ARMA model parameter estimation based on the equivalent MA approach

The paper investigates the relation between the parameters of an autoregressive moving average (ARMA) model and its equivalent moving average (EMA) model. On the basis of this relation, a new method is proposed for determining the ARMA model parameters from the coefficients of a finite-order EMA model. This method is a three-step approach: in the first step, a simple recursion relating the EMA model parameters and the cepstral coefficients of an ARMA process is derived to estimate the EMA model parameters; in the second step, the AR parameters are estimated by solving the linear equation set composed of EMA parameters; then, the MA parameters are obtained via simple computations using the estimated EMA and AR parameters. Simulations including both low- and high-order ARMA processes are given to demonstrate the performance of the new method. The end results are compared with the existing method in the literature over some performance criteria. It is observed from the simulations that our new algorithm produces the satisfactory and acceptable results.     

模型降阶和参数估计的一种快速遗传算法

针对传统遗传算法求解模型降阶和参数估计时,适配值评价既费时又效率较低的缺点,对实数编码GA引入相似度和可信度的概念,采用插值方法进行适配值评价,大大减少了评价环节的计算量,提高了整个算法的效率和实时性．通过对典型模型降阶和参数估计问题的仿真,验证了所提出方法的可行性和有效性．     

Alopex-based evolutionary algorithm and its application to reaction kinetic parameter estimation

In this paper, a novel Alopex-based evolutionary algorithm (AEA) is proposed, whose distinguished features are stochastic selection and self-adaptive evolutionary computation. The AEA not only inherits the primary characteristics of basic evolutionary algorithms (EAs), but also possesses the merits of gradient methods and simulated annealing algorithm. It can efficiently maintain the population diversity and improve the capabilities of escaping from local optima. The numerical simulation results of 22 benchmark functions demonstrate that the performance of the proposed AEA is superior to that of the basic EAs. Finally, the new algorithm is applied to estimate the kinetic parameters of 2-chlorophenol oxidation of supercritical water. The promising results illustrate the efficiency of the proposed method and show that it could be used as a reliable tool for engineering applications.     

一种用于二维DOA估计的新颖Unitary ESPRIT算法

提出了一种基于双均匀线阵的特性新颖的Unitary ESPRIT算法。算法根据阵列接收数据,构造了一种实值数据形式,对其作实值奇异值分解,获得信号子空间。算法利用阵元几何的平移不变特性,构造了包含入射信号方位角和俯仰角的估计量。利用构造的估计量的特征值估计俯仰角信息,利用其对应的特征向量估计方位角信息,自动完成角度的配对。所提方法不仅保持了矩阵DOA估计算法和二维ESPRIT算法的优点,如自动的参数配对、不需要二维谱峰搜索等,而且能够在实数域中直接获得信号子空间,能够有效降低运算量。算法可以增加估计信号个数。仿真实验验证了算法的有效性。     

图像统计模型参数估计中的期望最大值算法

期望最大值算法是近年来图像统计模型参数估计技术领域的研究热点之一。在对期望最大值算法分析的基础上,结合其在图像统计模型参数估计中的应用研究,对改变标准期望最大值算法的3种方式进行比较分析。结合图像恢复、分割、目标跟踪以及与其他优化算法的融合应用,从丢失数据集的选取、丢失数据集和不完全数据集统计模型的建立,以及统计模型参数估计3个方面,评述期望最大值算法优缺点。丢失数据的选取和不完全数据的描述形式直接决定期望最大值算法的结构和计算复杂度,以致算法的成败。最后,讨论期望最大值算法目前存在的问题及未来的发展方向,指出其在具有丢失数据统计模型参数估计中广泛应用。     

一种高性能的二维波达方向估计算法

在二维虚拟ESPRIT算法的基础上,提出了一种高性能的改进算法。改进方法依照子阵数据直接空间平滑的思想对子阵数据进行预处理,用虚拟阵列变换技术实施阵列变换,采用波达方向矩阵法的思路计算二维角度参数。仿真结果表明,相比于空域平滑的DOA矩阵法和空域平滑的二维虚拟ESPRIT算法,该方法在低信噪比情况下对相干信号源具有更好的估计性能,对独立信号源,能够估计出超过子阵阵元数的信号。     

Computation of the exact Cramer–Rao lower bound for the parameters of a nonsymmetric half-plane 2-D ARMA model

The Cramer–Rao lower bound (CRLB) that gives the minimal achievable variance/standard deviation for any unbiased estimator offers a useful tool for an assessment of the consistency of parameter estimation techniques. In this paper, a closed-form expression for the computation of the exact CRLB on unbiased estimates of the parameters of a two-dimensional (2-D) autoregressive moving average (ARMA) model with a nonsymmetric half-plane (NSHP) region of support is developed. The proposed formulation is mainly based on a matrix representation of 2-D real-valued discrete and homogeneous random field characterized by the NSHP ARMA model. Assuming that the random field is Gaussian, the covariance matrix of the NSHP ARMA random field is first expressed in terms of the model parameters. Then, using this matrix structure, a closed-form expression of the exact Fisher information matrix required for the CRLB computation of the NSHP ARMA model parameters is developed. Finally, the main formulas derived for the NSHP ARMA model are rearranged for its autoregressive and moving average counterparts, separately. Numerical simulations are included to demonstrate the behavior of the derived CRLB formulas.     

A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm

A novel robust adaptive control algorithm is proposed and implemented in real-time on two degrees-of-freedom (DOF) of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) arm in joint-space. In addition to having a significant robustness property for the tracking, the algorithm also features a sliding-mode term based adaptive law that captures directly the parameter estimation error. An auxiliary filtered regression vector and filtered computed torque is introduced. This allows the definition of another auxiliary matrix, a filtered regression matrix, which facilitates the introduction of a sliding mode term into the adaptation law. Parameter error convergence to zero can be guaranteed within finite-time with a Persistent-Excitation (PE) condition or Sufficient Richness condition for the demand. The proposed scheme also exhibits robustness both in the tracking and parameter estimation errors to any bounded additive disturbance. This theoretical result is then exemplified for the BERT II robot arm in simulation and for experiments.     

基于ARMA建模的MEMS陀螺随机误差补偿改进算法研究

基于MEMS技术生产的MEMS器件,具有体积小、重量轻、成本低、耐冲击性、高可靠性等特点,它被广泛应用于动态水平测量装置当中。但是由于外界环境的干扰,MEMS器件的测量精度一直难以达到实际应用水平。分析了在动态水平测量当中影响MEMS器件测量精度的各种因素之后,提出了一种基于ARMA模型的针对MEMS陀螺器件随机误差补偿的改进型算法。以某型号的陀螺的随机误差为研究对象进行实验验证,结果表明,MEMS陀螺的测量精度在滤波之后有了明显的提高。经过改进后的卡尔曼滤波器和引入自适应渐消因子的卡尔曼滤波器,在静态环境下,它们的误差标准差分别降为原始误差的3.75%和4.8%,动态环境下的滤波精度也得到有效提高。证明该方法是可行的和有效的,具有较大的工程实践意义。     

基于微粒群算法的二维最大熵图像分割算法

将微粒群算法运用于二维最大熵图像阈值分割法。首先构建图像分割的二维最大熵准则函数,然后采用适用于整数规划的微粒群算法最大化该准则函数,最终实现含噪声干扰下图像的有效分割。分割实验表明,该方法具有较强的抗噪声能力,且比普通和基于遗传算法的二维最大熵法运算速度更快。     

通道失配对二维MUSIC算法测向性能的影响

针对二维MUSIC算法在空间谱估计中计算量大的问题,提出了一种基于三维垂直阵列结构的二维MUSIC算法,该算法在降维技术的基础上通过组合三个并行的均匀线阵分别估计出的一维DOA进行空间测向。然后重点分析了通道失配（幅相特性不一致）对二维MUSIC算法测向性能的影响。计算机仿真结果表明,通道失配严重影响着二维MUSIC算法的测向性能,在应用该算法进行空间信号波达方向估计的时候,必须考虑通道幅相误差的影响并对各通道的幅相特性进行均衡。     

非线性系统模型参数估计的算法模型

针对非线性系统模型的多样性,提出了适用于多种非线性模型的基于粒子群优化算法的参数估计方法。计算结果表明,粒子群优化算法是非线性系统模型参数估计的有效工具。     

小波域HMT模型参数的快速估计及其在图像降噪中的应用

小波域隐马尔可夫树(Hidden Markov Tree,HMT)模型可以很好地刻画尺度内与尺度间小波系数的相关性,但模型参数的训练过程复杂,计算量大。针对这个缺点,提出了一种不经训练的HMT模型参数快速估计方法。该算法首先用一种自适应阈值将每个子带小波系数分成不同的类,然后分别对每类进行统计,这种统计是局部的,因而有很好的局部自适应性,最后模型参数可以利用这些局部的统计特性来描述。将估计出的参数模型运用到图像降噪中,实验结果表明这种快速估计的HMT参数模型不仅可以大大提高计算速度,降低计算复杂度,而且从峰值信噪比和主观视觉效果上都不逊于传统的经过迭代训练的HMT模型降噪算法。     

基于瞬态相对模型的轨道机动目标运动参数估计

空问目标可能存在未知的机动,针对该类目标相对导航问题,提出一种基于瞬态相对模型的运动参数估计方法.该方法依据轨道密切性思想,在瞬态意义下建立相对运动模型和机动辨识算法,然后通过密切轨道到参考轨道的量测变换对冻结时间模型进行状态滤波,可为目标机动下的自主导航提供所需的相对状态与目标机动参数估值.误差分析表明,所得估值偏差均有界.仿真验证了该方法在目标存在机动情况下的有效性和一般性.     

基于改进二维ESPRIT-like算法的相干源方向角估计

针对用于二维十字相控阵的类旋转不变技术估计信号参数(ESPRIT-like)算法用于相干源方向角估计时存在仰角和方位角配对困难的问题,提出一种基于接收信号矩阵联合对角化的改进二维ESPRIT-like算法。首先,通过ESPRIT-like算法得到x和y方向自相关矩阵,并由其重构出对应的Toeplitz矩阵以去相干;然后对Toeplitz矩阵进行等效分解,得到去相干后x和y方向的等效接收信号矩阵;最后对等效接收信号矩阵进行矩阵联合对角化,实现相干源仰角和方位角自动配对,从而得到正确的信源方向估计结果。仿真结果表明,与改进前的算法相比,改进后的算法能够实现正确的方向角估计;与目前常用的基于空间平滑的二维多重信号分类(MUSIC)算法相比,在信噪比为30 dB时,该算法响应时间减少了79%左右,仰角和方位角分辨率分别提高了20%和40%左右,均方误差约为MUSIC算法角度误差的10%。     

Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (> 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m × 100 m NOAH and AIEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters.     

A passive movement method for parameter estimation of a musculo-skeletal arm model incorporating a modified hill muscle model

In this paper we present an experimental method of parameterising the passive mechanical characteristics of the bicep and tricep muscles in vivo, by fitting the dynamics of a two muscle arm model incorporating anatomically meaningful and structurally identifiable modified Hill muscle models to measured elbow movements. Measurements of the passive flexion and extension of the elbow joint were obtained using 3D motion capture, from which the elbow angle trajectories were determined and used to obtain the spring constants and damping coefficients in the model through parameter estimation. Four healthy subjects were used in the experiments. Anatomical lengths and moment of inertia values of the subjects were determined by direct measurement and calculation. There was good reproducibility in the measured arm movement between trials, and similar joint angle trajectory characteristics were seen between subjects. Each subject had their own set of fitted parameter values determined and the results showed good agreement between measured and simulated data. The average fitted muscle parallel spring constant across all subjects was 143 N/m and the average fitted muscle parallel damping constant was 1.73 Ns/m. The passive movement method was proven to be successful, and can be applied to other joints in the human body, where muscles with similar actions are grouped together.     

A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem

Due to the complicated circumstances in workshop, most of the conventional scheduling algorithms fail to meet the requirements of instantaneity, complexity, and dynamicity in job-shop scheduling problems. Compared with the static algorithms, dynamic scheduling algorithms can better fulfill the requirements in real situations. Considering that both flexibility and fuzzy processing time are common in reality, this paper focuses on the dynamic flexible job-shop scheduling problem with fuzzy processing time (DfFJSP). By adopting a series of transforming procedures, the original DfFJSP is simplified as a traditional static fuzzy flexible job-shop problem, which is more suitable to take advantage of the existing algorithms. In this paper, estimation of distribution algorithm (EDA) is brought into address the post-transforming problem. An improved EDA is developed through making use of several elements omitted in original EDA, including the historical-optimal solution and the standardized solution vectors. The improved algorithm is named as fast estimation of distribution algorithm (fEDA) since it performs better in convergence speed and computation precision, compared with the original EDA. To sum up, the ingenious transformation and the effective fEDA algorithm provide an efficient and practical way to tackle the dynamic flexible fuzzy job-shop scheduling problem.     

基于ARMA模型和狼群算法的陀螺随机漂移建模研究

光纤陀螺的随机漂移误差是影响惯性导航系统精度的关键因素之一,根据陀螺随机漂移数据的数学模型进行补偿,可有效地提高系统精度.在大量实验的基础上建立陀螺随机漂移的自回归移动平均(ARMA)模型,同时使用长自回归模型法求解模型参数,再对参数进行优化.实验结果证明:经狼群算法优化后的陀螺随机漂移模型更加准确,建模精度相对于传统的时间序列分析法有了较大提高.研究内容对光纤陀螺随机漂移建模精度的提高有较好的参考价值.     

Dynamic parameter estimation for a street canyon air quality model

The Operational Street Pollution Model (OSPM^®) is a widely used air quality model for urban street canyons. It is a parametric model, simulating the contribution from traffic emissions on a single street at receptor points at the buildings' facades. The OSPM contains a number of empirical parameters, accounting for processes such as emission factors or dispersion of pollutants. The values of these parameters are based on empirical assumptions, and might not be optimal for a specific street. In this work, we allow these parameters to vary within a certain meaningful range.We implemented two different parameter estimation schemes: a dynamic estimation procedure (using an ensemble Kalman filter) that allowed parameter values to vary, and a static estimation procedure scheme (using a least-squares algorithm) that kept parameter values fixed during the course of the simulation. We ran year-long simulations for five different streets in Danish cities, and evaluated performance by comparing forecast concentrations of NO_x, NO₂, O₃ and CO with observations.Overall, the parameter estimation substantially improved the performance of the model in forecasting, especially for NO₂ and CO. However it led to slightly more bias in the modelled daily maximum concentrations, suggesting that the parameter estimation fits to the bulk of the data rather than the extremes. Estimated parameter values varied substantially in time and between sites, making it difficult to generalise parameter estimates to other locations. Modelled concentrations from the OSPM were, on average, notably more accurate in simulations using measured urban background concentrations and meteorological parameters compared to using modelled data for these inputs. However this is only applicable when observations from nearby meteorological and urban background monitoring sites are available.We conclude that although dynamic parameter estimation has limited applicability to real-time air quality forecasting, it can potentially give useful feedback about the quality of model parameterisations or model inputs. Static parameter estimation is a simpler method, which is often as effective as dynamic parameter estimation.