2.2.7. Swerling模型

2.2.7. Swerling Models

利用目标RCS起伏和非相干积累的四种Swerling模型建立了广泛的雷达探测理论体系。

An extensive body ofradar detection theory has been built up using the four Swerling models oftarget RCS fluctuation and noncoherent integration (Swerling, 1960; Meyer andMayer, 1973; Nathanson, 1991; Skolnik, 2001).

它们由四种组合构成,其中两种是PDF,另两种是相关特性。

They are formed fromthe four combinations of two choices for the PDF and two for the correlationproperties.

使用的两种密度函数是指数函数和四自由度的chi-square函数(见表2.3)。

The two density functionsused are the exponential and the chi-square of degree 4 (see Table 2.3).

指数模型描述了由许多散射体组成的复杂目标的行为,但这些散射体都不会起主导作用。

The exponential modeldescribes the behavior of a complex target consisting of many scatterers, noneof which is dominant.

四自由度的chi-square模型则是由许多散射强度相似的散射体构成,但其中包括一个主要的散射体。

The fourth-degreechi-square model targets having many scatterers of similar strength with onedominant scatterer.

尽管Rice分布是上述情况下的精确PDF,但chi-square分布是匹配一阶矩和二阶矩的近似函数。

Although the Ricedistribution is the exact PDF for this case, the chi-square is an approximationbased on matching the first two moments of the two PDFs (Meyer and Mayer,1973).

当主散射体RCS是所有小散射体RCS之和的倍时,这些矩参数是匹配的,因此四自由度chi-square模型最适合这种情况。

These moments matchwhen the RCS of the dominant scatterer is  times that of the sumof the RCS of the small scatterers, so the fourth-degree chi-square model fitsbest for this case.

更一般的情况,自由度为2m = 1 + [a2/(1 + 2a)]的chi-square分布是Rice分布的最佳近似,其中a2为主散射体与所有小散射体之和的比值。

More generally, achi-square of degree 2m = 1 + [a2/(1+ 2a)] is a good approximationto a Rice distribution with a ratio of a2 of the dominant scattererto the sum of the small scatterers.

然而,只有四自由度chi-square分布这种特殊函数才被考虑为Swerling模型。

However, only thespecific case of the fourth-degree chi-square is considered a Swerling model.

Swerling模型又分为“Swerling 1”、“Swerling 2”等等。

The Swerling modelsare denoted as "Swerling 1," "Swerling 2," and so forth.

表2.5定义了4种情况。

Table 2.5 defines thefour cases.

有时候也将非起伏模型表示为"Swerling 0"或"Swerling 5"模型。

A nonfluctuatingtarget is sometimes identified as the "Swerling 0" or "Swerling5" model.

图2.17和2.18描述了两种Swerling模型的行为差异。

Figures 2.17 and 2.18illustrate the difference in the behavior of two of the Swerling models.

Figure 2.17. 三次扫描或CPI,每次扫描包含10个采样样本,样本功率为单位均值的Swerling 1模型Three scans or CPIs, each having10 samples of a unit mean Swerling 1 power sequence.

——本文译自Mark A. Richards所著的《Fundamentals of Radar Signal Processing(Second edition)》