Universal FRFT-based algorithm for parameter estimation of chirp signals

The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.     

Carrier frequency and symbol rate estimation based on cyclic spectrum

Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.With the development of wireless communication,the signal transmission environment has become increasingly bad,causing more difficulties in parameter estimation.It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related.In practice,it is impossible to calculate the cyclic spectrum of infinite length data signals.When using finite length data to obtain a cycle spectrum,the truncation noise is induced,resulting in interference.It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines.An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.     

Parameter estimation of LFM signals based on time reversal

In this paper, parameter estimation of linear frequency modulation(LFM) signals containing additive white Gaussian noise is studied. Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR) threshold, a parameter estimation method for LFM signals based on time reversal is proposed. The proposed method avoids SNR loss in the process of estimating the frequency, thus reducing the SNR threshold. The simulat...     

Parameter estimation of DSSS signals in non-cooperative communication system

A new adaptive estimator for direct sequence spread spectrum （DSSS） signals using fourth-order cumulant based adaptive method is considered. The general higher-order statistics may not be easily applied in signal processing with too complex computation. Based on the fourth-order cumulant with 1-D slices and adaptive filters, an efficient algorithm is proposed to solve the problem and is extended for nonstationary stochastic processes. In order to achieve the accurate parameter estimation of direct sequence spread spectrum （DSSS） signals, the fast step uses the modified fourth-order cumulant to reduce the computing complexity. While the second step employs an adaptive recursive system to estimate the power spectrum in the frequency domain. In the case of intercepted signals without large enough data samples, the estimator provides good performance in parameter estimation and white Gaussian noise suppression. Computer simulations are included to corroborate the theoretical development with different signal-to-noise ratio conditions and recursive coefficients.     

Differentially Private Distributed Parameter Estimation

Data privacy is an important issue in control systems, especially when datasets contain sensitive information about individuals. In this paper, the authors are concerned with the differentially private distributed parameter estimation problem, that is, we estimate an unknown parameter while protecting the sensitive information of each agent. First, the authors propose a distributed stochastic approximation estimation algorithm in the form of the differentially private consensus+innovations(DP-CI...     

Modelling of a Class of Nonstationary Time Series with Kalman Filter Using Moving Window

In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalmnan filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimation during recursive process by using LSM, the state space equation is constructed, then the Kahnan filter using moving window is made to get the data with reduced level of observation noise. Finally, the precise parameter estimation can be obtained by using the LSM again. The algorithm is carried on recursively. Good results for estimating and forecasting are shown by simulation, examples. The algorithm of Kalman filter using moving window proposed by us is introduced in this paper, which can guarantee the precision and convergence of Kalman filter.     

Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.     

Iterative Multiuser Receiver Based on Decorrelating Decision-Feedback Detection

Abstract: Iterative multiuser receiver for joint multiuser detection and channel decoding improves the receiver'sperformance by passing soft information between multiuser detection and channel decoding. In this paper, an iterativemultiuser receiver based on decorrelating decision-feedback detection is proposed, which is effective to implementmultiuser detection, channel decoding and parameter estimation. The data decoding and the parameter estimation arereinforced each other. The bit error rate decreases along with the increase of the iteration, and the parameter estimationapproaches the optimum at the same time.     

Discriminant analysis based on statistical depth

<正> In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four properties mentioned in Zuo and Serfling(2000)is proposed.Then aclassification rule based on the depth function family is proposed.The classification parameter b couldbe modified according to the type-Ⅰ error α,and the estimator of b has the consistency and achievesthe convergence rate n~(-1/2).With the help of the proper selection for depth family parameter c,theapproach for discriminant analysis could minimize the type-Ⅱ error β.A simulation study and a realdata example compare the performance of the different discriminant methods.     

MOMENT-METHOD ESTIMATION BASED ON CENSORED SAMPLE

In reliability theory and survival analysis, the problem of point estimation based on the censored sample has been discussed in many literatures. However, most of them are focused on MLE, BLUE etc; little work has been done on the moment-method estimation in censoring case. To make the method of moment estimation systematic and unifiable, in this paper, the moment-method estimators(abbr. MEs) and modified momentmethod estimators(abbr. MMEs) of the parameters based on type Ⅰ and type Ⅱ censored samples are put forward involving mean residual lifetime. The strong consistency and other properties are proved. To be worth mentioning, in the exponential distribution,the proposed moment-method estimators are exactly MLEs. By a simulation study, in the view point of bias and mean square of error, we show that the MEs and MMEs are better than MLEs and the “pseudo complete sample” technique introduced in Whitten et al.(1988). And the superiority of the MEs is especially conspicuous, when the sample is heavily censored.     

基于组合式信号源的非线性系统辨识

针对非线性系统中噪声的干扰,研究了一类神经模糊Hammerstein输出误差非线性系统的建模和辨识方法,利用组合式信号源实现静态非线性模块和动态线性模块参数辨识的分离,推导了相关性分析法和辅助模型递推最小二乘辨识方法估计动态线性模块和非线性模块的参数,有效抑制系统输出噪声的干扰。仿真结果表明：与最小二乘算法、多项式模型以及多信息方法相比,提出的方法具有参数估计收敛速度快,辨识精度高,建模误差小等优势,验证了所提学习算法的有效性。     

有色噪声系统的迭代辨识与递推辨识方法仿真比较研究

针对ARARX模型(即动态调节模型),提出了分别辨识系统模型参数向量和噪声模型参数向量的新型最小二乘迭代辨识方法.其基本思想是:通过极小化一个信息矩阵中含噪声项的准则函数,导出两个参数向量的最小二乘估计式,进一步将估计式中信息矩阵所含的未知噪声项用其迭代估计代替,而其迭代估计又用前一次迭代的参数估计进行计算.在每步迭代计算中,二者执行了一个递阶计算过程.与滤波式递推广义最小二乘算法相比,提出的迭代算法也可用于在线辨识,而且在每一步迭代计算中,反复利用了系统可测数据信息,因而能够获得高精度参数估计.仿真例子证实了理论研究结果.     

Maximum Likelihood Estimation of the Identification Parameters and Its Correction

By taking the subsequence out of the input-output sequence of a system polluted by white noise, an independent observation sequence and its probability density are obtained and then a maximum likelihood estimation of the identification parameters is given. In order to decrease the asymptotic error, a corrector of maximum likelihood (CML) estimation with its recursive algorithm is given. It has been proved that the corrector has smaller asymptotic error than the least square methods. A simulation example shows that the corrector of maximum likelihood estimation is of higher approximating precision to the true parameters than the least square methods.     

INS/双星组合导航半实物仿真研究

对INS/双星跑车试验数据进行了分析和处理,采用小波分解的方法去除了定点定位试验中的测量噪声,并对双星接收机的常值偏移进行了估计,仿真结果表明双星系统的定位精度在20米之内。在跑车试验中使用递推最小二乘法和Kalman滤波技术对INS/双星的位置组合方案进行了仿真。仿真结果表明,Kalman滤波技术比最小二乘算法具有更高的估计精度,它能有效地降低INS的位置误差。     

SR-CH∞KF用于弹丸飞行姿态测量研究

为提高弹丸姿态测量精度,提出一种基于H∞滤波的平方根容积卡尔曼滤波.该方法通过三轴地磁传感器和陀螺仪组合测量模型,采用欧拉角算法模型减少状态维数并使状态方程呈现线性化,可以减少计算量.该方法可以适用于量测噪声不确定的情况,引入新息序列不断修正误差限定参数来更新量测噪声估计值,可以提高滤波的精度和鲁棒性.奇异值分解能够保...     

基于车辆动力学和Kalman滤波的汽车状态软测量

针对汽车动力学控制过程中难以在线测得的横摆角速度等状态参数,根据参数软测量理论,采用Kalman滤波并结合汽车两自由度动力学模型,建立了汽车横摆角速度的线性最小均方误差估计算法。仿真计算与场地实验的结果验证了该算法的有效性,同时软测量技术的采用也为汽车控制系统的状态参数测量提供了一条可行、准确且低成本的研究思路。     

Adaptive tracking of a class of first-order systems with binary-valued observations and fixed thresholds

This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds.A recursive projection algorithm is proposed for parameter estimation based on the statistical properties of the system noise.Then,an adaptive control law is designed via the certainty equivalence principle.By use of the conditional expectations of the innovation and output prediction with respect to the estimates,the closed-loop system is shown to be stable and asymptotically optimal.Meanwhile,the parameter estimate is proved to be both almost surely and mean square convergent,and the convergence rate of the estimation error is also obtained. A numerical example is given to demonstrate the efficiency of the adaptive control law.     

一种优化的自适应总体最小二乘系统辨识算法

对于监督信号和训练信号都含有噪声的系统辨识问题,如果采用经典的最小均方和迭代最小二乘算法进行估计,会带来较大的误差,而直接求解又会有较大的计算量,不利于在线计算.将权向量的求解转化为增广输入向量自相关矩阵瑞利商的受限最佳化问题,对增广输入向量进行遮代估计,同时建立了步长因子和误差信号问的函数关系,这个函数关系是建立在代价函数相对于步长梯度的基础上,而不是基于经验公式.所提算法结构简单,具有更好的稳健性,仿真表明这种算法相对于同类总体最小二乘算法和其他自适应算法有更快的收敛速度和更高的收敛精度.     

空地联合预警系统的弹道估计实时算法

针对弹道导弹预警系统中的弹道实时估计难题,提出了一种新的卫星与雷达测量数据的加权快速融合方法,它是一种递推式计算方法,对有效测元进行逐个融合计算,可以提高估计精度。在此基础上,探讨了一种基于匹配多项式最小二乘滤波的雷达数据实时处理方法,该算法能有效的降低截断误差。给出了应用这两种方法的具体步骤,理论分析和仿真计算结果表明了算法的有效性。     

一种多传感器数据时空融合估计算法

当采用分布在不同空间位置上的多传感器观测值对测量噪声干扰下的参数进行融合估计时,数据融合存在时间性与空间性。为了提高测量精度,基于参数估计理论,提出一种多传感器数据时空融合算法。该算法将数据融合分解为两次估计,第一次是基于时间的递推融合估计,第二次是基于空间的自适应加权融合估计。该算法不要求知道测量数据的任何先验概率分布知识,编程简单,计算量小。计算机仿真表明,该算法在减少测量误差方面优于目前已有的基于时间或基于空间的多传感器数据融合算法。