基于谱分解的降阶求根MUSIC算法

求根多重信号分类(Root-MUSIC)算法以多项式求根代替谱峰搜索,降低了波达方向(DOA)估计的计算量,但当阵元数较大时,其计算量依然很大。为进一步降低计算量,该文提出一种降阶Root-MUSIC(RD-Root-MUSIC)算法。该算法基于谱分解将Root-MUSIC多项式的阶次降低一半,再根据矩阵特征多项式与求根多项式的关系构造友阵,采用Arnoldi迭代计算得到友阵的L个大特征值(L为信号数)并估计DOA。仿真结果表明,RD-Root-MUSIC估计精度与Root-MUSIC相近,但其在大阵元下具有比Root-MUSIC更低的计算量。     

一种高精度低复杂度波达方向估计新方法

提出了一种应用于移动自主网中节点定位的波达方向(Direction of Arrival,DOA)估计新方法,以满足移动自主网对节点定位机制的高精度和低复杂度的要求。利用牛顿法将延迟相加法和求根多重信号分类算法(Root-MUSIC)进行联合估计,定位过程分为2个阶段:采用延迟相加法进行实时粗略DOA估计;采用Root-MUSIC进行精准定位。将该方法实现于软件无线电(SDR)系统,综合了延迟相加法低复杂度和Root-MUSIC高估计精度的优点。Matlab仿真实验结果证明了该方法以较低的复杂度获得近似于单独采用Root-MUSIC所达到的高精度,解决了现有方法难以同时达到高精度和低复杂度的问题。     

Removing the outliers in root-MUSIC via pseudo-noise resampling and conventional beamformer

A joint use of pseudo-noise resampling technique and a conventional beamformer is proposed to mitigate the effect of outliers in Root-MUSIC estimator. After resampling of Root-MUSIC via pseudo-randomly generated noise we combine it with a conventional beamformer (in the sense that identification of the signal roots is based on the power of the conventional beamformer response). The resulting estimator can be referred to as modified resampled Root-MUSIC. The estimator bank is formed from a number of modified resampled Root-MUSIC. Censored selection of the results of modified pseudo-randomly resampled Root-MUSIC estimators is exploited based on an appropriate local performance test.     

求根MUSIC初值设置和更新算法

作为波达方向(direction-of-arrival,DOA)估计的重要方法之一,求根MUSIC(root-MUSIC)算法在实际工程中常需选择一定初值、进而借助迭代对多项式求根。为了提高求根的计算效率和正确性,必须研究适合root-MUSIC的初值设置和更新算法.致力于此,本文根据root-MUSIC多项式根的分布特点,提出了以迭代初值到单位圆平均距离最短(least average distance to unit circle,LADTUC)为准则的root-MUSIC迭代初值设置和更新算法.理论分析和实验结果表明,该算法能有效避免错误解和加速迭代收敛速度,从而为root-MUSIC算法的实际工程化提供理论参考.     

Polynomial coefficient finding for Root-MUSIC

Root-MUSIC (MUltiple SIgnal Classification) is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements. By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger ex...     

基于Root—MUSIC和Prony算法的电压闪变检测

电压闪变是电能质量的一个重要指标．为实现对电压闪变参数的准确测量,提出基于求根多重信号分类法和Prony算法的闪变参数检测方法．先对信号数据组成的矩阵进行特征值分解,得出信号子空间和噪声子空间,利用他们的相互正交关系通过单位圆上的根估计频率大小,同时利用Prony算法中的最小二乘法求得幅值信息．Matlab仿真结果表明该方法在低信噪比下利用Root—MUSIC法能准确检测频率,并结合最小二乘法在小样本数据上的优势,有效提高了幅值检测的准确度．     

基于求根多重信号分类和遗传算法的谐波间谐波频谱估计

为了改善电能质量,需要准确估计电网电压或电流的谐波、间谐波频谱.将空域处理技术的空间谱估计方法应用到电网信号的谐波、间谐波频谱估计.使用求根多重信号分类方法估计电压或电流的不同频率成分数及各成分的频率,各成分的幅值和相角估计由遗传算法完成.首先介绍频谱估计使用空间谱估计的理论依据,然后详尽地说明了求根多重信号分类方法和遗传算法的原理及算法流程.仿真结果和与其他方法的比较证明此方法在数据长度有限和噪声干扰时可以准确估计谐波、间谐波的频谱.     

基于四元数的Root-MUSIC的双基地MIMO雷达中角度估计算法

该文将四元数理论应用到双基地集中式多输入多输出(MIMO)雷达的角度估计中。文中通过传统数据模型构造四元数矩阵,提出了基于四元数的求根-多重信号分类(Root MUltiple SIgnal Classification, Root-MUSIC)的MIMO雷达中角度估计算法,该算法通过奇异值分解和Root-MUSIC来估计出发射角(Direction Of Departure, DOD)和接收角(Direction Of Arrival, DOA)。该算法的角度估计性能远优于现有文献的方法,并且无需谱峰搜索,复杂度大大降低。仿真结果验证了算法的有效性。     

基于Root-MUSIC和Adaline神经网络的 间谐波参数估计

为了提高电力系统间谐波分析的精度和分辨率,提出基于求根多重信号分类法（root-MUSIC)和自适应线性神经网络的间谐波参数估计方法.该算法利用求根多重信号分类法估计信号中谐波和间谐波的个数及频率,将谐波和间谐波的频率作为Adaline神经网络的输入进行学习,用得到的权值确定谐波和间谐波的幅值和相位;将频率作为权值在改进的Adaline神经网络中参与学习,估计谐波和间谐波的频率、幅值和相位.Matlab仿真结果表明,该算法频率分辨率高、检测准确、收敛快;当频率估计准确时,基本Adaline神经网络与改进的Adaline神经网络具有相近的检测精度,且前者的实时性更好.     

Two-step reliability test based unitary root-MUSIC for direction-of-arrival estimation

A two-step reliability test (TSRT) based unitary root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper. We combine the conventional beamforming and unitary root-MUSIC to compute the DOA estimates and employ the pseudo-noise resampling (PR) technique to construct a DOA estimator bank. Unlike the standard reliability test, we devise the TSRT which retains the successful DOA estimates of a given DOA estimator separately to construct a DOA estimate set that is used to determine the final DOA estimates. Compared to the existing PR based DOA estimation methods, our solution can achieve better threshold performance by using fewer PR runs. Furthermore, the TSRT can be easily applied to other DOA estimation methods. Simulations verify the effectiveness of the proposed scheme.