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Product high-order ambiguity function for multicomponent polynomial-phase signal modeling

机译:用于多分量多项式相位信号建模的乘积高阶模糊函数

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Parameter estimation and performance analysis issues are studied for multicomponent polynomial-phase signals (PPSs) embedded in white Gaussian noise. Identifiability issues arising with existing approaches are described first when dealing with multicomponent PPS having the same highest order phase coefficients. This situation is encountered in applications such as synthetic aperture radar imaging or propagation of polynomial phase signals through channels affected by multipath and is thus worthy of a careful analysis. A new approach is proposed based on a transformation called product high-order ambiguity function (PHAF). The use of the PHAF offers a number of advantages with respect to the high-order ambiguity function (HAF). More specifically, it removes the identifiability problem and improves noise rejection capabilities. Performance analysis is carried out using the perturbation method and verified by simulation results.
机译:研究了嵌入高斯白噪声中的多分量多项式相位信号(PPS)的参数估计和性能分析问题。当处理具有相同最高阶相位系数的多分量PPS时,首先描述现有方法引起的可识别性问题。这种情况在诸如合成孔径雷达成像或多项式相位信号通过受多径影响的通道的传播等应用中会遇到,因此值得仔细分析。根据一种称为乘积高阶模糊函数(PHAF)的转换,提出了一种新方法。 PHAF的使用在高阶模糊函数(HAF)方面具有许多优势。更具体地说,它消除了可识别性问题并提高了噪声抑制能力。使用扰动方法进行性能分析,并通过仿真结果进行验证。

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