RARE-based localization for mixed near-field and far-field rectilinear sources

In this paper, a novel localization method for mixed near-field (NF) and far-field (FF) rectilinear or strictly noncircular sources is proposed using the noncircular information for a symmetric uniform linear array (ULA). For FF case, we adopt the NC-MUSIC method to achieve the DOA parameter, for NF case, by exploiting the center symmetrical characteristic of the ULA, we decouple the array steering vectors into two new vectors: one related only to the DOA parameter, and the other dependent on both DOA and range parameters. Based on the principle of rank reduction (RARE), three MUSIC-like estimators are formed to estimate the direction of arrival (DOA) and the range of mixed NF and FF rectilinear sources successively. Meanwhile, distinguishing the types of sources is also solved. The deterministic Cramer–Rao bound (CRB) of the mixed rectilinear signals is derived by the Slepian–Bangs formulation. Simulation results are provided, showing that the proposed method yields a performance better than existing ones.     

Coherent summation of multiple short-time signals for direct positioning of a wideband source based on delay and Doppler

We consider identifying the source position directly from the received source signals. This direct position determination (DPD) approach has been shown to be superior in terms of better estimation accuracy and improved robustness to low signal-to-noise ratios (SNRs) to the conventional two-step localization technique, where signal measurements are extracted first and the source position is then estimated from them. The localization of a wideband source such as a communication transmitter or a radar whose signal should be considered deterministic is investigated in this paper. Both passive and active localization scenarios, which correspond to the source signal waveform being unknown and being known respectively, are studied. In both cases, the source signal received at each receiver is partitioned into multiple non-overlapping short-time signal segments for the DPD task. This paper proposes the use of coherent summation that takes into account the coherency among the short-time signals received at the same receiver. The study begins with deriving the Cramér–Rao lower bounds (CRLBs) of the source position under coherent summation-based and non-coherent summation-based DPDs. Interestingly, we show analytically that with coherent summation, the localization accuracy of the DPD improves as the time interval between two short-time signals increases. This paper also develops approximate maximum likelihood (ML) estimators for DPDs with coherent and non-coherent summations. The CRLB results and the performance of the proposed source position estimators are illustrated via simulations.     

Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (≤10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramér–Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC.     

Complex ICA by negentropy maximization.

In this paper, we use complex analytic functions to achieve independent component analysis (ICA) by maximization of non-Gaussianity and introduce the complex maximization of non-Gaussianity (CMN) algorithm. We derive both a gradient-descent and a quasi-Newton algorithm that use the full second-order statistics providing superior performance with circular and noncircular sources as compared to existing methods. We show the connection among ICA methods through maximization of non-Gaussianity, mutual information, and maximum likelihood (ML) for the complex case, and emphasize the importance of density matching for all three cases. Local stability conditions are derived for the CMN cost function that explicitly show the effects of noncircularity on convergence and demonstrated through simulation examples.     

Simultaneous direct position determination and pulse deinterleaving by a moving receiver

In this paper, a method for simultaneous deinterleaving and direct position determination (DPD) of multiple pulsed sources is presented, utilizing a pulse receiver on a moving platform. Traditional localization methods were based on the estimation of an intermediate parameter, and the intersection of these parameters results in source localization. DPD removes this step and determines the source's position by a single step processing. Previous works in DPD achieved suitable results in localization performance and multi-target resolution, but their problem structure is not applicable to the multiple pulsed sources, and a deinterleaving step is necessary. Moreover, traditional deinterleaving methods have limited performance. We present an approach, which localizes the sources and separates their pulses in a single-phase algorithm, based on a probability spectrum. Simulation results show that this combination of DPD and deinterleaving can enhance the performance of both phases.     

PSO随机数参数设置的多目标定位方法研究

为了解决林业部门对森林防火安全监测系统中对多个声音目标的跟踪及定位问题,根据声音能量随距离衰减模型,提出了采用粒子群算法(PSO)的多目标定位与优化方法.通过利用极大似然法对声音强度模型的定位算法,采用惯性权重的粒子群算法,着重讨论了随机参数不同的设置方法对定位追踪精度性能的影响.通过仿真实验证明,粒子群算法中设置随机数参数为常数,可以有效提高目标定位精度,并减小搜索复杂度.     

双基地MIMO雷达中基于传播算子的DOD和DOA估计算法

多输入多输出(Multiple-input multiple-output,MIMO)雷达利用多个天线发送和接收信号,具有超过传统相控阵的潜在优势.本文提出一种双基地MIMO雷达中基于传播算子的离开角(Direction of departure,DOD)和到达角(Direction of arrival,DOA)估计...     

Quaternion-MUSIC for near-field strictly noncircular sources with large-scale polarization array

In this paper, a novel localization method is proposed for DOA, range and polarization estimation of near-field noncircular sources in massive multiple-input-multiple-output (MIMO) systems. Compared with traditional MUSIC-based algorithms, the proposed algorithms can separate the polarization parameters from the spatial spectrum function, avoiding the four-dimensional (4-D) spectrum search and realizing the fast localization of the near-field source with high accuracy. First, the dimension-reduced MUSIC (DR-MUSIC) algorithm is proposed for DOA and range estimation with low computational complexity, and given a closed-form expression of polarization estimation. Next, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. In addition, the analysis of the computational complexity and simulations of the proposed method are provided, showing that the proposed method yields a better performance than DR-MUSIC in massive MIMO systems.     

非圆信号与圆信号共存下的快速方位估计算法*

针对非圆信号和圆信号共存时,信号的波达方向估计算法运算量较大的问题,提出了一种可同时估计非圆信号和圆信号DOA的快速算法。首先,利用观测数据及其复共轭构造中心复共轭对称矩阵,通过双映射定理可将该矩阵转换为实矩阵;然后,利用多级维纳滤波,无需计算阵列协方差矩阵及对其进行特征分解,仅通过对接收的实数据矩阵递推可快速求出观测数据的信号子空间;最后,利用酉ESPRIT算法的实值性,不必谱峰搜索,在保证DOA估计精度的同时可大大降低算法的计算复杂度。仿真结果验证了算法的有效性。     

分步定位法在ZigBee定位系统中的应用

在几种常规定位算法分析基础上,提出一种新型分步定位法。通过利用待测移动节点最近的3个参考节点可快速计算出移动节点的坐标值,从而使定位算法更容易实现。实验结果表明,该方法在不增加任何硬件成本的前提下可显著提高定位精度并增强系统稳定性,同时降低环境噪声带来的测量误差。与常规定位算法相比,该算法可广泛应用于各种基于距离的定位场合。     

加权最小二乘联合遗传算法的无源定位

针对复杂环境下运动通信辐射源的无源定位,闭式解方法对于时频差模型中的测量噪声敏感且存在定位均方根误差较大问题.为了改善大观测误差下的定位性能,本文提出一种加权最小二乘联合遗传算法的递推式混合TDOA/FDOA定位方法.该方法首先利用已知站点观测大量时频差数据并建立误差模型,基于模型对定位过程中的多组时频差序列进行数据处理;其次通过加权最小二乘求解目标位置的初始值;然后采用改进的遗传算法在初始值的基础上通过多组时频差序列不断迭代、递推求解,修正位置坐标;最后利用位置估计和频差模型完成对目标速度估计.仿真结果表明,本文定位算法相比于经典两步加权最小二乘法具有更低的均方根误差,在大观测误差下能保持较高精度.同时相比于其他混合定位算法收敛速度快,可以有效减少计算量.     

单基地MIMO雷达中相干目标的波达角和多普勒频率快速联合估计算法

针对单基地MIMO中相干目标的波达角（Direction-of-arrival,DOA）和多普勒频率联合估计问题,提出了一种降维-前向平滑-传播算子算法（Reduced dimension-forward spatial smoothing-propagator method,RD-FSS-PM）。该算法首先通过对接收信号进行降维变换以降低复杂度,继而利用前向平滑技术（Forward spatial smoothing,FSS）实现解相干,最后通过传播算子算法（Propagator method,PM）实现了对相干目标的波达角和多普勒频率联合估计,且无需额外配对。与传统的FSS-PM算法相比,所提算法波达角估计性能提升,多普勒频率估计性能接近而复杂度大大降低。本文同时分析了算法的理论均方误差（Mean squared error,MSE）和单基地MIMO雷达中波达角和多普勒频率联合估计问题的克拉美罗界（Cramer-Rao bound,CRB）。最后提供了详尽的仿真实验以验证算法的性能。     

基于30°角同心圆环形取样的室内人员检测方法

在现有基于信道状态信息的室内无源定位方法中,取样点的选取对指纹库的特征匹配准确率以及定位精度具有较大影响.根据WiFi信号的传输特性和信道的衰落特征,提出一种30°角同心圆环形取样法.离线阶段,按照同心圆对检测区域实现环形划分并每隔30.进行一次取样,运用主成分分析算法提取差异化信号特征并构建指纹库.在线阶段,通过陆地移动距离算法进行入侵检测,当检测到有人存在时,利用改进的支持向量回归算法并引入高斯核函数对数据进行特征匹配,最终实现人员的精确定位.实验结果表明,与CSI-MIMO、FIFS方法相比,该方法定位精度更高,定位误差更小.     

基于混合微粒群算法的说话人识别

为了解决传统高斯混合模型(GMM)对初值敏感,在实际训练中极易得到局部最优参数的问题,提出了一种采用微粒群算法优化GMM参数的新方法。该方法将最大似然估计融入到微粒群算法迭代过程中,形成了新的混合算法。它利用微粒群算法的全局优化性及最大似然估计的局部寻优性求解高斯混合模型的参数,以提高参数精度。说话人辨认实验表明,与传统的方法相比,新方法可以得到更优的模型参数,使得系统的识别率进一步提高。     

Received signal strength based positioning for multiple nodes in wireless sensor networks

We address the problem of locating multiple nodes in a wireless sensor network with the use of received signal strength (RSS) measurements. In RSS based positioning, transmit power and path-loss factor are two environment dependent parameters which may be uncertain or unknown. For unknown transmit powers, we devise two-step weighted least squares (WLS) and maximum likelihood (ML) algorithms for node localization. The mean square error of the former is analyzed in the presence of zero-mean white Gaussian disturbances. When both transmit powers and path-loss factors are unavailable, two nonlinear least squares estimators, namely, the direct ML approach and combination of linear least squares and ML algorithm, are developed. Numerical examples are also included to evaluate the localization accuracy of the proposed estimators by comparing with two existing node positioning methods and the Cramér–Rao lower bound.     

过完备ICA 算法在语音信号提取中的应用

在语音信号处理中常用麦克风采集语音,然后用算法进行提取和分离,目前常用的有独立分量分析（Independent component Analysis,ICA）算法。但是当麦克风个数少于说话人的个数时,即欠定情形,此时语音信号的提取需采用过完备ICA算法。提出了一种基于过完备ICA算法的两步算法：估计混合矩阵的几何算法和估计源矩阵的最短路径法。该算法能在欠定情形下对语音信号的提取有很好的作用,仿真实验验证了这一结果。     

宽线性波束形成技术综述

在圆信号的假设条件下,传统的线性波束形成技术仅仅利用了天线阵列观测矢量的协方差矩阵。然而,现代通信领域中的很多人工调制信号具有非圆特性,观测矢量不仅存在协方差矩阵,还存在伪协方差矩阵。宽线性波束形成技术是针对非圆信号环境提出的一类新技术,该类技术通过构造一个包含天线阵列观测矢量及其共轭的扩展观测矢量,建立有利于特定方向信号接收的目标函数及约束,推导出相应的扩展权重矢量。同传统线性波束形成技术相比,宽线性波束形成技术对非圆信号的接收性能有了明显提升,近年来,该类技术成为国内外研究热点之一。本文介绍了圆信号和非圆信号定义,给出了阵列模型并介绍了最小方差无畸变响应波束形成,对各种宽线性波束形成算法进行了综述,并对宽线性波束形成技术的下一步研究方向进行了展望。     

基于TOA非相干检测UWB测距系统的性能分析

鉴于非相干CRB对于分析非相干TOA性能有很大参考价值,将UWB测距系统中接收端信号经过平方律后进行包络检波所得的信号拟合成指数衰减的曲线,利用最大似然估计推导出非相干检测的CRB,并利用TC、MES算法验证其正确性。对影响TOA性能的非相干检测参数进行了分析,得出积分窗口的选取必须在TOA性能和算法收敛速度上进行折衷的结论。提出了一种新的分析信道时延扩展对CRB影响的方法,最后得出结论CRB随着时延扩展的增大而增大。     

重叠特征策略与参数优化的运动想象脑电模式识别

针对运动想象脑电信号的非线性、非平稳特性,提出重叠特征策略与参数优化方法.通过重叠频带滤波(OFB)进行预处理,在滤波后的信号上提取共同空间模式特征(CSP).将OFB-CSP特征输入鲁棒支持矩阵机,完成模式识别,在模式识别中通过校正粒子群算法(CPSO)动态调整被试个体最优参数.在两个公开数据集上进行实验,分别验证OFB预处理可提升CSP特征区分度,CPSO可为个体寻找最优的鲁棒支持矩阵机分类参数.文中方法提升运动想象识别率,样本和计算资源需求较小,适合脑机接口的实际应用.