回溯降维相干分布式非圆信号DOA快速估计

在相干分布式非圆(CDNC)信号波达方向(DOA)估计中,针对阵列输出矩阵扩展后维数增加带来的较大运算量问题,基于降维的多级维纳滤波(MSWF)技术,引入回溯优化思想,提出了一种快速估计算法。该算法首先利用信号非圆特性扩展阵列输出矩阵,然后通过MSWF递推分解快速求出信号子空间,避免了计算阵列协方差矩阵及特征分解,并且在递推过程中引入回溯优化机制提高了各级匹配滤波器的估计性能,最后由最小二乘(LS)或者总体最小二乘(TLS)得到DOA估计。仿真分析表明,所提算法与相干分布式非圆信号旋转不变子空间算法(CDNC-ESPRIT)性能相当,但复杂度得到了大幅度降低,相比于基于MSWF的非圆信号快速子空间(NC-MSWF-FS)算法,在较小的复杂度代价下大幅度提升了低信噪比时的估计性能,并且对初始参考信号的选取具有了较强的鲁棒性。     

基于3DT的空时自适应单脉冲参数估计算法

空时自适应处理(STAP)是机载预警雷达抑制杂波和干扰的一项关键技术,而多普勒三通道联合自适应处理(3DT)是适合工程实现的降维(RD)STAP方法。STAP目标检测后还需进一步估计目标的角度参数,因此将自适应单脉冲(AM)技术引入3DT,提出了一种高精度联合估计目标速度与方位空间角的空时自适应单脉冲算法。理论分析与仿真实验结果表明,当目标多普勒频率偏离检测多普勒单元中心频率时,该算法能同时减少目标多普勒跨越损失和空时导引矢量失配损失,进而提高输出信杂噪比(SCNR),改善目标测角精度。     

一种联合估计的有源校正高精度测向算法

针对存在系统误差的阵列模型,提出了一种有源标校下的联合估计测向算法。该算法把误差矩阵估计转化为误差系数估计,并采用到达角精确已知的源信号进行标校,在此基础上使用最小二乘法联合估计幅相不一致误差系数和互耦误差系数,最后使用结合误差矩阵的MUSIC算法测量信号的到达角。仿真表明,该算法仅需要3个标校源,其精度相比于无阵列误差情况下降0.05°,具有较好的工程可实现性。     

非圆实信号MIMO雷达中基于实值ESPRIT的角度估计

研究单基地多输入多输出(MIMO)雷达中的波达角(DOA)估计问题,提出了一种基于非圆(NC)实信号的实值旋转不变性信号参数估计(ESPRIT)算法.首先对接收信号进行降维变换,降低运算复杂度;之后根据非圆实信号特性构造中心Hermitian对称矩阵,通过酉(Unitary)变换将复数运算转为实数,进一步降低复杂度;最后根据ESPRIT得到角度估计.该算法无需谱峰搜索,运算复杂度较NC ESPRIT和Unitary ESPRIT大大降低,且该算法的角度估计性能优于后两种算法.论文分析了所提算法的复杂度,并推导了克拉美-罗界(CRB).仿真结果验证了该算法的有效性.     

非理想均匀圆阵的波达方向和多普勒频率联合估计

针对均匀圆阵存在一般阵列误差 (如阵元的幅相误差和安装位置误差等 )的情况 ,提出了多个信号的波达方向和多普勒频率估计方法。直接利用均匀圆阵的阵列流形 ,采用波达矩阵法估计各个信号的多普勒频率。由一般阵列误差的统计特性构造加权矩阵 ,采用加权总体最小二乘法估计各个信号的波达方向。此方法具有鲁棒性强等特点。计算机仿真证明了此方法的有效性     

Estimation of DOA and Doppler Frequency on Nonideal UCA

针对均匀圆阵存在一般阵列误差(如阵元的幅相误差和安装位置误差等)的情况，提出了多个信号的波达方向和多普勒频率估计方法。直接利用均匀圆阵的阵列流形，采用波达矩阵法估计各个信号的多普勒频率。由一般阵列误差的统计特性构造加权矩阵，采用加权总体最小二乘法估计各个信号的波达方向。此方法具有鲁棒性强等特点。计算机仿真证明了此方法的有效性。     

MIMO雷达DOD和DOA联合估计算法: RTR-ESPRIT

 针对双基地多输入多输出(MIMO)雷达多目标波离角(DOD)和波达角(DOA)的联合估计问题,提出一种接收-发射-接收(RTR)-ESPRIT算法。该算法首先利用一维接收ESPRIT(R-ESPRIT)预估计目标DOA,随后分别利用一维发射ESPRIT(T-ESPRIT)和一维接收ESPRIT得到目标的高精度DOD和DOA估计,在每两次ESPRIT算法之间分别构造正交投影算子对接收信号进行接收波束形成和发射波束形成。与传统ESPRIT算法相比,该算法大大降低了数据协方差矩阵维数和计算复杂度,无需额外的配对算法,且理论证明了该方法还可以用于相干目标和单快拍情况下DOD和DOA的联合估计。仿真结果表明了该算法的正确性及良好的估计性能。     

基于LS-SVM的浮选药剂量优化设定

针对矿物浮选在线检测X荧光分析仪缺失、人工检测严重滞后的问题,提出基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的浮选药剂量优化设定方法。该方法首先利用历史数据建立基于LS-SVM的药剂量优化模型,然后采用该模型实现浮选药剂量的优化设定。工业数据仿真结果表明,所提方法能够实现浮选生产过程的的指标要求。     

无需中间变量的多运动站时差定位新算法

传统时差(TDOA)定位模型通过引入中间变量来得到线性方程,需要两步求解过程且该模型不适合多运动站连续定位。为此,引入无需中间变量的时差定位模型,并在此基础上提出了一种约束加权最小二乘定位算法。首先将基于该模型的时差定位问题转换为加权最小二乘问题,然后推导代入时差测量值后观测矩阵和观测向量的误差项,将其每一列表示为确定矩阵与随机时差测量噪声向量乘积的形式,并基于此推导了关于目标状态的二次约束方程,最终只需通过广义特征值分解来得到目标状态估计,并推导了该估计的解析表达式。仿真结果表明所提算法的连续定位性能逼近克拉美罗-限且所得定位解渐近无偏。     

鲁棒EKF在脉冲星导航系统中的应用

针对脉冲星导航系统的滤波问题,传统的扩展卡尔曼滤波(EKF)算法存在不能克服系统模型存在不确定性参数以及乘性噪声等缺陷,提出一种鲁棒EKF算法。首先,分析了状态预测误差方程和估计误差方程,利用统计学原理,得到了状态预测方差矩阵和状态估计方差矩阵计算等式。由于系统模型存在不确定性参数,状态预测协方差矩阵和状态估计协方差矩阵无法计算;因此,利用4个重要矩阵不等式,分析并找到预测方差矩阵和状态估计方差矩阵的上界。最后,利用状态估计误差协方差矩阵上界设计状态增益矩阵,使得状态估计协方差矩阵的迹最小。将该算法对脉冲星导航系统进行仿真,仿真结果验证了所提算法的有效性。     

An LS-TLS-based sequential identification algorithm: Application toan accelerometer

Total least squares (TLS) parameter estimation is an alternative to least squares (LS) estimation when there are errors in both data matrix and observation vector. Especially, when some of the columns, not all, of the data matrix A are free of error, we call it a mixed LS-TLS problem. Accordingly, a sequential algorithm for solving a mixed LS-TLS problem is proposed here. The proposed algorithm employs an efficient algorithm to locate the minimum eigenpair, instead of singular value decomposition (SVD) which is computationally exacting. The proposed algorithm is applied to an accelerometer model to identify error parameters which are very important in inertial navigation systems (INS)     

A novel multistage estimation of signal parameters

A multistage estimation scheme is presented for estimating the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc. Such a situation arises, for example, in the case of the Global Positioning Systems (GPS). In the proposed scheme, the first-stage estimator operates as a coarse estimator of the frequency and its derivatives, resulting in higher RMS estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency (an event termed cycle slip). The second stage of the estimator operates on the error signal available from the first stage, refining the overall estimates, and in the process also reduces the number of cycle slips. The first-stage algorithm is a modified least-squares algorithm operating on the differential signal model and referred to as differential least squares (DLS). The second-stage algorithm is an extended Kalman filter, which yields the estimate of the phase as well as refining the frequency estimate. A major advantage of the proposed algorithm is a reduction in the threshold for the received carrier power-to-noise power spectral density ratio (CNR) as compared with the threshold achievable by either of the algorithms alone     

A Linear-correction Least-squares Approach for Geolocation Using FDOA Measurements Only

  A linear-correction least-squares(LCLS) estimation procedure is proposed for geolocation using frequency difference of arrival (FDOA) measurements only. We first analyze the measurements of FDOA, and further derive the Cram閞-Rao lower bound (CRLB) of geolocation using FDOA measurements. For the localization model is a nonlinear least squares(LS) estimator with a nonlinear constrained, a linearizing method is used to convert the model to a linear least squares estimator with a nonlinear constrained. The Gauss-Newton iteration method is developed to conquer the source localization problem. From the analysis of solving Lagrange multiplier, the algorithm is a generalization of linear-correction least squares estimation procedure under the condition of geolocation using FDOA measurements only. The algorithm is compared with common least squares estimation. Comparisons of their estimation accuracy and the CRLB are made, and the proposed method attains the CRLB. Simulation results are included to corroborate the theoretical development.     

基于左矩阵分式模型的模态参数识别方法

提出一种多输入多输出(MIMO)系统的宽频模态参数识别算法。该方法基于频响函数(FRF)的左矩阵分式模型(LMFD),通过最小二乘法在z域内求解模态参数,避免了s域内矩阵的病态问题。针对左矩阵分式模型的特点,给出了一种通过主分量分析(PCA)建立稳定图的方法。指出了传统的频域多参考点(PRFD)方法与基于左矩阵分式模型识别方法之间的关系。最后采用GARTEUR模型仿真算例与飞机模型的实测算例对所提出的方法进行了验证,结果表明该方法具有良好的识别效果。     

MIMO雷达中一种降维MUSIC的相干角度估计算法

研究单快拍下双基地多输入多输出（Multiple—InputMultiple-Output,MIMO）雷达中相干信源的离开角（Directionofdeparture,DOD）与到达角（directionofarrival,DOA）联合估计问题。利用单快拍下双基地MIMO雷达的接收信号构造一组Toeplitz矩阵,利用这组ToepIitz矩阵重构一个信号矩阵,提出一种基于降维多重信号分类（ReducedDimensionMultipleSignalClassification,RD-MUSIC）的DOD与DOA联合估计算法。提出的算法能够有效估计相干信源以及非相干信源的角度,实现角度的自动配对,并且角度估计性能远优于FBSS—ESPRIT算法以及ESPRIT-like算法。仿真结果验证了算法的有效性。     

Estimation of singularities for intercept point forecasting

In this paper, a recursive approach (an algorithm for estimation of singularity (AES)) is proposed to forecast the intercept point of a target and the pursuing interceptor recognized as the estimated singularity of a nodal cubic curve fitted to the data. The data comprises direction of arrival (DOA) estimates of both target and interceptor obtained at regular intervals of time using the maximum likelihood (ML) DOA estimation method. The estimates of coefficients of the cubic polynomial are given by a recursive least squares solution. From these coefficients, closed-form solutions for angle of interception and intercept time are obtained which are the forecasted coordinates of the intercept point. Experimental results demonstrate performance of the proposed algorithm     

Angle-Independent Doppler Velocity Measurement

When the angle between the target heading and the range vector is not known a priori, a Doppler velocity radar must estimate them simultaneously by utilizing a longer section of the track. The conventional least squares iterative solution is compared with a new explicit alogorithm which utilizes range-rate derivatives. It is shown that the explicit algorithm is biased but is less sensitive to noise. The bias, however, can be estimated and removed. Hence, the computationally simpler explicit velocity estimation method yields better performance. An analytical closed form expression for the resulting mean square estimation error and simulation results are given.     

一种基于多普勒频率的恒模信号直接定位方法

相比于传统的差分多普勒(DD)两步定位方法,以Amar和Weiss提出的基于多普勒频率的单步直接定位方法在低信噪比和小样本条件下具有更高的定位精度。在该类新型定位体制的基础上,提出了一种基于多普勒频率的恒模信号直接定位方法。首先,依据最大似然(ML)准则以及恒模信号的恒包络特征,建立相应的直接定位优化模型。然后,根据目标函数的代数特征将全部未知参量分成两组,并提出一种有效的多参量交替迭代算法,用以获得该优化问题的最优数值解。新算法包含了针对这两组未知参量的Newton型迭代公式,用以避免网格搜索,并能实现多维参数的"解耦合"估计。最后,推导出针对恒模信号的目标位置直接估计方差的克拉美罗界(CRB)。数值实验验证了新方法的优越性。