摘要
乘积高阶模糊函数(PHAF)是以分析多分量多项式相位信号(mc-PPS)而提出来的,但实际上它抑制交叉项的能力有限,仍然难以实现mc-PPS估计。逐次滤波方法是抑制交叉项的有力工具,但存在着分量间的误差扩散;松弛法(RELAX)采用循环迭代方式,对串行估计中的误差扩散有着较强的抑制能力,将二者结合起来提出来了迭代松弛PHAF方法。通过分析被估计信号参数变化时的性能表明改进后的PHAF具有较好的鲁棒性:减少了估计盲区,具有更好的估计精度,具有较低的信噪比(SNR)门限。这些性能由mc-PPS仿真例子所验证。
Though originally proposed for analyzing multi-component polynomial phase signa（lmc-PPS）,the Product High-order Ambiguity Function（PHAF） can not be used to estimate mc-PPS directly for its validity is degraded by the interference terms of the mc-PPS.Iterative filtering is an efficient method to suppress interference terms but is troubled by error propagation between components.The Relaxation method（RELAX） is capable of controlling error propagation in sequential estimation by recursive iteration.In this paper,a new method called iterative-RELAX PHAF is proposed by combining iterative filtering and RELAX.Analyzing the performance of the estimated signal variable parameter shows that the improved version of PHAF enjoys better robustness,provides better precision,reduces the blind area and has low threshold of Signal to Noise Ratio（SNR）.The performance of the new method is verified by simulations with mc-PPS.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第4期124-127,153,共5页
Computer Engineering and Applications
关键词
乘积高阶模糊函数
交叉项
多分量多项式相位信号
参数估计
product high-order ambiguity function
interference term
multi-component polynomial phase signal
parameter estimation
