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An improved approach for higher-order cyclostationarity based direction-finding of coherent sources using forward-backward linear prediction

机译:一种基于前向后线性预测的高阶基于循环平稳性的相干源测向方法

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We propose a modification of the previous higher-order cyclic statistics (HOCS)-based linear prediction (LP) approach by performing both the forward and backward LP with subarrays and defining both the form of forward and backward fourth-order cyclic cumulants, thus incorporate subarrays with forward-backward LP model and develop a new HOCS-based forward-backward LP direction-finding approach for coherent sources, we term it HOCS-FBLP. It is proved that the proposed method can decorrelate the coherency with fewer sensors. The simulations show that the improved approach has the advantages of larger array aperture and higher resolution under small length of data, as well as its powerful capability of interference suppression and signal selectivity duo to the use of HOCS.
机译:我们通过使用子阵列执行前向和后向LP和定义前向和向后四阶循环累积剂的形式,提出了基于前一循环统计(HOCS)的线性预测(LP)方法的修改,从而包括前向和向后的四阶循环累积物的形式具有前后LP模型的子阵列,并开发了一种基于Hocs的基于Hocs的前后LP方向查找方法,用于相干来源,我们术语IT Hocs-FBLP。事实证明,该方法可以用较少的传感器去相关。模拟表明,改进的方法具有较大的阵列孔径和更高的数据的优点,并且在小型数据的小程度下,其干扰抑制的强大能力和信号选择性DUO与HOCS的使用。

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