稀疏贝叶斯字典学习空时机动目标参数估计算法

针对基于稀疏恢复的空时自适应处理(STAP)目标参数估计方法中字典失配导致估计性能下降的问题,该文提出一种基于稀疏贝叶斯字典学习的高精度目标参数估计方法。该方法首先通过目标方位信息补偿多个阵元数据构建联合稀疏恢复数据,然后对补偿后的每个阵元数据利用双线性变换进行加速度和速度项分离。最后构建速度参数和加速度参数的泰勒级数动态字典,对机动目标参数进行高精度贝叶斯字典学习稀疏恢复。仿真实验证明,该方法能有效提高字典失配情况下目标参数估计精度,估计性能优于已有字典固定离散化的稀疏恢复空时目标参数估计方法。     

基于RPRG+ICCD与RANSAC的多分量多项式相位信号参数估计

多分量多项式相位信号的检测和参数估计是多项式相位信号处理的一大难点。时频分析方法能够通过时频分布表征多项式相位信号的瞬时频率,通过对提取的瞬时频率进行多项式拟合得到其系数,经过简单的变换即可获得多项式相位信号的参数。文中利用RPRG+ICCD分离多分量多项式相位信号得到各个分量的瞬时频率,再使用RANSAC进行多项式拟合,最终实现了多项式相位信号的参数估计。实验结果表明,文中提出的方法能够实现在时频域部分重叠的多分量多项式相位信号的分离和参数估计。     

借助于泰勒级数线性时变系统的分析与最优控制

泰勒级数是通过引入泰勒多项式失量的乘积矩阵及其转置来分析线性时变系统的,为了解决最优控制问题,引入了对泰勒级数反向积分的运算矩阵,用这种方法,具有二次性能指标的线性系统最优控制状态转移矩阵就可以借助泰勒级数近似地积分出来.已经获得的结果表明,只要取级致的很少几项,就可与精确解以很好地吻合、     

一种收敛于全局无偏最优解的非线性自回归（NAR）预测

本文研究了基于Ｖｏｌｔｅｒｒａ级数展开的非线性自回归预测模型的参数估计问题，从线性自适应ＩＩＲ波滤输出误差算法的观点，研究ＮＡＲ模型的参数估计问题，利用平均收敛条件，提出一可收敛于全局无偏最优解的新算法，解决了一般的预测误差算法的有偏解问题。     

双线性系统辨识与自适应控制

本文研究随机双线性系统的自适应控制，主要包括：双线性系统的辨识和双线性系统的预测控制，极点配置控制，提出－全局收敛的并且可收敛于无偏最优解的辨识新算法。在此基础上，研究双线性系统的预测和极点配置控制，给出一可实时检测系统稳定性的新方法，解决控制系统的闭环时变极点的稳定性问题。     

自由节点样条函数平滑滤波技术

考虑了目标跟踪和航天测控中测量数据的平滑问题.提出了利用自由节点样条函数来改善多项式最小二乘滤波器设计的模型和方法.用样条函数表示弹道,能改善目标机动时用多项式表示弹道引入的截断误差较大的缺点,提高弹道参数的估计精度.仿真计算结果表明该方法能显著改善目标机动时的运动参数估计精度.     

三阶多项式相位信号参数估计

三阶多项式相位信号常用于描述复杂运动目标的雷达回波,但其未知参数多,参数估计实现起来比较困难。针对此问题,本文提出了一种分两步的三阶多项式相位信号参数估计方法:首先利用Radon变换提取三阶多项式相位信号在三次相位变换后对应的直线,从而得到相位参数的初始值估计;然后将初始值代入最大似然估计函数并利用单纯形法对其进行优化得到精确估计结果。给出了单分量和多分量情况下的参数估计流程。仿真结果表明,本文算法估计性能达到CRB界,且起始工作信噪比门限低。      

复加性白高斯噪声中随机幅值多项式相位信号的Cramer—Rao下界

本文考虑了在复的白高斯加性噪声中观测的多个随机幅值多项式相位信号的参数估计问题，导出了多项式相位系数估计以及幅值样本均值的Ｆｉｓｈｅｒ信息矩阵，因而利用数值计算可得到相应的Ｃｒａｍｅｒ－Ｒａｏ界（ＣＲＢ），模拟结果验证了所得结果的正确性。     

基于RCCF‒PCT的机动目标运动参数估计

针对高速机动目标的运动参数估计问题,基于鲁棒互相关和多项式调频小波变换(RCCF-PCT),提出了一种高速机动目标运动参数估计方法。该方法首先采用幂率变换增强信号,利用距离像互相关和鲁棒多项式回归完成距离徙动校正;再采用多项式调频小波变换对多普勒徙动初步补偿后的信号进行参数化时频分析,利用提取的时频脊特征完成目标运动参数的精确估计。通过数值实验验证了算法的有效性。     

稀疏重构下的非线性调频信号参数估计算法

非线性调频信号广泛应用于雷达声纳领域，其具有多阶多项式相位、未知参数多的特点，给参数估计带来困难。针对此问题，本文提出一种稀疏重构下的非线性调频信号参数估计算法。该方法利用Gabor原子良好时频特性，以 范数稀疏正则最小二乘模型为目标函数，并推导了问题的二阶锥规划（SOCP）形式，最终通过求解的Gabor原子进行参数估计。算法分析信号的时频特征，完成信号的分解重构，适应于各类调频信号。仿真实验证明，本文算法对调频信号二阶与一阶相位参数估计精度都贴近CRB，而对二阶参数的估计较二次相位差分算法更适应较低信噪比。      

Joint state filtering and parameter estimation for linear stochastic time-delay systems

This paper presents the joint state filtering and parameter estimation problem for linear stochastic time-delay systems with unknown parameters. The original problem is reduced to the mean-square filtering problem for incompletely measured bilinear time-delay system states over linear observations. The unknown parameters are considered standard Wiener processes and incorporated as additional states in the extended state vector. To deal with the new filtering problem, the paper designs the mean-square finite-dimensional filter for incompletely measured bilinear time-delay system states over linear observations. A closed system of the filtering equations is then derived for a bilinear time-delay state over linear observations. Finally, the paper solves the original joint estimation problem. The obtained solution is based on the designed mean-square filter for incompletely measured bilinear time-delay states over linear observations, taking into account that the filter for the extended state vector also serves as the identifier for the unknown parameters. In the example, performance of the designed state filter and parameter identifier is verified for a linear time-delay system with an unknown multiplicative parameter over linear observations.     

Joint estimation of carrier offset and code timing for DS-CDMA with performance analysis

This paper considers the problem of joint carrier offset and code timing estimation for code-division multiple-access (CDMA) systems. In contrast to existing schemes that require nonlinear iterative searches over the multidimensional parameter space, this paper proposes a blind estimator that provides an algebraic solution to the joint parameter estimation problem. By exploiting the subspace structure of the observed signal, the multiuser estimation is first decoupled into a series of single-user estimation problems, and then analytical tools of polynomial matrices are invoked for joint carrier and code timing estimation of a single user. The proposed estimator is near-far resistant. It can deal with frequency-selective and time-varying channels. The performance of the proposed scheme is examined analytically by a first-order perturbation analysis. The authors also derive an unconditional Crame/spl acute/r-Rao bound (CRB) that is conditioned neither on fading coefficients nor information symbols; as such, the CRB is considered a suitable lower bound for blind methods. Numerical examples are presented to evaluate and compare the proposed and a multidimensional search (MD-search)-based scheme.     

Recursive nonlinear system identification by a stochastic gradient algorithm: stability, performance, and model nonlinearity considerations

A parameter estimation problem in a class of nonlinear systems is considered where the input-output relation of a nonlinear system is approximated by a polynomial model (e.g., a Volterra series). A least mean squares (LMS) type algorithm is utilized for the recursive estimation of the polynomial coefficients, and its resulting mean square error (MSE) convergence properties are investigated. Conditions for the algorithm stability (in the mean square sense) are established, steady-state MSE bounds are obtained, and the convergence rate is discussed. In addition, modeling accuracy versus steady-state performance is examined; it is found that an increase of the modeling accuracy may result in a deterioration of the asymptotic performance, that is, yielding a larger steady-state MSE. Linear system identification is studied as a special case.     

Robust filtering for bilinear uncertain stochastic discrete-timesystems

This paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method     

一种修正的线性调频信号参数估计算法

研究了加性噪声中线性调频信号的参数估计问题。基于相位解卷和多项式拟合,考虑到初始相位可能出现模糊,提出了一种修正的线性调频信号参数的联合估计算法。仿真实验证实了所提出的修正算法的有效性。     

基于粒子群优化算法的Logistic模型参数估计

将Logistic模型的参数估计问题转化为一个多维无约束函数优化问题,然后利用粒子群优化算法(PSO)的全局搜索能力对此问题求解.仿真实验中所使用的数据包括真实数据和随机采样数据.实验结果表明,在这两种数据条件下PSO算法均能够较准确地估计获得Logistic模型的参数,证实了PSO算法是Logistic模型参数估计的一种可靠有效的算法.同时也分析了参数维数和噪声对PSO算法的收敛性和稳定性的影响.     

基于改进型快速双线性参数估计的复杂运动目标ISAR成像

针对复杂运动目标的逆合成孔径雷达(ISAR)成像中多普勒扩散导致的成像质量下降,该文在建立方位回波信号为立方相位信号(CPS)的基础上,提出一种基于改进型快速双线性参数估计的复杂运动目标ISAR成像方法。该方法通过利用双线性立方相位函数,非均匀快速傅里叶变换(NUFFT),基于Chirp-z的尺度变换以及快速傅里叶变换(FFT)等操作,能够快速实现CPS参数估计和复杂运动目标的ISAR成像。由于实现过程均采用NUFFT和FFT快速实现,该方法计算量小,并且双线性操作可以保证其具有较好的抗噪声性能和交叉项抑制性能。理论分析和仿真结果验证了该ISAR成像算法的有效性。