一种发动机性能参数衰退的估计方法

发动机性能参数是不能通过测量得到的,但性能参数却直接反映各部件性能状况,因此研究发动机性能参数变化很有实际意义。本文考虑两种情况,一种情况发动机测量参数个数少于待估性能参数,另一种情况是测量参数个数等于待估性能参数。对于前者,通过整体上假定各性能参数服从线性衰退趋势,建立非线性约束估计发动机性能参数;对于后者,本文设计一方法,基于发动机稳态模型,通过求解非线性方程组,估计部件性能参数。考虑这两种方法的不足,提出一种新的性能参数估计方法。结果表明,新的估计方法更为有效,结果与实际更相符。     

Particle filtering based parameter estimation for systems with output-error type model structures

The output-error model structure is often used in practice and its identification is important for analysis of output-error type systems. This paper considers the parameter identification of linear and nonlinear output-error models. A particle filter which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilized to estimate the unmeasurable true process outputs. To improve the convergence rate of the proposed algorithm, the scalar innovations are grouped into an innovation vector, thus more past information can be utilized. The convergence analysis shows that the parameter estimates can converge to their true values. Finally, both linear and nonlinear results are verified by numerical simulation and engineering.     

Filtering based parameter estimation for observer canonical state space systems with colored noise

This paper surveys the identification of observer canonical state space systems affected by colored noise. By means of the filtering technique, a filtering based recursive generalized extended least squares algorithm is proposed for enhancing the parameter identification accuracy. To ease the computational burden, the filtered regressive model is separated into two fictitious sub-models, and then a filtering based two-stage recursive generalized extended least squares algorithm is developed on the basis of the hierarchical identification. The stochastic martingale theory is applied to analyze the convergence of the proposed algorithms. An experimental example is provided to validate the proposed algorithms.     

Operational matrix based set-membership method for fractional order systems parameter identification

In this paper, a new method is proposed to identify the coefficients and differentiation orders of fractional order systems with measurement noise. The proposed method combines the operational matrix method and the set-membership method. First, the block pulse functions operational matrix of the fractional differentiation is used to convert the fractional order system to an algebraic system. Then, the coefficients and differentiation orders are simultaneously estimated through a nest loop optimization process, where the optimal bounding ellipsoid set-membership algorithm is utilized to estimate the system’s coefficients and the orders are estimated with the interior-point method. The proposed method can accurately estimate the coefficients and differentiation orders of fractional order systems under any bounded measurement noise with less computational effort. Experimental results demonstrate the effectiveness of the proposed method.     

机载单天线SAR动目标检测与参数估计

对机载单天线SAR/GMTI模式下动目标参数估计精度较低的问题进行研究.首先,用多普勒频移量和距离走动量来估计动目标径向速度,根据估计的结果校正距离走动.然后,用改进的反射特性位移法来估计动目标的多普勒调频率,在不存在加速度时估计出动目标方位向速度.这样就可以在进行动目标参数估计的同时实现聚焦成像.仿真结果验证了该方法的有效性.     

Recursive parameter estimation algorithm for multivariate output-error systems

In this paper, we consider the parameter estimation issues of a class of multivariate output-error systems. A decomposition based recursive least squares identification method is proposed using the hierarchical identification principle and the auxiliary model idea, and its convergence is analyzed through the stochastic process theory. Compared with the existing results on parameter estimation of multivariate output-error systems, a distinct feature for the proposed algorithm is that such a system is decomposed into several sub-systems with smaller dimensions so that parameters to be identified can be estimated interactively. The analysis shows that the estimation errors converge to zero in mean square under certain conditions. Finally, in order to show the effectiveness of the proposed approach, some numerical simulations are provided.     

Several gradient parameter estimation algorithms for dual-rate sampled systems

This paper presents three identification methods for dual-rate sampled systems. The first method combines the stochastic gradient algorithm with the polynomial transformation technique, which can estimate the parameters of the identification model. The second method is the finite impulse response model based stochastic gradient algorithm, which can indirectly estimate the parameters of the dual-rate systems by using all the inputs and the available outputs. The third method is the missing output estimation model based stochastic gradient algorithm with a forgetting factor, which can directly estimate the parameters of the dual-rate systems by using all the inputs and all the outputs (include the estimated outputs). An example is provided to verify the effectiveness of the proposed methods.     

Auxiliary model based least squares parameter estimation algorithm for feedback nonlinear systems using the hierarchical identification principle

This paper presents a decomposition based least squares estimation algorithm for a feedback nonlinear system with an output error model for the open-loop part by using the auxiliary model identification idea and the hierarchical identification principle and by decomposing a system into two subsystems. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm has a smaller computational burden. The simulation results indicate that the proposed algorithm can estimate the parameters of feedback nonlinear systems effectively.     

Distributed dynamic state estimation with parameter identification for large-scale systems

In this paper, we consider a distributed dynamic state estimation problem for time-varying systems. Based on the distributed maximum a posteriori (MAP) estimation algorithm proposed in our previous study, which studies the linear measurement models of each subsystem, and by weakening the constraint condition as that each time-varying subsystem is observable, this paper proves that the error covariances of state estimation and prediction obtained from the improved algorithm are respectively positive definite and have upper bounds, which verifies the feasibility of this algorithm. We also use new weighting functions and time-varying exponential smoothing method to ensure the robustness and improve the forecast accuracy of the distributed state estimation method. At last, an example is used to demonstrate the effectiveness of the proposed algorithm together with the parameter identification.     

自适应雷达目标参数估计的Cramer-Rao下界

在窄带信号和窄带阵列假设下,分析了均匀面阵(UPA)自适应雷达中目标参数的最大似然估计.首先分析了UPA信号模型,随后给出了目标参数的最大似然估计和相应的CR下界.通过Jacobian矩阵给出了目标俯仰角、方位角、径向速度、模和相位的CRB.分析结果是1-D均匀线性阵列(ULA)到2-D(UPA)的推广,可用于空中运动目标参数估计的性能分析.     

Asymmetric Gaussian chirplet model and parameter estimation for generalized echo representation

Gaussian Chirplet Model (GCM) is commonly used for signal analysis in many fields including ultrasound, radar, sonar, seismology, and biomedicine. The symmetric envelope of GCM is often inadequate in representing real echo envelopes which are more likely to be asymmetric. In our previous work we introduced the Asymmetric Gaussian Chirplet Model (AGCM) that generalizes the GCM. In this paper, an efficient successive parameter estimation algorithm is proposed utilizing echo envelope and instantaneous phase obtained via analytical signal representation. The initial parameters obtained via successive estimation are fine-tuned with a fast Gauss–Newton algorithm developed for the AGCM to achieve Maximum Likelihood Estimation (MLE) of model parameters. The performance of parameter estimation algorithm is formally verified employing Monte-Carlo simulations and Cramer–Rao Lower Bounds. Parameter estimation is shown to be minimum variance and unbiased for SNR levels 10 dB and higher. Furthermore, AGCM has been tested on real ultrasound echoes measured from planar targets. AGCM provides better echo fits than the GCM due to its more flexible envelope.     

基于改进小波脊提取算法的数字信号瞬时频率估计方法

在深入分析小波脊原理的基础上,针对数字信号瞬时频率提取精度要求,利用小波脊提取数字信号的瞬时频率,并对方法的初始值设置、解析小波参数设置和估计精度设置等关键问题进行了分析并改进.实际信号实验表明,改进算法鲁棒性明显增强,实用价值较大.     

Active fault diagnosis for a class of closed-loop systems via parameter estimation

This paper addresses an active fault diagnosis problem for a class of discrete-time closed-loop system with stochastic noise. By introducing the theories of system identification, a novel active fault diagnosis method is developed to detect and isolate the faults. An important advantage of the proposed method is that there is no need to cut off the original input signal, which is necessary in most active fault diagnosis methods. Firstly, due to the features of the faults, we transform the problem of fault diagnosis into a problem of model selection by estimating model parameters. Then, the sufficient condition for active fault diagnosability is analysed, and the property that auxiliary input signal can enhance the fault diagnosability is given. Finally, simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.     

Hierarchical recursive least squares parameter estimation of non-uniformly sampled Hammerstein nonlinear systems based on Kalman filter

This paper focuses on parameter estimation problems for non-uniformly sampled Hammerstein nonlinear systems. By combining the lifting technique and state space transformation, we derive a nonlinear regression identification model with different input and output updating rates. Furthermore, the unmeasurable state vector is estimated by Kalman filter, and by using the hierarchical identification principle, we develop a hierarchical recursive least squares algorithm for estimating the unknown parameters of the identification model. Finally, illustrative examples are given to indicate that the proposed algorithm is effective.     

Global exponential stability for multi-group neutral delayed systems based on Razumikhin method and graph theory

This paper is concerned with the global exponential stability for an original class called coupled systems of multi-group neutral delayed differential equations (MNDDEs). By employing Razumikhin method along with graph theory, sufficient conditions are established to guarantee the global exponential stability of MNDDEs, which are in the form of Razumikhin theorem. For the convenience of use, sufficient conditions in the form of coefficients are also obtained. Furthermore, coefficient-type criterion is employed to study the stability of coupled neutral delay oscillators which shows the applicability of our findings. Finally, two numerical examples are given to demonstrate the validity and feasibility of the theoretical results.     

The filtering based parameter identification for bilinear-in-parameter systems

This paper discusses the parameter estimation for a class of bilinear-in-parameter systems with colored noise. By utilizing the filtering technique, we derive the relationship between the filtered output and the measurement output and obtain two linear regressive sub-models. A filtering based multi-innovation stochastic gradient algorithm is derived for interactively identifying each sub-model. The proposed algorithm avoids the estimation of correlated noise and improves the parameter estimation accuracy by making full use of the measurement data. The numerical simulation results indicate that the proposed algorithm has higher estimation accuracy than the hierarchical multi-innovation stochastic gradient algorithm.     

基于信息敏感性的指标筛选与赋权方法研究

为了解决现有主成分法存在的无法赋权及合理筛选指标的问题,提出了基于信息敏感性的指标筛选与赋权模型。本文的主要创新与特色:一是通过主成分占原始指标集信息的比例与每个被保留的主成分对指标的偏导数乘积的和,反映该指标对原始指标集信息的影响程度。依据信息敏感性指标筛选标准确定累积信息含量,通过累积信息含量的大小遴选指标。二是通过指标的信息敏感性占全部指标信息敏感性的比例进行指标的赋权,以使指标的权重反映不同指标相对信息含量的大小。最后,通过一个实例将提出的指标筛选方法与现有主成分指标筛选方法进行了对比分析,以此说明所提出的指标筛选方法的可行性及有效性。     

Physical parameter sensitivity of system eigenvalues and physical model reduction

The identification of subsystems and/or components that is related to a given eigenvalue of the overall system is a challenging and important topic. The use of special structure of the system matrices obtained busing bond graphs can result in identifying subsystems and/or components that affect a given eigenvalue of an overall system. This paper, by making use of a set of theorems and definitions proposes an efficient procedure for this purpose. The basic procedure is based upon the calculation of sensitivity of eigenvalues. The so-called “effect” matrices are produced that indicates the relative importance of physical parameters on a selected eigenvalue. In addition to the relative importance, the effect matrix is used for an efficient physical model reduction procedure. Furthermore, reasons of different dynamic behavior of a system can be explained. Use of effect matrices also improves the physical model reduction method based on decomposition procedures. Three examples are given to illustrate the approach and its consequences.     

Hubble parameter estimation via dark sirens with the LISA-Taiji network

The Hubble parameter is one of the central parameters in modern cosmology,and describes the present expansion rate of the universe.The values of the parameter i...     

Analysis and parameter estimation of bilinear systems via Chebyshev polynomials

The operational properties of the integration and product of Chebyshev polynomials are used in the analysis of bilinear systems by the approximation of time functions by truncated Chebyshev series. The operational properties are also applied to determine the unknown parameters of a general bilinear system from the input-output data. Examples with excellent results are given.