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.     

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。     

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.     

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.     

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。     

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。     

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。     

用MUSIC法确定声源频率和方位时误差的校正方法

为确定多个平面波窄带声源的频率和方位本文在阵列信号处理理论的基础上，提出用均匀线阵接收信号数据，并采用多信号分类法（ＭＵＳＩＣ法）对其进行频率，方位参数的分离估计，利用其正交性的谱估计来判断声源的频率和方位，在此基础地实际阵列中存在的阵元位置扰动，通道特性不一致和阵元之间的耦合这三种模型误差，提出了一种基于辅助源的校正方法，并得模型误差不影响声源的频率，只影响声源方位的判断，声学实验证实，通过校正     

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.     

基于FPGA的并行遗传算法硬件实现的研究

遗传算法具有天然的并行性。FPGA(Field programmable gate arrays)本质上的并行特性使其很适合用于实现并行的遗传算法。结合两者的并行特性，本文提出了一种基于FPGA的并行遗传算法。选用了适合硬件实现的选择、交叉、变异算子，并将它们设计成流水线结构。整个设计采用了XILINX公司的XC2V1000型号FPGA芯片。算法利用VHDL语言来描述。实现后的测试表明，这种硬件遗传算法有效减少了运行时间，使其在一些实时性要求较高的场合得到很好应用。     

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。     

简化的混合估计算法及其在GPS／SINS深组合中的应用

为解决GPS/SINS深组合导航系统滤波的非线性和噪声的不确定性的问题,针对深组合模型特点,设计了一种简化的基于U滤波的多模型混合估计滤波器。根据系统模型中状态方程是线性方程、观测方程是非线性方程的特点,提出了一种简化的U滤波算法(Ultra tight coupling unscented Kalman filter,UTCUKF),然后针对噪声变化建立了非线性模型,多模型混合估计滤波器的输出为各滤波器的概率加权融合,因此模型概率是根据噪声变化而调整的,从而也使系统输出对噪声变化具有一定自适应能力。最后进行了仿真,并与基于普通U滤波的多模型混合估计算法进行了比较。结果表明,本文算法的解算时间短,模型切换速度更快,而估计的精确度与同条件下的基于普通U滤波的多模型混合估计算法相当,更符合深组合系统高动态的要求。     

CFD问题显式有限差分方法的并行化

基于ＭＩＭＤ并行计算机模型，本文讨论了ＣＦＤ问题应用显式有限差分方法的并行化问题。利用区域分解法将计算问题分解为多个子问题，每个子问题由不同的处理器分别处理。针对ＳＣＢ计算格式的特点，尽量降低各处理器间的数据通讯，提高了并行计算效率。最后给出二维Ｅｕｌｅｒ方程组计算实例，计算结果令人满意，且算法有较好的可扩放性。     

A Status Graph Based Control Allocation Algorithm of Digital Micro-Thruster Array for Micro/Nano-Satellites Orbit Control Application

Digital micro-thruster arrays can be used for special missions of micro/nano-satellites with the requirements of high precision and small impulse. This paper presents a novel control allocation algorithm for the digital micro-thruster array,namely status graph based control allocation(SGBCA)algorithm,which aims at finding the optimal micro thrusters combination scheme to realize the sequential control synthesis for micro/nano-satellite during real-time orbit control tasks. A mathematical model is set up for the control allocation of this multivariate over-actuated system.Through dividing thrusters into disjoint segments by offline calculation and combining segments dynamically online to provide a sequence of the required impulse for the micro/nano-satellite,the time complexity of the control allocation algorithm decreases significantly. All levels of impulse can be generated by the digital micro thruster arrays and the service life of the arrays can be extended using the segment converting strategy proposed in this paper. The simulation indicates that the algorithm can satisfy the requirements of real-time orbit control for micro/nano-satellites.     

高精度ENO格式的有效实现

基本无振荡（ＥＮＯ）有限差分格式是近几年出现的一类高精度差分格式，重构多项式的形成是ＥＮＯ格式构造的核心部分，一般均以网格平均值作为插值点。文中由通量值作为插值点，构造了一个简单的高精度格式，在求解高维欧拉方程时，较节省计算时间。引入三 阶ＴＶＤＲｎｇｅＫｕｔｔａ时间离散，就二维欧拉方程的激波反射与双马赫反射问题进行了数值试验，试验结果表明格式所具有的高精度和基本无振荡性质，能够满足实际计算的需要     

Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm

Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system. By studying the defects of the former Kalman filter based estimation method, a new estimating method is proposed. First the nonlinear vehicle dynamics system, containing inaccurate model pa rameters and constant noise, is established. Then a dual unscented particle filter （DUPF） algorithm is proposed. In the algorithm two unscented particle filters run in parallel, states estimation and parameters estimation update each other. The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter （UKF） and dual extended Kalman filter （DEKF）, and it also has good capability to revise model parameters.     

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。     

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