2.2.7. Swerling模型

2.2.7. Swerling Models


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).


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


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


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


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


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).


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.


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.


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.


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)》