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.     

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。     

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。     

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.     

声矢量传感器阵中基于Kalman滤波和OPASTd的DOA跟踪算法

研究了声矢量传感器阵动目标角度跟踪问题,并提出了声矢量传感器阵中 一种基于Kalman滤波和正交压缩近似投影子空间跟踪(Orthonormal projection approximation and subspace tracking of deflation, OPASTd)的波达方向(Direction of arrival,DOA)跟踪算法。该算法通过OPASTd算法来进行DOA的跟踪,从而克服了PASTd算法由于在某些情况下振荡但不收敛进而压缩数据、在迭代更新中由特征向量的不准确性产生误差累积等原因引起破坏信号子空间正交性的缺陷。Kalman滤波和OPASTd相结合算法可在估计角度的同时进行数据关联,与传统的PASTd算法相比,角度跟踪性能更好。该算法的优越性均可在文中得到验证。     

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。     

ESPRIT算法在单快拍阵列信号中的应用

ESPRIT算法利用旋转不变技术估计信号参数,是近年来提出的阵列信号处理理论的一个新领域,它具有高分辨率、高精度、不需空间搜索等优点。单快拍总被当作相干信号处理,本文采用了Teoplitz结构协方差矩阵估计,起了空间平滑作用,所以可以估计相于信号源的波达方向(DOA),与标准空间平滑协方差矩阵估计相比较,Teoplitz协方差阵估计在总体上提高了信噪比,并且不牺牲阵列孔径。 计算机模拟结果表明,基于Teoplitz结构协方差矩阵的TLS-ESPRIT算法在20dB信噪比下,可以分辨出相隔0.2瑞利波束宽度的空间信号。     

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。     

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。     

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.     

一维与二维信号的快速内插算法

一维信号的快速内插在语音处理、数字波束形成、雷达实时仿真等方面有重要应用。图象处理中则常需对二维信号进行内插。文中首先提出了一种利用ＦＦＴ的一维信号内插的子序列算法。去除了Ａｄａｍｓ算法的额外补偿项ｅｒ（ｍ）与其他冗余运算，所需实乘数只有Ａｄａｍｓ算法的２０％左右，实加数约为Ａｄａｍｓ算法的３０％。然后本文又提出了一种基于子序列ＦＦＴ的二维信号快速内插算法。该算法不仅解决了Ｓａｔｈｙａｎａｒａｙａ     

基于线性参数变化自适应观测器的鲁棒故障诊断

基于线性参数变化自适应观测器,研究了一类具有外部扰动的不确定线性参数变化系统的鲁棒故障诊断问题。针对飞控系统具有非线性和时变的特点,运用了线性参数变化技术,提出一种基于线性参数变化自适应观测器的自适应故障估计算法来估计故障信息。为降低外部扰动对故障估计的影响,引入了H∞鲁棒性能指标来设计线性参数变化自适应观测器以及故障估计算法。结果表明,该方法具有良好的故障估计性能,且对外部扰动具鲁棒性。设计过程中,将参数的求解转化为线性矩阵不等式（LMIs）约束下的凸优化问题。最后,将该方法应用于直升机线性参数变化飞控系统执行器故障诊断,仿真实验验证了该方法的有效性。     

对数正态分布下基于截断数据的统计分析

加速寿命试验是在短时间内获得高可靠产品的失效数据的一种有效办法,因而得到普遍应用。但在低应力下,要在短时间内得到失效数据较为困难,常常进行到一段时间被迫停止,因而得到的是截断数据。本文考虑在有截断数据情形下,对数正态分布未知参数的估计。具体地讲,试验一共有k个加速应力S_1,S_2,…,S_k,有R个产品进行试验,在应力S_i下有n_i个产品。进行加速寿命试验,但是我们得到的不全是寿命失效数据y_j~i,i=1,2,…,k,j=1,2,…,n_i,n_1+n_2+…+n_i=n,而是Z_j~i=min(y_j~i,τ_j~i是预先给定的截断时间,记δ_j~i=I(y_j~i≤τ_j~i),则我们只能观察到(Z_j~i,δ_j~i),i=1,2,…,k,j=1,…,n_i。本文给出了用(Z_j~i,δ_j~i)来估计未知参数的一种方法,证明了这种参数估计的存在性,无偏性及相合性。     

基于带限随机信号相位谱的分数延时估计方法

传统延时估计方法的精度受采样间隔的限制。为提高精度,提出一种新的适用于宽带信号的分数延时估计方法。该方法将周期图法与抛物线插值应用于带限随机信号的互相关谱,可以获得连续的延时估计。文中通过一系列仿真实验对该方法与其他分数延时估计方法的性能进行比较。结果表明,该方法的性能优于其他估计方法,在信噪比较高的情况下,可以接近克拉美罗下界。由于发射信号具有宽带和随机的特点,该方法适用于低截获概率雷达。     

基于方向调制的无线安全传输基本原理、关键技术与未来展望

方向调制(Directional modulation,DM)作为无线物理层安全传输的关键技术能够很好地提升系统的安全性能。然而,由于在角度测量过程中存在误差,因此需要在设计有用信号波束成形向量和人工噪声(Artificial noise,AN)投影矩阵时考虑角度误差,从而提升系统安全性能。本文首先描述了DM系统模型,然后介绍了到达角(Direction of arrival,DOA)估计技术、稳健波束成形设计的3种算法及功率分配技术。仿真表明:稳健波束成形合成方法的性能明显要优于非稳健合成方法,且有用信号和AN之间最优功率分配能明显提高安全速率性能。最后,对DM未来新的发展方向与所面临的挑战性等开放问题进行展望与总结。     

Geo-location for Ground Target with Multiple Observations Using Unmanned Aerial Vehicle

In order to improve the target location accuracy of unmanned aerial vehicle(UAV),a novel target location method using multiple observations is proposed.Firstly,the camera intrinsic parameters are calibrated.Then,the weighted least squares estimation is used to improve the localization precision because the traditional crossover method is vulnerable to noise and has low precision.By repeatedly measuring the same target point,a nonlinear observation equation is established and then covered to linear equations using Taylor expansion.The weighted matrix is obtained according to the height of the measurement point and the camera optic axis pointing angle,and then the weighted least squares estimation is used to calculate the target position iteratively.Finally,the effectiveness and robustness of this method is verified by numerical simulation and flight test.The results show that this method can effectively improve the precision of target location.     

Morphological Undecimated Wavelet Decomposition Fusion Algorithm and Its Application on Fault Feature Extraction of Hydraulic Pump

Since vibration signals of hydraulic pump are mostly nonlinear and traditional fusion algorithm cannot satisfyingly process them,a morphological undecimated wavelet decomposition fusion(MUWDF)algorithm is proposed.Firstly,under the framework of morphological undecimated wavelet decomposition(MUWD),multi-channel signals are decomposed.Approximate signals of all decomposition layers are selected by feature energy factor and fused according to the presented fusion rules.Furthermore,specific method for optimal selection of MUWDF parameters is presented to avoid subjective influences.Finally,the proposed algorithm is verified by simulation signals and pump vibration signals.     

线性方程组的并行算法及在Transputer系统上的实现

在“一种有效的多Transputer系统的并行算法——ABC法”一文的基础上,本文进一步研究将ABC法用于变带宽矩阵线性方程组的求解问题,对线性方程组的系数矩阵采用了逐行一维存储方式,提出了相应的并行Gauss消元法,给出了该算法的效率.分析结果表明,带宽越大方程阶数越高,这种算法的效率就越高。因此本算法适用于高阶的大带宽线性方程组的求解问题. 根据本文的算法,编制了线性方程组的并行求解程序,并分别在一个、二个和四个T414系统上做了若干算例,结果表明本文分析的结论是正确的。