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。     

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.     

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.     

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.     

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。     

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。     

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。     

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     

发射系下SINS/GPS/CNS组合导航系统联邦粒子滤波算法

传统的扩展卡尔曼滤波(Extended Kalman filter, EKF)算法应用于未来高超、空天飞行器的组合导航系统时,因其模型线性化展开会导致模型不准确,从而引起导航精度下降;采用蒙特卡洛方法来实现递推贝叶斯估计问题的粒子滤波(Particle filter,PF)算法能有效避免引入线性化误差,具有一定的优势。据此,针对高超、空天飞行器在发射过程中通常需要直接获得发射惯性系下的高精度导航参数的需求,提高发射惯性系下弹载组合导航系统滤波算法的精确性就尤为重要,PF滤波算法无需对非线性系统进行线性化展开即可直接实现对非线性系统的状态误差估计。为此,本文将PF滤波算法引入空天飞行器SINS/GPS/CNS多信息融合组合导航系统,设计了发射系下基于联邦滤波器的PF滤波算法,实现了对组合导航系统状态参数的直接建模估计。算法仿真结果表明,相较于发射系下SINS/GPS/CNS组合导航系统联邦EKF滤波算法,PF滤波算法有效提高了组合导航系统滤波精度。     

常规闭口风洞相阵列气动声学试验

传统气动声学研究观点认为,精确的声学测量要求风洞背景噪声和洞壁反射足够低,传声器测量结果有足够高的信噪比,这是大多数风洞无法达到的要求.近些年,基于声纳和雷达技术发展起来的麦克风相阵列技术可以通过增加阵列的传声器数目从而大幅提高声学测量的信噪比,具有噪声源研究和定位能力,并被成功地应用于非声学固壁风洞噪声源测量和噪声物理机制研究.作者基于相阵列波束生成频域算法研制出常规闭口风洞相阵列系统及相关技术,在FD-09风洞尝试进行了相阵列校准试验和某民机噪声测量试验.结果表明:相阵列技术能够准确捕捉到真实的校准声源,并从技术上验证了相阵列系统在常规闭口风洞测量气动噪声是有效的.     

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional（2-D）histogram region oblique division and artificial bee colony（ABC）optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional（1-D）method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization（NCPSO）,the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.     

利用温度测量结果反演三维热传导问题中热源项的算法研究

利用表面温度测量来反演热传导问题中的热源项是一类典型的热传导逆问题,在采用有限体积法对三维稳态热传导问题进行数值求解的基础上,将该热传导逆问题转化为优化问题,基于灵敏度分析建立了反演算法.采用该算法对一典型算例的计算结果表明:建立的算法是有效的,具有较好的抗噪性能.此外,对反演算法中计算收敛准则的选取进行了较深入的分析,结果表明,由于热传导逆问题的不适定性,优化过程中目标函数值越小并不意味着反演结果与真值更为接近,可以通过设定合适的收敛准则来模拟正则化项的作用,克服不适定性的影响.     

应用于微型飞行器阵列天线的自适应波束形成器

微型飞行器的天线采用灵巧的自适应阵列天线。由于自适应阵列天线的幅相误差会引起线性约束自适应波束形成器的期望信号相消,本文根据微型飞行器自适应天线系统的设计,基于广义旁瓣对消器(G SC)结构,在频域中将阻塞矩阵赋予自适应功能,进而得到了一种改进的,可行的,稳健的自适应波束形成器。仿真试验表明,此方法对天线阵列幅相误差具有稳健性,降低了运算量,可以应用于微型飞行器二维自适应阵列天线,实现实时的波束形成。     

改进的CFD/CSD耦合系统设计方法及其计算

论述了界面上的数据交换方法和时间推进格式在流体动力学（CFD）和结构动力学（CSD）耦合求解非线性气动弹性问题中的关键作用;改进了CFD／CSD耦合系统．包括基于边界元方法设计了CFD和CSD耦合界面的数据转换方法,该方法可在同一映射矩阵处理结构响应和非定常气动载荷转换,保证了耦合边界上的能量守恒;改进了一种松耦合方法流程,在保持模块化基础上提高了计算精度和效率。最后将本文方法应用于二维位移外插算例和A—GARD445．6机翼的动响应分析中,结果表明本文方法具有较高精度。     

Loading Localization by Small-Diameter Optical Fiber Sensors

Structural health monitoring(SHM)in service has attracted increasing attention for years.Load localization on a structure is studied hereby.Two algorithms,i.e.,support vector machine(SVM)method and back propagation neural network(BPNN)algorithm,are proposed to identify the loading positions individually.The feasibility of the suggested methods is evaluated through an experimental program on a carbon fiber reinforced plastic laminate.The experimental tests involve in application of four optical fiber-based sensors for strain measurement at discrete points.The sensors are specially designed fiber Bragg grating(FBG)in small diameter.The small-diameter FBG sensors are arrayed in 2-D on the laminate surface.The testing results indicate that the loading position could be detected by the proposed method.Using SVM method,the 2-D FBG sensors can approximate the loading location with maximum error less than 14 mm.However,the maximum localization error could be limited to about 1 mm by applying the BPNN algorithm.It is mainly because the convergence conditions(mean square error)can be set in advance,while SVM cannot.     

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。