短序列条件下基于分段多项式建模方法的相位估计性能分析
Phase Estimation Accuracy Based on Piecewise Polynomial-Phase Modeling Method with Short Sequences
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摘要: 该文主要对短序列非多项式相位条件下基于高阶模糊函数(HAF)的多项式相位系数估计算法性能进行了较详细的讨论。进一步研究了基于这种算法思想的分段多项式相位建模的瞬时相位估计方法。该方法的思想主要体现为将需估计数据序列进行分段，每个短数据段的瞬时相位采用一个低阶的多项式来逼近，而这些逼近多项式的各阶系数利用HAF或乘积高阶模糊函数(PHAF)的方法进行估计，最终整个数据序列的相位由各段估计出的瞬时相位合并而成。该方法的估计性能很大程度上取决于各分段数据序列的估计精度。文中分析了短序列非多项式相位对HAF及PHAF的影响，并通过仿真实验给出了具有一般性的结论。Abstract: In this paper, the performance of polynomial phase coefficient estimation algorithm based on High-order iguity Function (HAF) for non-polynomial phase signal with short sequences is discussed in detail. Further, ntaneous phase estimation method is developed on the basis of the idea of this algorithm. The main idea of the ;ssed algorithm is to divide the data sequence into several segments, approach the instantaneous phase of each short Lent by a low-order polynomial, estimate the parameters of the modeling polynomial-phase signal by HAF and Product methods, and finally integrate the whole phase with estimated instantaneous phase of each segment. The estimation mnance depends comparatively on the achievable accuracy of the segmented phase. The disadvantage of /PHAF-based polynomial-phase estimation method with short and non-polynomial phase sequences is analyzed in this r and some general conclusions are drawn after simulations.
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