Swerling models were introduced by Peter Swerling and are used to describe the statistical properties of the radar cross-section of complex objects.

## Contents

* 1 General Target Model
* 2 Swerling Target Models
* 2.1 Swerling I
* 2.2 Swerling II
* 2.3 Swerling III
* 2.4 Swerling IV
* 2.5 Swerling V (Also known as Swerling 0)
* 3 References

## General Target Model

Swerling target models give the radar cross-section (RCS) of a given object using a distribution in the location-scale family of the chi-squared distribution.

[math]\displaystyle{ p(\sigma) = \frac{m}{\Gamma(m) \sigma_{av}} \left ( \frac{m\sigma}{\sigma_{av}} \right )^{m - 1} e^{-\frac{m\sigma}{\sigma_{av}}} I_{[0,\infty)}(\sigma) }[/math]

where [math]\displaystyle{ \sigma_{av} }[/math] refers to the mean value of [math]\displaystyle{ \sigma }[/math]. This is not always easy to determine, as certain objects may be viewed the most frequently from a limited range of angles. For instance, a sea-based radar system is most likely to view a ship from the side, the front, and the back, but never the top or the bottom. [math]\displaystyle{ m }[/math] is the degree of freedom divided by 2. The degree of freedom used in the chi-squared probability density function is a positive number related to the target model. Values of [math]\displaystyle{ m }[/math] between 0.3 and 2 have been found to closely approximate certain simple shapes, such as cylinders or cylinders with fins.

Since the ratio of the standard deviation to the mean value of the chi-squared distribution is equal to [math]\displaystyle{ m }[/math]−1/2, larger values of [math]\displaystyle{ m }[/math] will result in smaller fluctuations. If [math]\displaystyle{ m }[/math] equals infinity, the target's RCS is non-fluctuating.

## Swerling Target Models

Swerling target models are special cases of the Chi-Squared target models with specific degrees of freedom. There are five different Swerling models, numbered I through V:

### Swerling I

A model where the RCS varies according to a Chi-squared probability density function with two degrees of freedom ([math]\displaystyle{ m = 1 }[/math]). This applies to a target that is made up of many independent scatterers of roughly equal areas. As few as half a dozen scattering surfaces can produce this distribution. Swerling I describes a target whose radar cross-section is constant throughout a single scan, but varies independently from scan to scan. In this case, the pdf reduces to

[math]\displaystyle{ p(\sigma) = \frac{1}{\sigma_{av}} e^{-\frac{\sigma}{\sigma_{av}}} }[/math]

Swerling I has been shown to be a good approximation when determining the RCS of objects in aviation.

### Swerling II

Similar to Swerling I, except the RCS values returned are independent from pulse to pulse, instead of scan to scan.

### Swerling III

A model where the RCS varies according to a Chi-squared probability density function with four degrees of freedom ([math]\displaystyle{ m = 2 }[/math]). This PDF approximates an object with one large scattering surface with several other small scattering surfaces. The RCS is constant through a single scan just as in Swerling I. The pdf becomes

[math]\displaystyle{ p(\sigma) = \frac{4\sigma}{\sigma_{av}^2} e^{-\frac{2\sigma}{\sigma_{av}}} }[/math]

### Swerling IV

Similar to Swerling III, but the RCS varies from pulse to pulse rather than from scan to scan. Examples include some helicopters and propeller driven aircraft.

### Swerling V (Also known as Swerling 0)

Constant RCS, corresponding to infinite degrees of freedom ([math]\displaystyle{ m\to\infty }[/math]).

## References

* Skolnik, M. Introduction to Radar Systems: Third Edition. McGraw-Hill, New York, 2001.
* Swerling, P. Probability of Detection for Fluctuating Targets. ASTIA Document Number AD 80638. March 17, 1954.

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