复合制导空空导弹截获概率模型仿真

针对复合制导空空导弹截获目标时导引头预置天线角度与导弹允许截获不在同一时刻这一问题,建立了一种新的截获概率估算模型。根据中末制导交接班时导弹截获目标的算法流程,确立了导弹截获目标的条件,分析了影响导弹截获概率的主要误差源,建立了各误差数学模型,并通过等效转化法将弹目指示位置向量在三维空间的散布转化为弹目实际相对位置向量在三维空间的散布,完成截获概率估算模型的建立。该模型利用已知的误差先验估计及导弹飞行过程中的测量信息,可在一次弹道计算中解算导弹截获概率。利用此模型采用控制变量法分析了各误差因素与截获概率的关系,仿真结果表明：目标截获概率对数据链周期和测角精度敏感性最高。     

基于随机加权估计的Sage自适应滤波及其在导航中的应用

为了克服Kalman滤波和Sage自适应滤波的缺点,在分析基于新息向量、残差向量和状态改正数向量的自适应协方差估计存在问题的基础上,提出根据新息向量、残差向量和状态改正数对滤波精度影响的不同程度,采用随机加权法对新息向量、残差向量和状态改正数进行估计,以得到观测噪声协方差矩阵和系统动态噪声协方差矩阵.进一步,利用随机加权法对观测噪声协方差阵和系统噪声协方差阵进行估计,以提高动态导航定位的滤波解算精度.研究结果表明,基于随机加权估计的Sage自适应滤波效果明显优于基于算术平均值估计的滤波方法.     

组网雷达测量色噪声数据的Hermite插值融合方法

利用组网雷达测量的色噪声数据融合处理出飞行目标坐标与速度,并估计出各雷达测量误差的有关参数。先根据雷达测量原理建立跟踪数据的色噪声误差模型,利用多雷达观测的色噪声数据序列初步估计目标的坐标与速度初值,再利用Hermite插值法估计目标坐标与雷达测量坐标的偏差,不断改进Hermite插值多项式的系数以及误差模型中色噪声修正系数,提高了目标坐标速度等参数的处理精度。仿真数据实验表明该计算方法稳定可靠,定位精度较高,可以发挥Hermite插值法在色噪声测量数据融合处理中的作用。     

基于约束总体最小二乘的单站DOA/TDOA联合误差校正与定位算法

多基外辐射源雷达定位系统受系统偏差影响较大。该文针对多基外辐射源雷达到达角度(DOA)和到达时差(TDOA)联合定位系统,提出一种基于约束总体最小二乘(CTLS)的无源定位和误差校正算法。首先引入辅助变量,将DOA和TDOA非线性观测方程进行线性化处理。考虑伪线性化后定位方程中噪声矩阵各分量统计相关特性,将无源定位与误差校正联合优化问题建立为CTLS模型,并采用牛顿迭代方法对模型求解。在此基础上,考虑辅助变量与目标位置的关联性,设计关联最小二乘算法改进目标位置估计值,采用后验迭代方法进一步提高系统偏差估计精度。最后推导了算法的理论误差。仿真结果表明:该算法能够有效地估计目标位置和系统偏差。     

扩展卡尔曼滤波在目标跟踪中的应用研究

扩展卡尔曼滤波在非平稳矢量信号和噪声环境下具有广泛的应用,针对机动目标运动模型的特点,采用基于扩展卡尔曼滤波的算法对运动目标进行跟踪处理,该算法首先建立了运动目标的状态模型和观测模型,然后对观测数据进行滤波和误差估计处理,最后通过计算机的蒙特卡洛仿真得到了滤波轨迹和运动目标的距离和角度误差,仿真结果表明,扩展卡尔曼滤波算法具有很好的目标跟踪性能.     

一种基于最小化广义Rayleigh商的无源定位算法研究

 为改善只测角无源定位的性能,提出了一种基于最小化广义Rayleigh商的无源定位算法.该算法利用扰动观测矩阵和扰动观测向量的乘性结构,将约束总体最小二乘问题转化为最小化广义Rayleigh商问题,从而只需对一对矩阵束进行广义特征值分解即可求得全局最优定位解.仿真结果表明所提算法性能稳健且计算量较小,定位收敛精度逼近克拉美罗限(CRLB),远优于最小二乘(LS)算法和总体最小二乘(TLS)算法,实用性强.     

基于变分贝叶斯双尺度自适应时变噪声容积卡尔曼滤波的同步定位与建图算法

为解决移动机器人在同步定位与建图(SLAM)中因系统噪声和观测噪声时变导致状态估计精度降低的问题,该文提出一种基于变分贝叶斯的双尺度自适应时变噪声容积卡尔曼滤波SLAM算法(DSACKF SLAM)。该算法采用逆Wishart分布对一步预测误差协方差矩阵P_k|k-1和观测噪声协方差矩阵R_k建模,分别用来降低系统噪声和观测噪声的影响,并利用变分贝叶斯滤波实现对移动机器人状态向量X_k,P_k|k-1和R_k的联合估计。分别在系统噪声和观测噪声时变和时不变的条件下进行仿真实验,结果表明与基于无迹卡尔曼滤波的SLAM算法(UKF SLAM)、自适应更新观测噪声的容积卡尔曼滤波的SLAM算法(VB-ACKF SLAM)相比,所提DSACKF SLAM算法在噪声时变时,平均位置误差分别减小1.54 m, 3.47 m;噪声时不变时,平均位置误差分别减小0.62 m,1.41 m,证明DSACKF SLAM算法有更好的估计性能。     

空间扩展目标对准误差图像的高分辨复原

大气湍流、光子噪声和光学跟踪系统对准误差严重降低了空间目标观测图像的分辨率.根据最大似然估计原理,建立了提高目标图像分辨率的多帧盲反卷积算法,用共轭梯度优化方法从目标记录图像估计出原始目标函数和点扩散函数.运用低通平滑滤波技术在算法迭代过程中逐步完成对噪声的抑制.模拟实验数据和实际图像的复原结果表明,论文建立的盲反卷积算法有效地克服了大气湍流、光子噪声和光学系统对准误差,提高了目标图像的分辨率,复原目标图像的分辨率达到了光学衍射极限的水平.     

基于加权最小二乘支持向量机的欠定盲源分离

为了进一步提高欠定盲源分离算法中混合矩阵估计方法的性能,提出了一种基于加权最小二乘支持向量机（SVM）的欠定盲源分离混合矩阵估计方法。该方法利用信号的方向角度特征估计出有效信源信号个数,然后采用加权最小二乘支持向量机方法获得初始权值,每次将其中一个权值对应的样本点作为测试样本,其余点作为训练样本,依次对样本的误差变量进行更新,再根据权值计算公式实现所有权值的更新,进而确定最优分类平面,实现对观测信号的最优分类,最终估计出混合矩阵。实验结果表明,新算法是有效的,其平均误差是基于K-均值方法误差的0.2倍左右,是基于SVM算法平均误差的0.5倍左右。     

增益相位误差下基于部分校准嵌套阵列的DOA估计

现有的波达方向（Direction of Arrival,DOA）估计方法大多依赖于阵列导向矩阵的精确无偏条件,而实际工程中由于时钟偏移、阵元位置偏差的存在导致该条件往往难以满足.为匹配阵列实际接收条件,本文基于部分校准嵌套阵列,提出了一种增益相位误差下的DOA估计新方法.该方法首先利用连乘子函数和简单的代数运算完成初始增益相位误差估计,然后通过协方差矩阵向量化和稀疏表示理论构建具有连续自由度的稀疏表示向量模型,最后考虑有效样本的影响,在初始增益相位误差估计的基础上应用稀疏总体最小均方（Sparse total least squares,STLS）算法完成波达方向估计.本文所提方法不仅对阵列增益相位误差不敏感,而且可依靠嵌套阵列高自由度特性和STLS算法的抗扰动特性获得改进的分辨率和估计精度,计算机仿真结果验证了所提算法的有效性.     

Deconvolution and total least squares in finding the impulseresponse of an electromagnetic system from measured data

Total least squares (TLS) is a method of solving over-determined sets of linear equations AX≈b when there are errors both in the observation vector b(m×1) and in the data matrix A(m×n). This method is particularly useful when the data matrix A is singular or highly ill conditioned. We present the method of finding the TLS by applying the singular value decomposition to the discrete deconvolution problem. Numerical results are presented for finding the impulse response of a transmission line from experimental data. The advantage of this approach is that this method can be automated based on the signal to noise ratio of the measured data     

基于总体最小二乘算法的多站无源定位

将总体最小二乘算法应用于多站无源定位中,分别提出了基于角度估计的总体最小二乘算法,基于时差估计的总体最小二乘算法以及基于角度和时差估计的总体最小二乘算法。算法首先把非线性的观测方程转化为伪线性的观测方程,然后构造增广矩阵,并对该矩阵进行奇异值分解即可估计出目标位置,因此无需迭代计算或者获得目标位置的粗略估计,仿真结果表明该算法具有较高的定位精度。     

A unified approach to three eigendecomposition methods forfrequency estimation

The authors present a unified approach to three eigendecomposition-based methods for frequency estimation in the presence of noise. These are the Tufts-Kumaresan (TK) method, the minimum-norm (MN) method, and the total least squares (TLS) method. It is shown that: (1) the MN method is a modified version of the TK method; (2) the TLS method is a generalization of the MN method; (3) the TLS solution vector can be expressed in matrix form, and an alternative way of computing it is presented; (4) the MN and the TLS methods exhibit some improvement over the TK method     

Constrained maximum likelihood solution of linear equations

The total least squares (TLS) is used to solve a set of inconsistent linear equations Ax≈y when there are errors not only in the observations y but in the modeling matrix A as well. The TLS seeks the least squares perturbation of both y and A that leads to a consistent set of equations. When y and A have a defined structure, we usually want the perturbations to also have this structure. Unfortunately, standard TLS does not generally preserve the perturbation structure, so other methods are required. We examine this problem using a probabilistic framework and derive an approach to determining the most probable set of perturbations, given an a priori perturbation probability density function. While our approach is applicable to both Gaussian and non-Gaussian distributions, we show in the uncorrelated Gaussian case that our method is equivalent to several existing methods. Our approach is therefore more general and can be applied to a wider variety of signal processing problems     

Low Complexity DFT Based DOA Estimation for Synthetic Nested Array Using Single Moving Sensor

The issue of direction of arrival (DOA) estimation for synthetic nested array is investigated in this paper. The synthetic nested array (SNA) is formed by one single sensor moving according to the configuration of the physical nested array. With the synthetic array, both high resolution DOA estimation and array aperture miniaturization requirements can be met. To reduce the computationally complexity for SNA, a discrete Fourier transform (DFT) based algorithm is proposed which needs no eigen decomposition. We first reconstruct the data matrix reshaped from the data received by moving senor to obtain the observation vector and then get the initial DOA estimates via DFT of the observation vector. At last the fine estimates can be obtained through searching for peaks corrected by phase rotation matrix over a small sector. The proposed algorithm for SNA can achieve better bearing estimation performance than spatial smoothing (SS) subspace based methods such as SS-MUSIC and SS-ESPRIT, due to the fact that it can fully utilize array aperture while SS-MUSIC and SS-ESPRIT lose a half. Besides, the proposed algorithm involves full degree of freedoms (DOF). Numerical simulations validate the efficiency and superiority of the proposed algorithm.     

An efficient recursive total least squares algorithm for FIRadaptive filtering

An algorithm for recursively computing the total least squares (TLS) solution to the adaptive filtering problem is described. This algorithm requires O(N) multiplications per iteration to effectively track the N-dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix. It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise. The TLS solution on the other hand, is seen to produce unbiased solutions. Examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited. Computer simulations comparing the relative performance of RLS and recursive TLS are described     

Consistent normalized least mean square filtering with noisy data matrix

When the ordinary least squares method is applied to the parameter estimation problem with noisy data matrix, it is well-known that the estimates turn out to be biased. While this bias term can be somewhat reduced by the use of models of higher order, or by requiring a high signal-to-noise ratio (SNR), it can never be completely removed. Consistent estimates can be obtained by means of the instrumental variable method (IVM),or the total/data least squares method (TLS/DLS). In the adaptive setting for the such problem, a variety of least-mean-squares (LMS)-type algorithms have been researched rather than their recursive versions of IVM or TLS/DLS that cost considerable computations. Motivated by these observations, we propose a consistent LMS-type algorithm for the data least square estimation problem. This novel approach is based on the geometry of the mean squared error (MSE) function, rendering the step-size normalization and the heuristic filtered estimation of the noise variance, respectively, for fast convergence and robustness to stochastic noise. Monte Carlo simulations of a zero-forcing adaptive finite-impulse-response (FIR) channel equalizer demonstrate the efficacy of our algorithm.     

改进K-means算法对AltBOC信号组合码序列的盲估计

针对恒包络交替二进制偏移载波(Alternate Binary Offset Carrier, AltBOC)调制信号组合码序列难以估计的问题，提出了利用改进K-means算法进行信号组合码盲估计方法。该方法首先通过引入互调分量以及重建副载波的方式构建AltBOC信号模型，然后在接收端将AltBOC信号分段成单倍组合码周期窗长的不重叠观测数据矩阵，并利用相似性原理从观测数据中选择最优的样本作为K-means聚类中的初始均值向量，最后通过K-means算法迭代优化数据样本与其聚类均值向量的平方误差，完成对AltBOC信号组合码序列的盲估计。计算机仿真结果表明，利用该算法在信噪比-15dB下能够较为精确地估计AltBOC信号组合码序列。      

Robust Least Squares Constant Modulus Algorithm to Signal Steering Vector Mismatches

The linearly constrained least squares constant modulus algorithm (LSCMA) may suffer significant performance degradation and lack robustness in the presence of the slight mismatches between the actual and assumed signal steering vectors, which can cause the serious problem of desired signal cancellation. To account for the mismatches, we propose a doubly constrained robust LSCMA based on explicit modeling of uncertainty in the desired signal array response and data covariance matrix, which provides robustness against pointing errors and random perturbations in detector parameters. Our algorithm optimizes the worst-case performance by minimizing the output SINR while maintaining a distortionless response for the worst-case signal steering vector. The weight vector can be optimized by the partial Taylor-series expansion and Lagrange multiplier method, and the optimal value of the Lagrange multiplier is iteratively derived based on the known level of uncertainty in the signal DOA. The proposed implementation based on iterative minimization eliminates the covariance matrix inversion estimation at a comparable cost with that of the existing LSCMA. We present a theoretical analysis of our proposed algorithm in terms of convergence, SINR performance, array beampattern gain, and complexity cost in the presence of random steering vector mismatches. In contrast to the linearly constrained LSCMA, the proposed algorithm provides excellent robustness against the signal steering vector mismatches, yields improved signal capture performance, has superior performance on SINR improvement, and enhances the array system performance under random perturbations in sensor parameters. The on-line implementation and significant SINR enhancement support the practicability of the proposed algorithm. The numerical experiments have been carried out to demonstrate the superiority of the proposed algorithm on beampattern control and output SINR enhancement compared with linearly constrained LSCMA.