Multipath Time Delay Estimation Based on MUSIC Algorithm Under Small Sample Conditions

On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.

A Modified Root-MUSIC Algorithm for Signal DOA Estimation

This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail. It is also pointed out theoretically that this is equivalentto have increased the snapshot number and can make the DOA estimation better. Finally, somesimulating results to verify the theoretical analyses are presented.

A Time-Frequency Associated MUSIC Algorithm Research on Human Target Detection by Through-Wall Radar

In this paper,a time-frequency associated multiple signal classification(MUSIC)al-gorithm which is suitable for through-wall detection is proposed.The technology of detecting hu-man targets by through-wall radar can be used to monitor the status and the location information of human targets behind the wall.However,the detection is out of order when classical MUSIC al-gorithm is applied to estimate the direction of arrival.In order to solve the problem,a time-fre-quency associated MUSIC algorithm suitable for through-wall detection and based on S-band stepped frequency continuous wave(SFCW)radar is researched.By associating inverse fast Fouri-er transform(IFFT)algorithm with MUSIC algorithm,the power enhancement of the target sig-nal is completed according to the distance calculation results in the time domain.Then convert the signal to the frequency domain for direction of arrival(DOA)estimation.The simulations of two-dimensional human target detection in free space and the processing of measured data are com-pleted.By comparing the processing results of the two algorithms on the measured data,accuracy of DOA estimation of proposed algorithm is more than 75%,which is 50%higher than classical MUSIC algorithm.It is verified that the distance and angle of human target can be effectively de-tected via proposed algorithm.

MUSIC ALGORITHM FOR LOCATING POINT-LIKE SCATTERERS CONTAINED IN A SAMPLE ON FLAT SUBSTRATE

In this paper, we consider a MUSIC algorithm for locating point-like scatterers contained in a sample on flat substrate. Based on an asymptotic expansion of the scattering amplitude proposed by Ammari et al., the reconstruction problem can be reduced to a calculation of Green function corresponding to the background medium. In addition, we use an explicit formulation of Green function in the MUSIC algorithm to simplify the calculation when the cross-section of sample is a half-disc. Numerical experiments are included to demonstrate the feasibility of this method.

A MODIFIED HIGH-ORDER CUMULANT MUSIC ALGORITHM FOR SIGNAL DOA ESTIMATION

This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.

The Artifact-Free MEG-MUSIC Algorithm on Magnetoencephalographic Source Localization

Multichannel biomagnetometers can be used to measure the spatio temporal magnetic field produced by neural activity in a human brain. The measured data are usually contaminated by noise and some artifact signals. These artifact signals may be caused by heart beats or eye blinks. Actually, these artifact signal sources are also bioelectric activities. In this paper, we demonstrate the effectiveness of MEG MUSIC algorithm for eliminating the artifacts. In the paper, the artifact fields are not considered as noise but as signals that have a linear relationship with their bioelectric source activities. Computer simulations demonstrate that for the localization of sources distributed in the cortical region, the MEG MUSIC algorithm is also efficient under the presence of the artifacts.

基于Root-MUSIC算法的激光扫频干涉测量方法研究

玻璃的厚度作为工业生产中的一项重要的指标,如何对其快速的测量成为了研究热点。提出采用激光扫频干涉测量方法,将采样的信号进行插值拟合,校正其拍频非线性,再通过频谱估计法获取距离信息。当测量厚度较小的玻璃时,FFT算法因受栅栏效应和频谱干涉的影响,导致难以在频谱图中将玻璃前后表面的频率区分出来。为解决该问题,采用Root-MUSIC算法处理信号,实验结果表明在FFT算法分辨临近目标频率失效的情况下,Root-MUSIC算法可以进行有效区分,运算效率也远高于MUSIC算法,且精度可达50微米左右,为解决玻璃厚度测量问题提供了思路。

一种奇异值分解与子空间加权联合的改进MUSIC算法

在低信噪比、小快拍数等非理想条件下,经典DOA估计算法对邻近目标的分辨率严重下降,甚至失去分辨能力。针对这一问题,提出了一种将重构的接收信号协方差矩阵进行奇异值分解并与改进的加权子空间方法相结合的改进算法。该算法充分利用互相关信息构建新的接收信号协方差矩阵,并对噪声子空间信息采用新的校正方法,对噪声特征值进行改造,之后对噪声子空间进行加权,最后与信号子空间加权技术相联合。仿真实验证明,改进算法在低信噪比和小快拍数条件下可以分辨间隔4°的相邻目标,统计分析表明该算法的分辨率明显优于经典MUSIC算法。

基于MFCC-CS-MUSIC的矿井提升机故障源精准识别方法研究

在煤矿生产领域中,矿井提升机作为一种辅助运输的设备,在矿井整个运输工程中承担着非常重要的作用,其安全性和稳定性直接影响着煤矿生产效率和井下工作人员生命安全。矿井提升机故障发生后,其声音信号也会随着设备运行状态而改变,因此可以通过分析该声音的特征来检测设备是否处于不正常运行状态。鉴于此提出了基于MFCC-CS-MUSIC实现的矿井提升机故障源精准识别方法。通过采集提升机音频信号,应用MFCC(梅尔频率倒谱系数)算法提取多个通道声音信号梅尔频率倒谱系数进行故障识别;应用MUSIC(多信号分类)故障识别后的音频信号进行定位求得信号的最小化波达方向。将MUSIC算法求得的DOA(波达方向定位)值作为优化变量,以计算DOA和测量DOA之间的差异为目标函数,利用CS(布谷鸟)算法对目标函数进行寻优,从而实现对提升机故障源精准定位。试验和应用结果均表明,利用CS算法优化后MUSIC算法得到的定位坐标误差Δψ在5°以内,实际位置坐标方位角误差Δθ在4°以内。该方法实现了提升机故障准确识别和提升机故障源的精准定位,大幅缩短了排查矿井提升机故障位置的时间,显著提升了矿井提升机的工作效率。

Melody Generator: A Device for Algorithmic Music Construction

This article describes the development of an application for generating tonal melodies. The goal of the project is to ascertain our current understanding of tonal music by means of algorithmic music generation. The method followed consists of four stages: 1) selection of music-theoretical insights, 2) translation of these insights into a set of principles, 3) conversion of the principles into a computational model having the form of an algorithm for music generation, 4) testing the “music” generated by the algorithm to evaluate the adequacy of the model. As an example, the method is implemented in Melody Generator, an algorithm for generating tonal melodies. The program has a structure suited for generating, displaying, playing and storing melodies, functions which are all accessible via a dedicated interface. The actual generation of melodies, is based in part on constraints imposed by the tonal context, i.e. by meter and key, the settings of which are controlled by means of parameters on the interface. For another part, it is based upon a set of construction principles including the notion of a hierarchical organization, and the idea that melodies consist of a skeleton that may be elaborated in various ways. After these aspects were implemented as specific sub-algorithms, the device produces simple but well-structured tonal melodies.

针对相干信号DOA估计的改进MUSIC算法

针对相干信号波达方向(Direction of Arrival,DOA)估计,提出了一种改进的多重信号分类(Multiple Signal Classification,MUSIC)算法。首先,利用信号协方差矩阵的两个最大特征值所对应的特征向量,构造出两个Toeplitz矩阵;然后,利用前后向空间平滑思想得到这两个矩阵的无偏估计并求和;最后,利用MUSIC算法从中估计出相干信号DOA。和已有方法相比,该方法无需损失阵列孔径且具有更优的DOA估计性能。

基于协方差矩阵重构的改进型MUSIC算法

针对经典MUSIC算法在信源相干条件下算法性能失效问题,提出一种基于数据协方差矩阵二次重构并对噪声子空间进行加权处理的改进型MUSIC算法。首先对接收到的数据协方差矩阵进行重构处理,然后根据特征值分解得到噪声特征值的最大值与最小值的比值关系,使用合适的加权系数得到新的噪声子空间并推导得到改进后的空间谱函数,最后结合谱峰搜索完成对信源目标的DOA估计。仿真结果表明,改进型MUSIC算法有效消除了信号的相干性,不造成阵列孔径损失,在低信噪比、小快拍数以及信号入射间隔较小的情况下,能有效估计出相干信号的波达方向。

基于特征值聚类的MUSIC算法

传统MUSIC算法需要预先知道目标信号源的个数,进一步确定信号子空间和噪声子空间的维数,最后进行谱峰搜索。在实际工程中,无法预知待测目标的个数,针对这一问题,提出了一种基于密度聚类算法的改进型MUSIC算法。该算法将协方差矩阵的特征值进行聚类,通过DBSCAN聚类算法可求出目标信号源的个数,再进一步估计出目标的方位。仿真结果表明:提出的改进算法在信号源个数未知的情况下能够准确估计出信号源的个数和方位,较传统的MUSIC算法有更大实用性。

基于实值Root-MUSIC和MP的间谐波检测算法

针对目前电力系统中谐波与相近间谐波检测精度不高、受噪声影响较大的情况,提出一种基于实值Root-MUSIC和MP的间谐波检测算法。通过对信号建立自相关矩阵并进行特征分解得到特征空间,基于特征空间构造实值Root-MUSIC多项式估计出信号频率,通过MP算法对信号进行分解,估计出信号的幅值与相位。仿真结果表明,该检测算法有着较好的抗噪性,相比传统MUSIC算法在减少了大量运算度的情况下在谐波与相近间谐波的检测中有着更好的精确度。

混合阵列误差校正与快速极化MUSIC算法

为解决通道不一致性对传统极化敏感阵列长矢量模型的测向精度影响及传统长矢量多重信号分类(multiple signal classification,MUSIC)算法实时性不高的问题,本文在传统极化敏感测向系统基础上,在阵列中心增加一个标量平面螺旋天线,利用其天线方向图的增益稳定性,作为内部源对其他矢量通道不一致性进行实时校正;然后将结合标量圆阵和快速傅里叶变换(fastFouriertransform,FFT)的快速MUSIC算法推广到矢量阵列,提出降维快速极化MUSIC算法.仿真结果验证了此误差校正方法的有效性,且快速算法在保证测角精度前提下有效提高了算法实时性.本文为极化敏感阵列测向提供了一种误差校正方法及一种快速实用的测向算法.

基于协方差矩阵Toeplitz重构的MUSIC算法的波达方位估计方法

针对传统波达方向(Direction of Arrival,DOA)估计方法通过空间平滑对相干信号进行处理损失阵列孔径的问题,文章提出了一种基于协方差矩阵托普利兹(Toeplitz)矩阵重构的多重信号分类(Multiple Signal Classification,MUSIC)算法的波达方位估计方法。该方法首先根据阵列接收数据的协方差矩阵及其翻转矩阵来构造新协方差矩阵,并利用新协方差矩阵构造Toeplitz矩阵,然后对其进行特征值分解,得到Toeplitz矩阵的噪声子空间,利用噪声子空间求出信号空间谱,通过谱峰搜索估计入射信号的方位角。文中方法拓展了阵列孔径,增加了可估计相干信号的数量,提升了方位估计的性能,提高了阵列的空间分辨率。仿真和湖上实验数据处理结果表明,文中方法可估计出更多的相干信号,而且在低信噪比、少快拍以及信号入射角度间隔较小时仍然具有良好的方位估计性能。

一种基于MUSIC算法的宽带信号DOA估计

针对传统MUSIC算法在信噪比较低的条件下分辨力下降的问题,提出一种适用于宽带信号的改进MUSIC算法,首先对接收信号进行快速傅里叶变换,将时域信号转移至频域,并划分子带;对每个子带上的信号求解协方差矩阵,进行特征值分解,构造信号子空间和噪声子空间;将信号在两个子空间的投影比值作为参考,进行子带波达方向估计,进而平均求出最终宽带信号波达方向。仿真结果表明,在低信噪比条件下,相比于传统MUSIC算法,所提方法具有较好的空间分辨力。

基于改进野狗优化算法的二维MUSIC声源定位研究

针对二维多重信号分类算法(MUSIC)在进行信号波达方向估计时存在运算复杂、速度慢的问题,提出基于改进野狗优化算法的二维MUSIC声源定位算法。该算法首先通过结合精英反向学习策略与二次插值方法改进野狗优化算法,提高算法寻优能力,再与MUSIC算法结合,使算法能快速且准确定位。实验结果表明,该算法与传统MUSIC方法对比,定位精度得到提高,且节约了88.6%的搜索时长,计算量降低,获得更好的实时性。

基于TLS的改进子空间投影算法

针对经典MUSIC算法在信源相干、低信噪比、小快拍数等非理想环境下性能失效的问题,提出了一种改进的基于TLS的加权子空间投影算法。首先对阵列接收的数据协方差矩阵进行重构处理,以达到解相干目的;其次充分利用子空间信息,基于总体最小二乘拟合方法对特征值进行拟合修正,基于修正MUSIC算法思想,利用校正后的噪声特征值和信号特征值分别对噪声子空间和信号子空间进行加权处理,得到改进后的噪声子空间和信号子空间,并将两者结合得到新的空间谱函数;最后进行谱峰搜索,完成信号源的波达方向估计。仿真结果表明,改进后的算法既适用于相干信号环境,在低信噪比、小快拍数及信号入射角度间隔较小的情况下,又能有效估计出信源的波达方向。

基于海试数据的水声信号自适应波束形成方法性能分析

为了更好验证自适应波束形成技术的实际应用,本文研究了基于海试数据的不同自适应波束形成方法的性能,对比分析CBF算法、MVDR算法、MUSIC算法的优缺点,总结了各算法适用条件及实用性。研究结果表明,在声呐的实际应用中,CBF算法更适用于实时性要求较高、目标数量较少的方位探测;MUSIC算法更适用于实时性要求不高、目标数量较多、目标数量已知的方位探测;MVDR算法更适用于实时性要求不高、目标数量较多、目标数量未知的方位探测。