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Hybrid FM-polynomial phase signal modeling: parameter estimation and Cramer-Rao bounds

机译:混合FM多项式相位信号建模:参数估计和Cramer-Rao边界

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Parameter estimation for a class of nonstationary signal models is addressed. The class contains combination of a polynomial-phase signal (PPS) and a frequency-modulated (FM) component of the sinusoidal or hyperbolic type. Such signals appear in radar and sonar applications involving moving targets with vibrating or rotating components. A novel approach is proposed that allows us to decouple estimation of the FM parameters from those of the PPS, relying on properties of the multilag high-order ambiguity function (ml-HAF). The accuracy achievable by any unbiased estimator of the hybrid FM-PPS parameters is investigated by means of the Cramer-Rao lower bounds (CRLBs). Both exact and large sample approximate expressions of the bounds are derived and compared with the performance of the proposed methods based on Monte Carlo simulations.
机译:解决了一类非平稳信号模型的参数估计问题。该类包含多项式相位信号(PPS)和正弦或双曲线类型的调频(FM)分量的组合。这种信号出现在雷达和声纳应用中,涉及带有振动或旋转分量的移动目标。提出了一种新颖的方法,该方法允许我们依赖于多延迟高阶模糊函数(ml-HAF)的属性将FM参数的估计与PPS的估计参数解耦。利用Cramer-Rao下界(CRLB)研究了混合FM-PPS参数的任何无偏估计器均可实现的精度。推导了边界的精确和大型样本近似表达式,并将它们与基于蒙特卡洛模拟的拟议方法的性能进行了比较。

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