不完全信息下导弹参数与状态的联合估计

基于UKF推导了非线性系统参数的递推最大似然估计算法, 并结合UKF实现了对不完全信息下导弹的参数与状态的实时联合估计。首先通过引入导弹的导引律, 建立导弹的状态滤波模型, 进而给出参数辨识的一般递推似然法, 并在UKF状态滤波算法的基础上推导出非线性递推最大似然参数估计方法, 实现了参数估计与状态滤波的并行计算。仿真结果表明, 该方法收敛速度快, 具有很好的实时性和较高的估计精度。     

Recursive Parameter Estimation Algorithms and Convergence for a Class of Nonlinear Systems with Colored Noise

Parameter estimation is important for controller design of linear systems and nonlinear systems. The parameters of the systems can be estimated through some identification algorithms. This paper presents a recursive generalized extended least squares algorithm and a generalized extended stochastic gradient (GESG) algorithm for identifying the parameters of a class of nonlinear systems. Furthermore, a multi-innovation GESG algorithm is derived to improve the estimation accuracy. The simulation example is provided to test the effectiveness of the proposed algorithms.     

A State-Space Approach to Multiuser Parameters Estimation Using Central Difference for CDMA Systems

In this paper, it is shown that a state-space model applies to the code-division multiple-access (CDMA) channel, and Central Difference Filter (CDF) produces channel estimates with the minimum mean-square error (MMSE). This result may be used as compare to Extended Kalman Filter (EKF) which used as channel estimator in CDMA system. The main purpose of this paper is to compare robustness of channel estimator for realistic rapidly time-varying Rayleigh fading channels. To overcome the highly nonlinear nature of time delay estimation and also improve the accuracy, consistency and efficiency of channel estimation, an iterative nonlinear filtering algorithm, called the CDF has been applied in the field of CDMA System. The proposed channel estimator has a more near-far resistant property than the conventional Extended Kalman Filter (EKF). Thus, it is believed that the proposed estimator can replace well-known filters, such as the EKF. The Cramer-Rao lower bound (CRLB) is derived for the estimator, and simulation result show that it is nearly near-far resistant and clearly outperforms the EKF. Jang Sub Kim was born June 15, 1974, in Yeongdeok, Korea. He received the M.S. degree in school of electrical and computer engineering from Sungkyunkwan University, Seoul, Korea. He is currently with the School of Information and Communication Engineering, Sungkyunkwan University, where he was a Ph. D. student since 1999. His research interests include code-division multiple access, channel estimation, position location, and wireless communications. Seokho Yoon (S‘99–M‘1) received the B.S.E. (summa cum laude), M.S.E., and Ph.D. degrees in electrical engineering from KAIST, Daejeon, Korea, in 1997, 1999, and 2002, respectively. From April 2002 to June 2002, he was with the Department of Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology, Cambridge, MA, and from July 2002 to February 2003, he was with the Department of Electrical Engineering, Harvard University, Cambridge, MA, as a Postdoctoral Research Fellow. In March 2003, he joined the School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea, where he is currently an Assistant Professor. His research interests include spread spectrum systems, mobile communications, detection and estimation theory, and statistical signal processing. Dr. Yoon is a member of the IEEK and KICS. He was the recipient of a Bronze Prize at Samsung Humantech Paper Contest in 2000. Dong-Ryeol Shin (M‘97) was born in Seoul, Korea, in 1957. He received the B.S., M.S. and Ph.D degree in electrical engineering from the Sungkyunkwan University in 1980, and the Korea Advanced Institute of Science and Technology (KAIST) in 1982 and the Georgia Institute of Technology in 1992, respectively. During 1992-1994, he had worked for Samsung Data Systems, Ltd., Korea. Since 1994, he has been with network research group at the Sungkyunkwan University, Korea, as a professor. His current research interests include wireless communications and ubiquitous computing.     

一种抑制符号速率估计背景色噪声的非线性滤波算法

数字调制信号符号速率估计的依据是循环平稳理论,由于信号的符号速率就是其基本循环频率,因此可以通过提取信号非线性变换(例如循环自相关函数)的循环频率获知符号速率.但是,非线性变换不仅能产生对应于符号速率的正弦分量及其各次谐波,还会将信号自身转变成不利于谱线提取的连续有色噪声(其能量主要分布在低频部分).当观察数据长度有限时,自噪声对谱线提取的影响尤其明显.本文深入研究了数字调制信号非线性变换的频域特征,充分利用离散频率分量不同于连续噪声而在其邻域内突起的显著特点,提出一种能够有效抑制背景色噪声的非线性滤波算法.文中详尽的Monte Carlo仿真验证了算法的有效性.     

非线性功放信道下联合信道估计研究

为了解决非线性放大器在60 GHz毫米波信道中造成的非线性影响,提出了基于马尔科夫蒙特卡洛（Markov Chain Monte Carlo,MCMC）算法的联合信道估计与信号检测技术。采用的是MCMC算法中的Metropolis-Hastings方法,在非线性放大器及信道参数未知的情况下,通过被非线性和噪声污染的输出信号（观测信号）来估计非线性放大器的参数,检测输入信号被称为盲均衡技术。仿真结果给出了非线性参数与真实值的对比图以及随SNR变化的误比特率,性能优越。     

大气噪声模型参数的非线性回归估计

Class B噪声模型是描述大气噪声的一种很好的统计物理模型。该文采用非线性回归算法估计Class B模型的参数。该算法从Class B噪声模型的特征函数出发,推导非线性回归模型,并优化算法迭代过程。同时设计了初始值估计方案,加速了算法的收敛。并采用特殊设计的序列计算对数特征函数,解决了特征函数估计的零点波动的问题。仿真和实测结果表明,该算法收敛快,精度高,有很高的实用价值。     

空间色噪声环境下双基地MIMO雷达角度和多普勒频率联合估计方法

该文提出了一种空间色噪声环境下双基地MIMO雷达角度和多普勒频率的联合估计方法。该方法利用相邻时刻匹配滤波器输出的互相关矩阵构造新矩阵,消除了空间色噪声的影响,通过对其进行奇异值分解(SVD)获得信号子空间,然后将相邻时刻匹配滤波器输出的时间相位差作为时间旋转因子,采用ESPRIT方法获得目标多普勒频率、波离方向(DOD)和波达方向(DOA)的联合估计。该方法没有阵列孔径损失,且所估计参数能够实现自动配对。计算机仿     

DOA Estimation and Detection in Colored Noise Using Additional Noise-Only Data

In a typical array processing scenario, noise acting on the array can not be assumed spatially white. It is in many cases necessary to use quiet periods, when only noise is received, to estimate the noise covariance. If estimation of the signal parameters and noise covariance is performed jointly, performance can be improved. This is especially true when stationarity considerations limit the amount of available valid noise-only data. An asymptotically valid approximative maximum likelihood method (AML) for the estimation problem is derived in this work. The resulting criterion is, when concentrated with respect to the signal parameters, relatively simple. In numerical experiments, AML shows promising small-sample performance compared to earlier methods. The criterion function is also well suited for numerical optimization. The new criterion function allows for the development of a novel, MODE-like, non-iterative estimation procedure if the array belongs to the important class of uniform linear arrays. The resulting procedure retains the asymptotic properties of maximum likelihood, and numerical simulations indicate superior threshold performance when compared to an optimally weighted subspace fitting (WSF) formulation of MODE. For the detection problem, no method has been presented that takes the unknown noise covariance into account. Here, a well known detection scheme for WSF is extended to work in this scenario as well. The derivations of this scheme further stress the importance of using the correct weighting in WSF when the noise covariance is unknown. It is also shown that the minimum value of the criterion function associated with AML can be used for the detection purpose. Numerical experiments indicate very promising performance for the AML-detection scheme.     

Roundoff Noise Minimization for 2-D State-Space Digital Filters Using Joint Optimization of Error Feedback and Realization

The joint optimization problem of error feedback and realization for two-dimensional (2-D) state-space digital filters to minimize the effects of roundoff noise at the filter output subject to$L_2$-norm dynamic-range scaling constraints is investigated. It is shown that the problem can be converted into an unconstrained optimization problem by using linear-algebraic techniques. The unconstrained optimization problem at hand is then solved iteratively by applying an efficient quasi-Newton algorithm with closed-form formulas for key gradient evaluation. Analytical details are given as to how the proposed technique can be applied to the cases where the error-feedback matrix is a general, block-diagonal, diagonal, or block-scalar matrix. A case study is presented to illustrate the utility of the proposed technique.     

空间色噪声环境下双基地MIMO雷达DOD和DOA联合估计方法

针对空间色噪声环境提出一种同时利用空域和时域信息的双基地MIMO雷达DOD（Direction of Departure）和DOA（Direction of Arrival）联合估计方法。该方法在时域高斯白噪声的假设下．将不同时刻的匹配滤波器输出进行互相关以消除空间色噪声的影响并获得空时互相关矩阵,然后基于传播算子方法构造一低维矩阵,利用其特征参数与待估参数的特定关系获得目标DOD（DirectionofDe．parture）和DOA（DirectionofArrival）的联合估计。该方法能够有效克服空间色噪声的影响,所估计参数自动配对。与现有方法相比,其孔径损失小,具有更优的参数估计性能和最大可检测目标数。且对发射阵元个数没有特殊要求,具有更广泛的适用性。计算机仿真验证了本文方法的有效性。     

Constrained State Estimation for Nonlinear Systems with Unknown Input

This paper extends the problem of state estimation for linear discrete-time systems with unknown input to the nonlinear systems. Based on physical consideration, the constraints of state are also considered. And the constraints which can improve the quality of estimation are imposed on individual updated sigma points as well as the updated state. The advantage of algorithm is that it is able to deal with arbitrary constraints on the states during the estimation procedure, Least-squares unbiased estimation algorithm can be used to obtain unknown input, and the unknown input which can be any signal affects both the system and the outputs. The state estimation problem is transformed into a standard Unscented Kalman filter problem which can easily be solved. Simulations are provided to demonstrate the effectiveness of the theoretical results.     

空间色噪声环境下基于时空结构的双基地MIMO雷达角度和多普勒频率联合估计方法

该文针对空间色噪声环境提出一种基于时空结构的双基地MIMO雷达角度和多普勒频率联合估计方法,并推导了基于时空结构时角度和多普勒频率估计的克拉美-罗界(CRB)。该方法在时域噪声为高斯白噪声的假设下,首先将不同时刻匹配滤波器输出进行互相关以消除空间色噪声的影响,然后将相邻时刻匹配滤波器输出的时间相位差作为时间旋转因子,采用ESPRIT方法估计目标的DOD(Direction Of Departure), DOA(Direction Of Arrival)和多普勒频率。该方法能够克服空间色噪声的影响,所估计参数自动配对且无阵列孔径损失,并且适用于发射和接收阵列不满足平移不变结构的情况。计算机仿真验证了该文所提方法的有效性。     

量子噪声和非线性色抽运噪声共同驱动下单模激光的锁相

建立了一个由量子噪声和非线性色抽运噪声共同驱动下的单模激光立方模型,并将其在极坐标下分解为场幅和相位的朗之万方程(LE)。采用近似福克-普朗克方程的方法处理相位朗之万方程中的色噪声,使其马尔科夫化。将马尔科夫化后的相位朗之万方程与锁相条件相结合,得到稳定锁相值。详细讨论了量子噪声实虚部间的关联和抽运噪声实虚部间的关联对激光锁相的影响。结果表明激光锁相由量子噪声实虚部之间的关联引起,而抽运噪声实虚部间的关联和量子噪声实虚部间的关联均可改变这一锁相。     

基于Radon变换的大前斜视SAR多普勒参数估计方法

多普勒中心估计是前斜视SAR成像的一个关键问题.本文首先分析了前斜视SAR回波距离徙动的特点,然后提出了利用Radon变换从距离向压缩后的回波响应曲线提取多普勒中心的方法.本方法无需已知雷达平台速度和斜视角,可以直接估计得到多普勒中心,并且没有多普勒中心模糊,适用于大前斜视角SAR成像.此外,本方法在雷达平台运动速度已知时,还可进一步估计出雷达波束斜视角,进而估计出多普勒调频率及高阶相位系数,用于相位补偿及聚焦成像.仿真实验证明了算法的有效性.     

A Nonlinear Filtering Technique for the Identification of Biological Signals

A new nonlinear filtering technique is presented allowing extraction of relevant biological signals from background. The technique s derived from the one of dc restoration used in nuclear electronics. A discussion is given of the problem and a solution through the use of a nonlinear filter is proposed; the filter's way of operating is then described together with a complete multiunit neural spike processing instrument using this filtering technique. Some experimental results are presented.     

色噪声环境下MIMO雷达角度估计算法

针对色噪声环境下的MIMO雷达目标角度估计问题,提出一种基于四阶累积量切片的角度估计算法。算法利用MIMO雷达的接收数据计算出四阶累积量,构造出累积量切片矩阵,通过特征分解,结合ESPRIT算法实现了雷达目标的角度估计。同时进行了低复杂度改进,去掉了冗余信息,保留了MIMO雷达阵元扩展能力和目标分辨力,具有自动抑制加性高斯噪声和任意高斯色噪声的能力。最后计算机仿真结果证实了算法的有效性和可行性。     

Detection and Tracking of MIMO Propagation Path Parameters Using State-Space Approach

This paper describes a novel approach for detection, estimation and tracking of multiple-input multiple-output (MIMO) radio propagation parameters from multidimensional channel sounding measurements. A realistic state-space model is developed for the purpose, and the extended Kalman filter (EKF) is applied in a particular computationally efficient form to track the geometrical double-directional propagation path parameters. The observation model utilizes the dense multipath component (DMC), describing the distributed scattering in the channel, as part of the underlying noise process. The DMC model assumes an exponential profile in delay, and allows for an arbitrary angular distribution. In addition, a novel dynamic state dimension estimator using statistical goodness-of-fit tests is introduced. The employed methods are supported by illustrative estimation examples from MIMO channel sounding measurements.      

Improving the Efficiency of Natural Computing Algorithms in DOA Estimation Using a Noise Filtering Approach

We propose a novel strategy to generate initial candidate solutions for bio-inspired algorithms applied to the direction of arrival estimation problem. The idea, which aims to improve the efficiency of the estimator, consists in using the frequency response of a well-known optimum noise reduction filter as the probability density function of the set of candidate solutions. In accordance to this approach, we also employ a modified likelihood function to reduce the estimation error. Simulation results considering an immune-inspired algorithm confirm a significant improvement of its performance and efficiency, and the new estimator reaches the conditional Cramér–Rao lower bound.     

Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering

Noise reduction is a very important topic in image processing. We propose a new method to deal with the case where the noisy image has different noise levels in different regions. The main idea is to segment automatically the noisy image into several sub-images so that each sub-image has approximately the same noise level. We perform Block matching 3D filtering (BM3D) to these subimages in order to obtain denoised sub-images. We then merge sub-images together and enhance the discontinuous regions between the sub-images by performing BM3D again on small image patches. Our experimental results show the effectiveness of this proposed method in terms of Peak signal to noise ratio (PSNR) when compared with the bivariate wavelet shrinkage and the standard BM3D method. In addition to Gaussian white noise, our method performs better than the bivariate wavelet shrinkage and the standard BM3D method even for signal dependent noise.     

有色噪声中正弦信号参数的去耦估计算法

首先介绍了一种估计有色噪声中多个正弦信号参数的去耦参数估计算法，然后提出了一种基于ＦＦＴ的“迭代清除算法”来估计有色噪声中多个周期相近信号的参数。该算法不仅计算简单，而且可获得较高的频率分辨率。数值仿真结果表明了该算法的有效性。