基于快速平行因子分解的声矢量传感器阵二维DOA估计

将声矢量传感器阵列参数估计问题与平行因子(Parallel factor,PARAFAC)模型相结合,提出了一种基于快速PARAFAC分解的二维波达方向(Direction of arrival,DOA)估计算法。该算法首先将接收信号构建为PARAFAC模型,然后在数据域对参数矩阵进行初估计,最后利用PARAFAC分解获得信号二维DOA估计。该算法能够应用于任意结构的声矢量传感器阵列,同时能够得到和信源一一匹配的仰角和方位角估计。借助于参数矩阵的初始估计,所提算法收敛速度较快,其计算复杂度大大降低。该算法角度估计性能接近于PARAFAC算法,同时优于借助旋转不变性进行信号参数估计(Estimation of signal parameters via rotational invariance technique,ESPRIT)算法和传播算子(Propagator method,PM)算法。     

双平行线阵中基于Euler变换传播算子的二维DOA估计算法

考虑双平行线阵中非圆信号二维波达方向 (Direction of arrival,DOA) 估计问题,提出了一种基于Euler变换传播算子（Propagator method,PM）的二维DOA 估计算法。该算法利用非圆信号的特性,扩展了接收数据矩阵,使得角度估计性能优于二维PM算法。同时采用Euler变换把非圆PM算法中的复数运算转换为实数运算,降低计算复杂度,角度估计性能逼近非圆PM算法。该算法可以实现二维角度的自动配对,与传统PM算法相比,可同时估计出更多的信源。该算法的优越性均可在文中得到验证。     

基于极大后验原理的非线性系统传感器故障估计

针对扩展卡尔曼滤波算法(Extended Kalman filter,EKF)计算复杂,粒子滤波算法动态跟踪能力差,单一无先导扩展卡尔曼滤波算法(Unscented Kalman filter,UKF)滤波精度低等缺陷,本文根据极大后验原理(Max-imum posterior principle,MPP),针对一类非线性系统设计了一种改进型的无先导卡尔曼故障估计滤波器来估计被控系统所发生的加性传感器故障。首先根据极大后验估计原理,推导出一种最优常值故障估计器。在此基础之上,推导出次优的加性常值故障估计滤波器,并对故障估计滤波器进行了无偏性证明。最后,将得到的理论结果应用于非线性倒立摆系统,仿真验证了所提方法的有效性。     

采用逐次正交化法的最优分布式Kalman滤波器

为解决大系统中应用整体式Kalman滤波遇到的非实时性难题,本文提出一种能保持最优的分布式Kalman滤波计算方法——逐次正交化法。这是一种适合多微机并行计算的最优算法,确保滤波的“实时性”,优于一般次优分布式Kalman滤波器。     

自适应阵列天线中应用Kalman补偿算法的探讨

为了补偿在自适应阵列天线中应用Kalman算法时,由于模型误差和计算误差所引起的自适应阵列天线性能的损失,本文引入Kalman补偿算法,即自适应Kalman渐消记忆滤波算法及自适应Kalman参量识别滤波算法,获得了有益的结果。最后在上述两种算法的基础上,导出了一种新的算法,该算法具有上述两种算法的优点。     

基于分布式Kalman滤波和BP神经网络组合的静电陀螺漂移辨识方法

提出一种新的方法,把分布式Kalman滤波(DKF)方法与后向传播神经网络(BPNN)技术相结合,用于静电陀螺漂移的模型辨识.首先,为了消除测量噪声影响,将同一个静电陀螺带有噪声的多次测量数据集映射到一个虚拟的传感器网络中,然后采用具有嵌入式紧致滤波功能的DKF对映射数据进行滤波预处理.在此基础上,将预处理结果转换为用于训练神经网络的输入数据和输出数据,然后采用BPNN辨识静电陀螺漂移.实验表明,该方法可有效用于陀螺漂移的模型辨识.     

一种改进的UGPF算法及其在导航问题中的应用

通过对高斯粒子滤波(GPF)算法的分析与总结,提出了一种基于无味卡尔曼滤波(UKF)方法的改进GPF算法(改进UGPF算法).该方法主要利用UKF获取更优的重要性抽样函数,同时优化GPF滤波的算法流程结构.最后通过二维目标跟踪过程中位置导航参数估计问题,对该算法进行了仿真分析,所得结果验证了该算法的有效性.     

基于自适应容积粒子滤波的车辆状态估计

针对车辆状态估计中由模型的强非线性、噪声的非高斯分布等相关因素导致估计精度下降甚至发散的问题,本文提出了基于自适应容积粒子滤波(Adaptive cubature particle filter,ACPF)的车辆状态估计器。首先基于非稳态动态轮胎模型,构建高维度非线性八自由度车辆模型。其次利用自适应容积卡尔曼滤波(Adaptive cubature Kalman filter,ACKF)算法更新基本粒子滤波(Particle filter,PF)算法的重要性密度函数,以完成自适应容积粒子滤波算法设计。利用车载传感器信息,运用ACPF算法实现对车辆的侧倾角、质心侧偏角等关键状态变量高精度在线观测。搭建Simulink-Carsim联合仿真平台进行了算法的验证,结果表明该算法状态估计精度高于传统无迹粒子滤波(Unscented particle filter,UPF)算法,且算法运算效率高于UPF算法,而传统PF估计值发散。研究结果为实现车辆动力学精准控制提供了理论支持。     

基于经验模态分解的有限阵元宽带自适应阵列DBF方法

针对有限阵元条件下宽带自适应阵列自由度不够和抗干扰性能下降的问题,提出一种基于经验模态分解(Empirical mode decomposition,EMD)的宽带自适应阵列数字波束形成(Digital beamforming,DBF)方法。该方法首先对阵元接收的快拍数据进行EMD处理,然后对各个模态函数矩阵应用线性约束最小方差(Linearlyconstrained minimum variance,LCMV)波束形成算法求解自适应权矢量,最后对信号进行重构。与传统的基于快速傅里叶变换(Fast Fourier transform,FFT)子带自适应阵列方法相比,该方法具有以下优势:适用于阵元数目受限的宽带自适应阵列,无需事先指定模态函数划分的频段,可以提高阵列处理的自由度。仿真验证了所提方法的有效性。     

用FPGA实现abc坐标系的三维空间矢量调制算法

研究了一种应用于三相四桥臂逆变器的基于abc坐标系的新的空间矢量调制算法,避免了传统的αβ0坐标变换,使开关矢量的选取和占空比的计算更简单,显著降低了调制算法的复杂性。同时详细说明了基于abc坐标系的空间矢量脉宽调制算法的原理,并研究了用FPGA来实现该空间矢量调制算法的方法。由于FPGA不需要顺序执行指令,可以多个进程并发执行,响应速度快,可明显提高系统性能。仿真和实验结果证明了该空间矢量调制算法的正确性和利用FPGA实现该算法的可行性。     

Computationally Efficient MUSIC-Based Algorithm for Joint Direction of Arrival (DOA) and Doppler Frequency Estimation in Monostatic MIMO Radar

The problem of joint direction of arrival (DOA) and Doppler frequency estimation in monostatic multiple-input multiple-output (MIMO) radar is studied and a computationally efficient multiple signal classification (CE-MUSIC) algorithm is proposed.Conventional MUSIC algorithm for joint DOA and Doppler frequency estimation requires a large computational cost due to the two dimensional (2D) spectral peak searching.Aiming at this shortcoming,the proposed CE-MUSIC algorithm firstly uses a reduced-dimension transformation to reduce the subspace dimension and then obtains the estimates of DOA and Doppler frequency with only one-dimensional (1D) search.The proposed CE-MUSIC algorithm has much lower computational complexity and very close estimation performance when compared to conventional 2D-MUSIC algorithm.Furthermore,it outperforms estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm.Meanwhile,the mean squared error (MSE) and Cramer-Rao bound (CRB) of joint DOA and Doppler frequency estimation are derived.Detailed simulation results illustrate the validity and improvement of the proposed algorithm.     

Computationally Efficient Direction of Arrival Estimation for Improved Nested Linear Array

Nested linear array enables to enhance localization resolution and achieve under-determined direction of arrival(DOA)estimation. In this paper,the traditional two-level nested linear array is improved to achieve more degrees of freedom(DOFs)and better angle estimation performance. Furthermore,a computationally efficient DOA estimation algorithm is proposed. The discrete Fourier transform(DFT)method is utilized to obtain coarse DOA estimates,and subsequently,fine DOA estimates are achieved by spatial smoothing multiple signals classification(SS-MUSIC)algorithm. Compared to SS-MUSIC algorithm,the proposed algorithm has the same estimation accuracy with lower computational complexity because the coarse DOA estimates enable to shrink the range of angle spectral search. In addition,the estimation of the number of signals is not required in advance by DFT method. Extensive simulation results testify the effectiveness of the proposed algorithm.     

Low-Complexity DOA Estimation of Noncircular Signals for Coprime Sensor Arrays

This paper presents a low-complexity method for the direction-of-arrival(DOA)estimation of noncircular signals for coprime sensor arrays. The noncircular property is exploited to improve the performance of DOA estimation. To reduce the computational complexity,the rotational invariance propagator method(RIPM)is included in the algorithm. First,the extended array output is reconstructed by combining the array output and its conjugated counterpart. Then,the RIPM is utilized to obtain two sets of DOA estimates for two subarrays. Finally,the true DOAs are estimated by combining the consistent results of the two subarrays. This illustrates the potential gain that both noncircularity and coprime arrays provide when considered together. The proposed algorithm has a lower computational complexity and a better DOA estimation performance than the standard estimation of signal parameters by the rotational invariance technique and Capon algorithm. Numerical simulation results illustrate the effectiveness and superiority of the proposed algorithm.     

A Real-Valued 2D DOA Estimation Algorithm of Noncircular Signal via Euler Transformation and Rotational Invariance Property

The problem of two-dimensional(2 D)direction of arrival(DOA)estimation for double parallel uniform linear arrays is investigated in this paper.A real-valued DOA estimation algorithm of noncircular(NC)signal is proposed,which combines the Euler transformation and rotational invariance(RI)property between subarrays.In this work,the effective array aperture is doubled by exploiting the noncircularity of signals.The complex arithmetic is converted to real arithmetic via Euler transformation.The main contribution of this work is not only extending the NC-Euler-ESPRIT algorithm from uniform linear array to double parallel uniform linear arrays,but also constructing a new 2 Drotational invariance property between subarrays,which is more complex than that in NCEuler-ESPRIT algorithm.The proposed 2 DNC-Euler-RI algorithm has much lower computational complexity than2 DNC-ESPRIT algorithm.The proposed algorithm has better angle estimation performance than 2 DESPRIT algorithm and 2 D NC-PM algorithm for double parallel uniform linear arrays,and is very close to that of 2 D NC-ESPRIT algorithm.The elevation angles and azimuth angles can be obtained with automatically pairing.The proposed algorithm can estimate up to 2(M-1)sources,which is two times that of 2 D ESPRIT algorithm.Cramer-Rao bound(CRB)of noncircular signal is derived for the proposed algorithm.Computational complexity comparison is also analyzed.Finally,simulation results are presented to illustrate the effectiveness and usefulness of the proposed algorithm.     

A Generalized Propagator Algorithm for Localization of Non-circular Sources Using Arbitrary Array Geometry

A propagator-based algorithm for direction of arrival(DOA)estimation of noncoherent one-dimensional(1-D) non-circular sources is presented such as binary phase shift keying(BPSK) and amplitude modulation(AM). The algorithm achieves DOA estimation through searching a 1-D spectrum,which is newly formed on the basis of the rank reduction criterion,and works well without knowledge of the non-circular phases. And then,a searchfree implementation of the algorithm is also developed by using the polynomial rooting technique. According to the noncircular property,the algorithm can virtually enlarge the array aperture,thus significantly improving its estimation accuracy and enabling it to handle more sources than the number of sensors. Moreover,the algorithm requires no rotational invariance,so it can be applied to arbitrary array geometry and dispense with the high-complexity procedure of the eigen-decomposition of the correlation sample matrix. Finally,numerical simulations verify the performance and effectiveness of the proposed algorithm.     

互耦补偿的神经网络算法用于均匀圆阵波达方向估计

径向基函数神经网络方法在阵列信号处理中得到广泛应用。但是，阵列天线单元间耦合对基函数中心的影响会降低波达方向估计的精度，因此有必要对神经网络的输入数据进行互耦补偿，以生成正确的基函数中心。本文首先利用矩量法计算天线阵的广义阻抗矩阵，再使用直接数据域算法对神经网络的训练数据进行互耦补偿，神经网络训练完成后，神经网络用于实现波达方向估计。仿真结果表明，神经网络算法和直接数据域算法结合具有补偿效果好，计算量小的特点。     

虚拟阵列测向性能分析

针对相干信号源测向以及模糊的实际问题,提出一种基于任意形状平面阵列的测向方法。经计算机仿真,证明该方法的有效性。     

大时延扩展CDMA信道下空时多用户检测

提出了基于平行因子分析的大时延扩展CDMA信道下空时多用户检测算法.文中建立了大时延扩展CD-MA信道下接收信号新模型,它将接收信号看成是2个三线性模型之和,并提出了广义三线性交替最小二乘的算法(TALS-G)进行空时多用户检测.仿真结果说明:TALS-G算法具有较好误码率性能,且该方法在阵列扰动情况下仍有较好的性能.它无需信道衰落系数和DOA信息,是盲信号检测方法,具有鲁棒性.     

极化敏感均匀圆阵中PARAFAC信号检测算法

分析了极化敏感均匀圆阵接收到的信号，该信号具有三线性模型特征。提出了极化敏感均匀圆阵中平行因子信号检测算法。该算法利用三线性交替最小二乘（TALS）算法估计出信源矩阵，然后对其进行判决。仿真结果表明；该算法误码率性能接近于非盲解相关方法;与非盲解相关方法相比，在较高的SNR情况下误码率相差不到2dB；且在阵列扰动情况下仍具有较好的误码率性能。该算法无需空域信息和极化信息，是一种盲鲁棒方法。