Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory

This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems.     

Parameter estimation for a class of radial basis function-based nonlinear time-series models with moving average noises

This paper focuses on the parameter estimation for radial basis function-based state-dependent autoregressive models with moving average noises (RBF-ARMA models). An extended projection algorithm is derived based on the negative gradient search. In order to reduce the sensitivity of the algorithm to noise and reduce the fluctuations of the parameter estimation errors, a modified extended stochastic gradient algorithm is proposed. By introducing a moving data window, a modified moving data window-based extended stochastic gradient algorithm is further developed to improve the parameter estimation accuracy. The simulation results show that the proposed algorithms can effectively estimate the parameters of the RBF-ARMA models.     

Blind estimation of carrier frequency offset for OFDM systems with time-varying DC offset

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in radar and communication systems. High sensitivity to carrier frequency offset (CFO) is one of the major drawbacks of OFDM. CFO estimation for OFDM systems had been extensively studied and various algorithms had been proposed. However, the established algorithms may be compromised by the adoption of direct-conversion architecture and multi-mode low noise amplifier in the OFDM receiver, which introduces time-varying direct current offset (TV-DCO) into the system. In our previous study, we developed an eigen-decomposition based estimation algorithm, which is robust to TV-DCO but suffers from performance degradation under low to medium signal-to-noise ratio and requires high computation efforts. To address those issues, we in this paper propose a novel blind CFO estimation algorithm. By making use of the second order differential filtering and subspace method, the proposed algorithm achieves great performance improvement with reduced complexity. The performance of the proposed algorithm is demonstrated by simulations.     

The data-filtering based bias compensation recursive least squares identification for multi-input single-output systems with colored noises

For the multi-input single-output (MISO) system corrupted by colored noise, we transform the original system model into a new MISO output error model with white noise through data filtering technology. Based on the newly obtained model and the bias compensation principle, a novel data filtering-based bias compensation recursive least squares (BCRLS) identification algorithm is developed for identifying the parameters of the MISO system with colored noise disturbance. Unlike the exiting BCRLS method for the MISO system (see, in Section 3), without computing the complicated noise correlation functions, still the proposed method can achieve the unbiased parameters estimation of the MISO system in the case of colored process noises. The proposed algorithm simplifies the implementation of and further expands the application scope of the existing BCRLS method. Three numerical examples clearly illustrate the validity of and the good performances of the proposed method, including its superiority over the BCRLS method and so on.     

Identification of errors-in-variables ARX models using modified dynamic iterative PCA

Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed Dynamic Iterative Principal Components Analysis (DIPCA) technique solves the EIV identification problem but is only applicable to white measurement errors. We propose a novel identification algorithm based on a modified DIPCA approach for identifying the EIV-ARX model for single-input, single-output (SISO) systems where the output measurements are corrupted with coloured noise consistent with the ARX model. Most of the existing methods assume important parameters like input-output orders, delay, or noise-variances to be known. This work’s novelty lies in the joint estimation of error variances, process order, delay, and model parameters. The central idea used to obtain all these parameters in a theoretically rigorous manner is based on transforming the lagged measurements using the appropriate error covariance matrix, which is obtained using estimated error variances and model parameters. Simulation studies on two systems are presented to demonstrate the efficacy of the proposed algorithm.     

Distributed MPC-based adaptive control for linear systems with unknown parameters

This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique.     

Output feedback sliding-mode control based on dynamic-gain observer for non-minimum phase systems

This paper considers the output feedback sliding-mode control for an uncertain linear system with unstable zeros. Based on a frequency shaping design, a dynamic-gain observer is used for state estimation of an uncertain system. This paper confirms that (1) state estimation is globally stable in a practical sense, (2) the resultant error can be arbitrarily small with respect to the system uncertainties, and (3) the proposed sliding-mode control can drive the uncertain system state into an arbitrarily small residual set around the origin, such that the size of residual set is controlled by the filter design. Moreover, the proposed control design is inherently robust to measurement noise; the effect of measurement noise can effectively be attenuated without any additional work.     

Learning soft mask with DNN and DNN-SVM for multi-speaker DOA estimation using an acoustic vector sensor

Using an acoustic vector sensor (AVS), an efficient method has been presented recently for direction of arrival (DOA) estimation of multiple speech sources via the clustering of the inter-sensor data ratio (AVS-ISDR). Through extensive experiments on simulated and recorded data, we observed that the performance of the AVS-DOA method is largely dependent on the reliable extraction of the target speech dominated time–frequency points (TD-TFPs) which, however, may be degraded with the increase in the level of additive noise and room reverberation in the background. In this paper, inspired by the great success of deep learning in speech recognition, we design two new soft mask learners, namely deep neural network (DNN) and DNN cascaded with a support vector machine (DNN-SVM), for multi-source DOA estimation, where a novel feature, namely, the tandem local spectrogram block (TLSB) is used as the input to the system. Using our proposed soft mask learners, the TD-TFPs can be accurately extracted under different noisy and reverberant conditions. Additionally, the generated soft masks can be used to calculate the weighted centers of the ISDR-clusters for better DOA estimation as compared to the original center used in our previously proposed AVS-ISDR. Extensive experiments on simulated and recorded data have been presented to show the improved performance of our proposed methods over two baseline AVS-DOA methods in presence of noise and reverberation.     

相控阵雷达反演口径分布中的误差分析

为了预估相控阵雷达各部件的非理想性对反演天线口径幅相分布的影响 ,分析了相控阵天线移相器插损和移相误差、接收机噪声、接收机非线性和测相误差、A/D变换位数等对时序 Walsh- Hadamard相位权重反演天线口径幅相分布的影响。理论分析表明 ,Walsh- Hadamard变换对相控阵雷达的天线和接收机的误差和噪声有一定程度的平滑和抑制作用。在此基础上 ,以实际使用为背景 ,对各个环节的误差提出了要求。这些要求一般的雷达以及目前的技术都能够达到 ,因而系统的实现是可行的。     

On purposeful systems : Russelle L. Ackoff and Fred E. Emery. 288 pages,diagrams, 6x9in., Aldine-Atherton; Chicago,I11., Tavistock,London, 1972. Price, $12.95 (£3.50).

A closed-form analytical expression for evaluation of the output noise voltage of active-R filters has been developed. The expression is applicable to any low-pass, band-pass or high-pass two amplifier second order active-R realization. The expression for the noise voltage has been derived in terms of the filter specifications and parameters of noise sources contained within the filter. Thus, it is very convenient for practical calculations. The expression is also useful for obtaining a design that minimizes the magnitude of the output noise. Experimental measurements indicate close agreement with the theroetical analysis.     

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.     

基于空间平滑技术的高阶累积量空间谱估计

提出基于空间平滑技术的高阶累积量空间谱估计。在经典的空间谱估计中,均利用信号的二阶统计量,并且假设噪声是白噪声。在实际应用场合,噪声通常是色噪声,这时采用高阶统计量可以获得比二阶矩更理想的性能。本文利用空间平滑技术对单一的高阶累积量算法进行改进。新算法具有同时测向能力、测向精度高、超分辨能力,能在低信噪比环境中取得好的性能。计算机仿真证明了该方法的有效性。     

Performance of the stochastic MV-PURE estimator in highly noisy settings

The stochastic minimum-variance pseudo-unbiased reduced-rank estimator (stochastic MV-PURE estimator) has been developed to provide linear estimation with robustness against high noise levels, imperfections in model knowledge, and ill-conditioned systems. In this paper, we investigate the theoretical performance of the stochastic MV-PURE estimator under varying levels of additive noise. We prove that the mean-square-error (MSE) of this estimator in the low signal-to-noise (SNR) region is much smaller than that obtained with its full-rank version, the minimum-variance distortionless estimator, and the gap becomes larger as the noise level increases. These results shed light on the excellent performance of the stochastic MV-PURE estimator in highly noisy settings obtained in simulations so far. Furthermore, we extend previous numerical simulations to show how the insight gained from the results of this paper can be used in practice.     

Moving horizon estimation for multirate systems with time-varying time-delays

This paper presents a moving horizon estimation approach for the multirate sampled-data system with unknown time-delay sequence. To estimate the unknown variables of interest, two main challenging issues need to be addressed: (a) synthesizing the multirate input and output data for state estimation, (b) simultaneously estimating the continuous state and discrete time-delay sequence. In this work a moving horizon estimation based approach is developed to tackle these issues. The proposed approach can simultaneously estimate both the continuous states and discrete time-delay sequence for dynamic systems. The effects of different noise level on the estimation of continuous states and discrete time-delay sequence are analyzed. The effectiveness of this method is illustrated through a simulation study.     

Combined state and parameter estimation for a bilinear state space system with moving average noise

This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods.     

Parameters estimation using sliding mode observer with shift operator

In different areas of engineering, mathematical models are used to describe real life phenomena and experiments are conducted to validate them. It is common that these models may contain a number of parameters that cannot be measured directly or calculated. Thus, parameter estimation is an important step in the process of modeling based on empirical data of the system.In the control system’s literature, one can find considerable amount of research in the area of system parameters identification. Most of these techniques are based on minimizing the estimation error in some statistical framework such as least square error based methods. In most cases, using these techniques, one can prove the uniform exponential stability of the state and parameter estimation error, but the convergence rate can be too low. However, when designing control systems, knowledge of unknown immeasurable (or even time varying) parameters might be crucial for the operation of the controller and thus have to be accurately estimated with a desired rate of convergence. In this paper, we demonstrate a way to provide an estimation technique with tunable convergence rate using sliding mode with linear operators such as time delay.     

异步电动机直接转矩控制系统研究

传统的感应电机直接转矩控制系统存在转矩脉动大等缺点,为提高感应电机直接转矩控制系统的动态性能,采用一种改进的转矩估算模型,在推导其数学模型的基础上,采用MATLAB/SIMULINK仿真软件对感应电机新型直接转矩控制系统进行建模与仿真,仿真实验研究结果表明：采用改进的转矩估算模型可以提高异步电动机直接转矩控制系统的动态性能。     

Fuzzy-logic adaptive variable structure multiple-model algorithm for tracking a high maneuvering target

The conventional interacting multiple model (IMM) algorithm will increase the computational load when applying a large number of models, meanwhile, it cannot yield accurate estimation results with a small number of models. Furthermore, the unknown target acceleration is regarded as an additional process noise to the target model, and its time-varying variance is hard to be approximated. The paper proposes a fuzzy-logic adaptive variable structure multiple model (FAVSMM) algorithm for tracking a high maneuvering target. The algorithm can optimize the model parameters using the model probability and construct an optimal model set quickly, and the fuzzy-logic IMM algorithm included in the FAVSMM algorithm is adopted for states estimation. The simulation results show that the proposed algorithm can match well with the actual target trajectory with less computational complexity and better accuracy.     

基于特征缺失修复的人脸三维运动形变估计算法

人脸由于其非刚性明显,受到运动背景变化、形变程度复杂、特征丢失等因素的影响,使得在进行三维动态人脸形变估计时,面临着估计不准,误差较大的问题。为了解决这些问题,提出一种基于特征缺失修复的人脸三维运动形变估计算法,将非刚体人脸运动的形变估计过程中,运用特征补偿方法,补偿由于噪声、形变程度复杂带来的特征丢失缺陷,运用较多的人脸形变特征进行形变分析,准确计算人脸的形变程度。实验结果表明,这种方法得到的解误差较小,效果明显。     

Subspace design of low-rank estimators for higher-order statistics

Higher-order statistics (HOS) are well known for their robustness to additive Gaussian noise and ability to preserve phase. HOS estimates, on the other hand, have been criticized for high complexity and the need for long data in order to maintain small variance. Since rank reduction offers a general principle for reduction of estimator variance and complexity, we consider the problem of designing low-rank estimators for HOS. We propose three methods for choosing the transformation matrix that reduces the mean-square error (MSE) associated with the low-rank HOS estimates. We also demonstrate the advantages of using low-rank third-order moment estimates for blind system estimation. Results indicate that the full rank MSE corresponding to some data length N can be attained by a low-rank estimator corresponding to a length significantly smaller than N.