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Expanded Swerling target models

机译：扩展的Swerling目标模型

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摘要

Radar target fluctuation models were first introduced by Swerling in the 1950s and they proved to be very useful. Swerling soon realized, however, that his original four models were inadequate and generalized them through use of the gamma distribution. These generalized results still had serious limitations in modeling fluctuating targets. For some targets the Swerling I correlated model produced an overly pessimistic value for PD at large signal-to-noise ratios (SNRs) while the Swerling III correlated model produced an overly optimistic one. As a result, other fluctuation models, such as the log-normal and Weibull, remain useful in spite of their own limitations. Two new models that expand on the generalized Swerling model are presented here. They are physically motivated and can produce the desired PD levels at high SNR values. They are described in detail and both the moment generating functions (MGFs) and PD expressions are determined. They provide a significant advance in our modeling capabilities through their flexibility for modeling many different types of target radar cross section (RCS) responses. These new models apply to both the case where the pulse-to-pulse target responses are correlated as well as the case where they are uncorrelated, thereby overcoming the limitations of the log-normal and Weibull models.
机译：雷达目标起伏模型是Swerling在1950年代首次提出的，事实证明它们非常有用。但是，Swerling很快意识到，他最初的四个模型是不足够的，并通过使用伽马分布对其进行了概括。这些广义结果在波动目标建模中仍然存在严重局限性。对于某些目标，Swerling I相关模型在较大的信噪比（SNR）下对PD产生了过于悲观的值，而Swerling III相关模型则产生了过于乐观的结果。结果，其他波动模型，例如对数正态和Weibull，尽管有其自身的局限性，但仍然有用。这里介绍了在广义Swerling模型上扩展的两个新模型。它们具有物理动机，可以在高SNR值下产生所需的PD电平。对它们进行了详细描述，并且确定了矩生成函数（MGF）和PD表达式。通过灵活地建模许多不同类型的目标雷达横截面（RCS）响应，它们在建模能力方面取得了重大进步。这些新模型既适用于脉冲对目标响应相关的情况，也适用于不相关的情况，从而克服了对数正态和Weibull模型的局限性。

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