理想二进制集成阈值的近似方法，这与SMI算法中的阈值设置有相似之处。 3. Schleher (1976) 的雷达检测在Weibull杂乱背景下进行了研究，这对于理解不同类型的杂波环境下的性能分析至关重要。 4. David (1981) 的《顺序统计》一书提供了关于概率论中的基础知识，对于理解和处理随机样本的分布特性有很大帮助。 5. DeMiguel and Casar (1997) 针对Weibull和其他对数-对数线性尾分布的杂乱环境，提出了CFAR检测方法，这与SMI算法的优化策略有潜在关联。 Gradshteyn and Ryzhik (2000) 的积分表则提供了数值计算的工具，可能在推导复杂的统计表达式时起到关键作用。文章的核心内容是通过理论推导与实际案例分析，深入剖析了幅度异构杂波环境下SMI算法如何处理杂波并优化性能，这对于雷达信号处理、自适应检测系统以及抗杂波技术的理解具有重要意义。

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New York: Academic Press, 2000, pp. 331 and 899.

Clutter Nulling Performance of SMI in Amplitude

Heterogeneous Clutter En vironments

In order to investigate the clutter nulling performance of

the sample matrix inversion (SMI) algorithm in amplitude

heterogeneous environments, we derive an analytical expression

for the average signal-to-interference plus noise ratio (SINR) loss

based on random matrix theory. The results show that comparing

with the case where the training data utilizes the homogeneous

secondary samples (which are independent and identically

distributed (IID) to the snapshot in the cell under test (CUT)),

formulating the adaptive weight with the secondary samples

which have stronger clutter power than that in CUT will in crease

the output SINR. Nevertheless, the achievable performance

improvement might be quite limited. Conversely, selecting the

secondary samples with weaker clutter power will degrade the

performance.

I. INTRODUCTION

Space time adaptive processing (STAP) has been

recognized as a critical technique in airborne radar for

improving the detection performance of low-velocity

target [1, 2]. In STAP, a key problem is the estimation

of the interference covariance matrix and computation

of the adaptive weight (here interference means

clutter plus noise). Typically, the snapshots from

the neighborhood range bins, which are referred

to as secondary samples, are used to adaptively

formulate the interference covariance matrix. To

make a satisfactory estimation, it is required that the

secondary samples are homogenous, i.e., independent

and identically distributed (IID) to the snapshot in the

cell under test (CUT).

In the real world, however, such homogeneity

cannot always be achieved. Nonstationarity of the

external environment will lead to clutter heterogeneity,

i.e., the secondary samples do not share the same

statistics as those in CUT [3]. As a consequence

Manuscript received May 31, 2011; revised October 26, 2011 and

March 21, 2012; released for publication October 19, 2012.

IEEE Log No. T-AES/49/2/944544.

Refereeing of this contribution was handled by Y. Abramovich.

This work was supported in part by Anhui Provincial Natural

Science Foundation under Grant 1208085QF103, National Natural

Science Foundation of China under Grant 61171120 and 61201379,

the National High Technology Research and Development Program

(“863” Program) of China under Grant 2011AA8013019c, and the

Foundation of Tsinghua University under Grant 20101081772.

0018-9251/13/$26.00

° 2013 IEEE

1366 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 49, NO. 2 APRIL 2013

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