宽带信号近似最大似然方位估计快速算法

 针对短采样宽带信号近似最大似然方位估计（AML）计算量大的问题,将马尔可夫蒙特卡罗(MCMC)方法与近似最大似然方位估计相结合,提出一种基于完美抽样的近似最大似然方位估计快速算法（PAML）。该算法将AML算法的空间谱函数作为信号的概率分布函数,并利用完美抽样方法从该概率分布函数中抽样。与AML和遗传算法的对比实验研究表明,两目标情况下PAML算法在中低信噪比条件下的估计性能与AML和遗传算法性能相当,而计算量分别是二者的1/24和1/3。随着目标个数的增加,PAML算法的计算量优势将更加明显。     

An Adaptive Estimation Algorithm for Time-Varying Bit Synchronizers

This paper presents an adaptive estimation algorithm for bit synchronization, assuming that the parameters of the incoming data process are time varying. The data are linearly related to the state variable to be determined. By employing the Gauss-Markov theorem, the optimal recursive estimate (including the nonstationary property of the system), which is a matrix operation on the transformed data, is obtained. The efficiency of the estimator is measured by the amount of data required and the speed of convergence. Both of these have proved to be of satisfactory and acceptable quality.     

非高斯杂波下自适应雷达目标检测新方法

在球不变随机向量(SIRV)非高斯杂波背景下,研究了多脉冲相参雷达目标的自适应检测问题。假设杂波具有相同的协方差矩阵结构和可能相关的纹理分量,提出了新的协方差矩阵估计器,并获得了相应的自适应归一化匹配滤波器(ANMF)。理论分析表明,在估计杂波分组大小与实际情况匹配时,所获得的ANMF对杂波功率水平和协方差矩阵结构均具有恒虚警率(CFAR)特性。仿真结果表明:当估计的杂波分组大小失配时,所获得的ANMF具有近似CFAR特性,并进一步分析了不同参数变化对所提检测器性能的影响。与已有的ANMF相比,所获得的ANMF具有更好的检测性能,且迭代次数更小,其相对于已知杂波协方差矩阵的最优归一化匹配滤波器(NMF)的检测损失也更小,具有很好的实际应用前景。     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

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     

Sequential ridge regression

A sequential algorithm which closely approximates ridge regression is introduced, and it is pointed out that the desired sequential ridge estimator can be obtained by properly choosing the free parameters of a startup technique for ordinary sequential least squares estimation. The derivation of this result is trivial; however, since no matrix inversions are needed for its implementation, this algorithm is of practical importance     

无需辅助数据的分布式目标自适应检测器

在非高斯背景和没有辅助数据的条件下,研究了高分辨率雷达分布式目标的自适应检测问题.首先采用有序检测理论和协方差矩阵的迭代估计方法粗略估计散射点集合,进一步利用迭代估计方法获得协方差矩阵的近似最大似然估计,提出了无需辅助数据的自适应检测器(ADWSD).ADWSD在非高斯背景下具有近似恒虚警率特性,且检测性能远好于修正的...     

AHP中判断矩阵元素最优估计值的误差分析

在AHP模型中，请一组专家对某一属性进行评价，通过适当的数学处理方法，可得判断矩阵元素的最优估计值，并根据残差来确定所求估计值是否达到精度要求，若不符合精度要求则需返还给专家进行重新评估。这是AHP中构造判断矩阵的一种新方法。     

Bias and Standard Deviation of the Estimator for the Underlying Probability in a Sequence of Bernoulli Trials with Preselected Number of Successes

In a sequence of Bernoulli trials which stops after a preselected number M of successes, the maximum likelihood estimator of the underlying probability is given by the ratio of M and the required number of observations. Closed-form expressions for the bias and standard deviation of this estimator are developed.     

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。     

Reducing geometric dilution of precision using ridge regression

A signal processing technique is proposed for improving position-fix navigation system accuracy performance when the geometry of the navigation landmarks (e.g. sensors) are nearly collinear. In the navigation literature, the accuracy degradation associated with a nearly collinear measure geometry is termed the geometric dilution of precision (GDOP). Its presence causes not only the variance of the position estimates to be highly inflated but also any bias terms which may be present in the model. Since a nearly collinear predictor matrix is mathematically equivalent to GDOP, it is proposed to use the ridge regression technique in a navigation signal processor. A position-fix algorithm based on ridge regression reduces the bias and variance inflation caused by GDOP and the overall mean-squared position error as well. Ridge regression contains the GDOP-sensitive least-mean-square (LMS) estimator as a special case. Even with a matched model, GDOP can inflate the mean-square error (MSE) of the ordinary least-squares estimator, whereas the ridge regression technique chooses a suitable biased estimator that will reduce the MSE, which is the main goal. The ridge concept is extended to include GDOP-amplified bias errors. A simple range/range navigation system is analyzed to illustrate the underlying principles of ridge regression     

Analysis of a signal starting time estimator based on the Page test statistic

Many signal processing applications require the detection of an abrupt change with subsequent estimation of the actual time of occurrence of the change. The time of the most recent reset to zero of the Page test statistic is proposed for this purpose. The probability mass function of the estimator is determined analytically subject to a quantization of the Page test statistic update. Closed-form results for the first three uncorrected moments of the estimator are presented. The analytical results are verified by comparison to simulation results and the fineness of the quantization required for accurate representation is investigated by evaluation of the Kolmogorov-Smirnov statistic. The bias, standard deviation, and skewness of the estimator as a function of the signal strength and detector threshold are evaluated for Gaussian shift-in-mean and noncentral chi-squared changes in signal type.     

EM-ML algorithm for track initialization using possibly noninformative data

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an Fl Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the EVIM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.     

Angle of arrival estimation of coherent signals using an arraydoublet in motion

Shown here is how the estimation of signal parameters via relational invariance techniques (ESPRIT) algorithm may be used with a single pair of antennas in motion to estimate angles of arrival (AOA) for coherent signals. The approach exploits the Doppler frequency shifts caused by the doubler in motion. With this estimator, the number of signals that can be handled is not limited by the size of the array, as in the usual ESPRIT application, but by an adjustable parameter. A theoretical performance analysis of the estimator and typical examples showing the use of this estimator are given     

Efficient recursive state estimator for dynamic systems withoutknowledge of noise covariances

An efficient recursive state estimator for dynamic systems without knowledge of noise covariances is suggested. The basic idea for this estimator is to incorporate the dynamic matrix and the forgetting factor into the least squares (LS) method to remedy the lack of knowledge of noises. We call it the extended forgetting factor recursive least squares (EFRLS) estimator. This estimator is shown to have similar asymptotic properties to a completely specified Kalman filter state estimator. More importantly, the performance of EFRLS greatly exceeds that of existing filtering techniques when the noise variance is misspecified. In addition, EFRLS also performs well when there is cross-correlation between the process and measurement noise streams or temporal dependencies within those streams. Some discussions and a number of simulations are made to provide practical guidance on the choice of an optimal forgetting factor and evaluate the performance of the EFRLS algorithms, which strongly dominates that of the standard forgetting factor recursive least squares (FRLS) and some misspecified Kalman filtering     

A Modified Discrete Karhunen-Loeve Random Field Estimator

Based upon the modified Karhunen-Loeve model of the gravity disturbance vector proposed by us recently, a corresponding Karhunen-Loeve random field estimator is developed in this paper for a two-dimensional grid of gravity data in a local finite region.The new eigenvectors obey the "required" orthogonality relations on the chosen grid provided the KL expansion is now separated into odd and even integers. The closed-form solution of the estimator isthen obtained under the "diagonal" assumption for the gain coefficients. It is shown that this assumption, without which nosolution is possible, enables the estimator to reproduce the data atgrid points when the noise in the data is absent and the number of terms in the Karhunen-Loeve expansion are equal to the number of grid points.     

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

Ｓｉｎｃｅｐｈａｓｅｄａｒａｙｒａｄａｒｃａｎａｌｏｃａｔｅｔｈｅｒａｄａｒｒｅｓｏｕｒｃｅｓｆｌｅｘｉｂｌｙ，ｉｔｈａｓｔｈｅｐｏｔｅｎｔｉａｌｔｏｆｕｒｔｈｅｒｉｍｐｒｏｖｅｔｈｅｐｅｒｆｏｒｍａｎｃｅｏｆｔｒａｃｋｉｎｇｍａｎｅｕｖｅｒｉｎｇｔ...     

一种频域插值变化迭代频率估计方法

针对现有频率估计算法存在的复杂度高、频率估计能力弱、估计结果均方差大等缺点,在固定迭代AM（Aboutanios—Mulgrew）无偏频率估计算法基础上,提出一种频域插值变化迭代频率估计算法,推导了不同迭代参数实现无偏估计的充分条件,证明了有偏估计时本算法的收敛性和偏离度,通过设置不同迭代参数,可以实现无偏或有偏估计。仿真分析表明：当具有较高信噪比时,在整个频率估计范围内,该方法均方误差接近CRLB（Cramer-RaoLowerBound,克拉美一罗下限）;当FFT（FastFourierTransform,快速傅里叶变换）粗估计残余频率接近0．5时,该方法的均方误差优于CRLB,为CRLB的96％。     

Instrumental variable adaptive array processing

An instrumental variable (IV) approach is presented for estimating the weights of an adaptive antenna array. Theoretical analysis of the IV method shows that the antenna gain weights are independent of finitely correlated noise, so that unbiased estimation of signal arrival angles is possible. Only matrix inversions are required to compute the weight estimates. In this sense, the IV method provides performance comparable with eigenvector techniques but with lower computational burden. Both minimal and overdetermined IV estimators are derived. The overdetermined estimators give the same theoretical array weights as minimal estimators, but yield more accurate weight estimates in real data situations. Simulation results are presented to compare these IV methods with one another and with conventional matrix inversion weight estimators. In these examples it is seen that IV methods are able to resolve closely spaced interference sources when conventional matrix inversion techniques cannot. It is also shown that overdetermined methods are capable of providing weight estimates with lower variances than those of minimal methods