基于自适应LPFT的非平稳信号到达角估计

对非平稳阵列信号处理来说,通过时频分析预处理可以提高对不同信源到达角(direction of arrival, DOA)的分辨能力和估计精度。本文以提高在多信号分量环境下时频表示的能量聚集性为目标,提出一种自适应的局部多项式傅里叶变换(local polynomial Fourier transform, LPFT)方法,通过对信号瞬时频率曲线进行多项式拟合,确定LPFT的窗函数长度及各阶系数,以较小的计算量实现自适应时频分析。在此基础上,提出一种基于自适应LPFT的多信号分类(multiple signal classification,MUSIC)算法。仿真结果表明,与其他时频MUSIC算法相比,该方法对信号形式的适应能力强,在DOA估计精度、多信源角度分辨能力方面具有一定优势。

DOA estimation of non-stationary signals based on adaptive LPFT

For non-stationary array signal processing, time-frequency analysis will help to separate sources and improve the precision of direction of arrival (DOA) estimation. An adaptive local polynomial Fourier transform (LPFT) method is proposed to improve the energy concentration of time-frequency representation for multi-component signals. The window width and coefficients of LPFT are determined by performing polynomial curve fitting on the signals’ instantaneous frequency traces, thus the adaptive time-frequency analysis is realized with an affordable computaional burden. Furthermore, an adaptive LPFT based multiple signal classification (MUSIC) algorithm is proposed. Compared with other time-frequency MUSIC algorithms, the proposed algorithm is more adaptable to signal patterns and has considerable advantages in the aspects of DOA estiamtion pricesion and multiple sources separation.