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Networked H∞ filtering for discrete-time nonlinear singular system with mixed random delays and packet dropouts

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Networked H∞ filtering for discrete-time nonlinear singular...

A Low Complexity Linear Moving Average filtering Technique for PAR Reduction in OFDM Systems

第36届中国控制会议

作者： Qiyi Xu Yijun Zhang Shunyuan Xiao School of Automation Nanjing University of Science and Technology

The H filtering design problem is studied for a class of networked discrete-time nonlinear singular system.Both mixed random delays and packet dropouts in the communication channels are considered.The mixed time-delays consists of the discrete and distributed delays.And the packet dropout phenomenon occurs in a random way.Three mutually independent Bernoulli distributed random variables are utilized to model the stochastic phenomena.By using the Lyapunov-Kravoskii stability approach,sufficient conditions are presented to guarantee the closed-loop filter error system is regular,causal and stable with a certain H performance index.And the H filtering design method is provided.An illustrative example is provided to illustrate the effectiveness of the proposed filter design method.

关键词： Networked system filtering singular system mixed delays packet dropouts

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International Journal of Communications, Network and System Sciences 2018年第10期11卷 199-215页

作者： Hassan Ali Raziq Yaqub Department of Electrical Engineering and Computing The University of Newcastle Callaghan Australia Department of Electrical Engineering and Computer Science Alabama A&M University Normal AL USA

In this paper, a linear moving average recursive filtering technique is proposed to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. The proposed low complexity technique is analyzed in an oversampled OFDM system and a simple distribution approximation of the oversampled and linearly filtered OFDM signals is also proposed. Corresponding time domain linear equalizers are developed to recover originally transmitted data symbols. Through extensive computer simulations, effects of the new filtering technique on the oversampled OFDM peak-to-average power ratio (PAR), power spectral density (PSD) and corresponding linear equalizers on the frequency selective Rayleigh fading channel transmission symbol-error-rate (SER) performance are investigated. The newly proposed recursive filtering scheme results in attractive PAR reduction, requires no extra fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) operations, refrains from transmitting any side information, and reduces out-of-band radiation. Also, corresponding linear receivers are shown to perform very close to their frequency domain counterparts.

关键词： Moving Average filtering Recursive filtering Orthogonal Frequency Division Multiplexing Peak-to-Average-Power Ratio Inverse Fast Fourier Transform

Uncertainty and filtering of hidden Markov models in discrete time

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Probability, Uncertainty and Quantitative Risk 2020年第1期5卷 68-101页

作者： Samuel N.Cohen Mathematical Institute University of OxfordWoodstock RoadOxfordUK

We consider the problem of filtering an unseen Markov chain from noisy observations,in the presence of uncertainty regarding the parameters of the processes involved.Using the theory of nonlinear expectations,we describe the uncertainty in terms of a penalty function,which can be propagated forward in time in the place of the filter.We also investigate a simple control problem in this context.

关键词： filtering Optimal control Robustness Nonlinear expectation

Occlusion Robust Low-Contrast Sperm Tracking Using Switchable Weight Particle filtering

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Advances in Sexual Medicine 2014年第3期4卷 42-54页

作者： Mohammadreza Ravanfar Leila Azinfar Mohammad Hassan Moradi Reza Fazel-Rezai Amirkabir University of Technology Tehran Iran

Sperm motility analysis has a particular place in male fertility diagnosis. Computerized sperm tracking has an important role in extracting sperm trajectory and measuring sperm’s dynamic features. Due to free movements of sperms in three dimensions, occlusion has remained a challenging problem in this area. This paper aims to present a robust single sperm tracking method being able to handle misdetections in sperm occlusion scenes. In this paper, a robust method of segmentation was utilized to provide the required measurements for a switchable weight particle filtering which was designed for single sperm tracking. In each frame, the target sperm was categorized in one of these three stages: before occlusion, occlusion, and after occlusion where the occlusion had been detected based on sperm’s physical characteristics. Depending on the target sperm stage, particles were weighted differently. In order to evaluate the algorithm, two groups of samples were studied where an expert had selected a single sperm of each sample to track manually and automatically. In the first group, the sperms with no occlusion along their trajectories were tracked to depict the general compatibility of the algorithm with sperm tracking. In the second group, the algorithm was applied on the sperms which had at least one occlusion during their path. The algorithm showed an accuracy of 95% on the first group and 86.66% on the second group which illustrate the robustness of the algorithm against occlusion.

关键词： Sperm Tracking Particle filtering Object Occlusion Watershed Algorithm

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混凝土泵活塞位移小波滤波方法研究

机械设计与制造 2010年第12期 116-117页

作者：刘会勇李伟彭秀英胡园赵青贵州大学机械工程与自动化学院贵阳550003 浙江大学流体传动及控制国家重点实验室杭州310027 贵州大学土木建筑工程学院贵阳550003

通过选择合适的小波基和阈值规则,对混凝土泵工作过程中的活塞位移测量信号进行小波分解和滤波处理,有效去除了活塞位移测量信号中的噪声,取得了良好的滤波效果,为混凝土泵实时监控和故障诊断提供处理和识别依据。

关键词：混凝土泵活塞位移小波滤波方法研究 method of wavelet filtering 测量信号阈值规则小波分解实时监控识别依据滤波效果滤波处理故障诊断工作过程小波基噪声

Sensor Fusion with Square-Root Cubature Information filtering

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Intelligent Control and Automation 2013年第1期4卷 11-17页

作者： Ienkaran Arasaratnam Ford Motor Company of Canada Ltd. Windsor ON Canada

This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.

关键词： Kalman Filter Information Filter Multi-Sensor Fusion Square-Root filtering

Adaptive “Cubature and Sigma Points” Kalman filtering Applied to MEMS IMU/GNSS Data Fusion during Measurement Outlier

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Journal of Sensor Technology 2013年第4期3卷 115-125页

作者： Hamza Benzerrouk Hassen Salhi Alexander Nebylov International Institute for Advanced Aerospace Technologies of Saint-Petersburg State University of Aerospace Instrumentation 67 Bolshaya Morskaya Saint Petersburg Russia SET Laboratory (Systèmes Electriques et Télecommande) of Electronic Department of Saad Dahlab University of Blida Soumaa Algeria

In this paper, adaptive sensor fusion INS/GNSS is proposed to solve specific problem of non linear time variant state space estimation with measurement outliers, different algorithms are used to solve this specific problem generally occurs in intentional and non-intentional interferences caused by other radio navigation sources, or by the GNSS receiver’s deterioration. Non linear approximation techniques such as Extended Kalman filter EKF, Sigma Point Kalman Filters such as UKF and CDKF are computed to estimate the navigation states for UAV flight control. Several comparisons are conduced and analyzed in order to compare the accuracy and the convergence of different approaches usually applied in navigation data fusion purposes. The last non linear filter algorithm developed is the Cubature Kalman Filter CKF which provides more accurate estimation with more stability in Tracking data fusion application. In this work, CKF is compared with SPKF and EKF in ideal conditions and during GNSS outliers supposed to occur during specific interval of time, innovation based adaptive approach is selected and used to modify the covariance calculation of the non linear filters performed in this paper. Interesting results are observed, discussed with real perspectives in navigation data fusion for real time applications. Three parallel modified algorithms are simulated and compared to non-adaptive forms according to Root Mean Square Error (RMSE) criteria.

关键词： IMU MEMS GPS GNSS Kalman filtering Cubature Rule Sigma Points Unscented Kalman Filter

Generalized Parseval’s Theorem on Fractional Fourier Transform for Discrete Signals and filtering of LFM Signals

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Journal of Signal and Information Processing 2013年第3期4卷 274-281页

作者： Xiaotong Wang Guanlei Xu Yue Ma Lijia Zhou Longtao Wang Automation Department of Dalian Naval Academy Dalian China

This paper investigates the generalized Parseval’s theorem of fractional Fourier transform (FRFT) for concentrated data. Also, in the framework of multiple FRFT domains, Parseval’s theorem reduces to an inequality with lower and upper bounds associated with FRFT parameters, named as generalized Parseval’s theorem by us. These results theoretically provide potential valuable applications in filtering, and examples of filtering for LFM signals in FRFT domains are demonstrated to support the derived conclusions.

关键词： Discrete Fractional Fourier Transform (DFRFT) Uncertainty Principle Frequency-Limiting Operator Linear Frequency-Modulation (LFM) Signal filtering

State Estimation for Non-linear Sampled-Data Descriptor Systems:A Robust Extended Kalman filtering Approach

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Journal of Harbin Institute of Technology(New Series) 2019年第5期26卷 24-31页

作者： Mao Wang Tiantian Liang Zhenhua Zhou Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin 150001 China Changzhou Vocational Institute of Light Industiy Technology Changzhou 213000 Jiangsu China

This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.

关键词： sampled-data system descriptor system state estimation Kalman filtering REKF