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Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

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控制理论与应用（英文版） 2013年第1期11卷 27-34页

作者： Xingbo WANG Huanshui ZHANG Minyue FU School of Control Science and Engineering Shandong University School of Electrical Engineering and Computer Science University of Newcastle

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.

关键词： Target tracking Wireless sensor network maximum likelihood estimation Kalman filtering Fisher information matrix Sensor selection

Equivalent point estimation for small target groups tracking based on maximum group likelihood estimation

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Science China(Information Sciences) 2020年第8期63卷 260-262页

作者： Chao ZHOU Rui WANG Cheng HU Radar Research Laboratory School of Information and ElectronicsBeijing Institute of Technology Key Laboratory of Electronic and Information Technology in Satellite Navigation Ministry of Education

Dear editor,Small target groups (STG), such as insect/bird [1]flocks and drone groups [2], have become new threats to civil aviation and urban security [3].With the advantages of long range and alltime/all-weather operation capability, radar is one of the most suitable technologies for STG detection and tracking [4, 5]. However, because of the special group motion characteristics (orderly as a whole and flexibly as individuals), tradi-

关键词： group tracking equivalent point maximum likelihood estimation structure information dynamic countour

Adaptive speckle reduction of ultrasound images based on maximum likelihood estimation

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Chinese Optics Letters 2004年第1期2卷 24-26页

作者：刘旭黄永锋寿文德应涛 Department of Biomedical Engineering Shanghai Jiaotong University Shanghai 200030 Department of Ultrasound Imaging Shanghai Sixth People's Hospital Shanghai 200233

A method has been developed in this paper to gain effective speckle reduction in medical ultrasound images. To exploit full knowledge of the speckle distribution, here maximum likelihood was used to estimate speckle parameters corresponding to its statistical mode. Then the results were incorporated into the nonlinear anisotropic diffusion to achieve adaptive speckle reduction. Verified with simulated and ultrasound images, we show that this algorithm is capable of enhancing features of clinical interest and reduces speckle noise more efficiently than just applying classical filters. To avoid edge contribution, changes of contrast-to-noise ratio of different regions are also compared to investigate the performance of this approach.

关键词： maximum likelihood estimation Speckle Ultrasonic imaging Wave filters

BOOTSTRAP　maximum　likelihood　estimation　OF　THE　PARAMETER　IN　SPECTRAL　DENSITY　OF　STATIONARY　PROCESSES

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Acta Mathematicae Applicatae Sinica 1996年第3期12卷 225-233页

作者：于丹 Institute of Systems Science Chinese Academy of Sciences Beijing China

This paper is concerned with the estimation of parameters in spectral density of a stationary process. A new estimate of the parameter is proposed by using the bootstrap method. We call it the bootstrap maximum likelihood estimate (BMLE). In this paper, under suitable regularity conditions, the BMLE is shown to be consistent and asymptotically normal.

关键词： Bootstrap maximum likelihood estimation spectral density stationary process

Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 866-877页

作者： Elham Javanfar Mehdi Rahmani the Department of Electrical Engineering Imam Khomeini International UniversityQazvin ***Iran

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

关键词： Data-based filter maximum likelihood estimation unknown covariance weighted maximum likelihood estimation weighted sum-of-norms clustering

Convergence Diagnostics for Gibbs Sampler via maximum likelihood estimation

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Journal of Beijing Institute of Technology 2003年第2期12卷 212-215页

作者：程杞元林秀光 Department of Mathematics

A diagnostic procedure based on maximum likelihood estimation, to study the convergence of the Markov chain produced by Gibbs sampler, is presented. The unbiasedness, consistent and asymptotic normality are considered for the estimation of the parameters produced by the procedure. An example is provided to illustrate the procedure, and the numerical result is consistent with the theoretical one.

关键词： Markov chain Monte Carlo Gibbs sampler maximum likelihood estimation

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

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Open Journal of Modelling and Simulation 2024年第2期12卷 33-42页

作者： Yisa Adeniyi Abolade Yichuan Zhao Department of Mathematics and Statistics Georgia State University Atlanta Georgia USA

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

关键词： Compositional Data Linear Regression Model Least Square Method Robust Least Square Method Synthetic Data Aitchison Distance maximum likelihood estimation Expectation-Maximization Algorithm k-Nearest Neighbor and Mean imputation

Collaborative Target Tracking in WSNs Based on maximum likelihood estimation and Kalman Filter

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Collaborative Target Tracking in WSNs Based on Maximum Likel...

基于数据驱动的一类具有信息效应的传染病模型研究

第三十届中国控制会议

作者： WANG Xingbo,ZHANG Huanshui,and JIANG Xiangyuan School of Control Science and Engineering,Shandong University,Jingshi Road 73,Jinan,250061,P.R.China

＜正＞Target tracking using wireless sensor networks requires efficient collaboration among sensors.Existing collaborative tracking approaches based on the extended Kalman filter,as addressed in many previous papers,suffer from low tracking accuracy. In this paper,we present a new collaborative target tracking approach in wireless sensor networks based on the combination of maximum likelihood estimation and Kalman filtering.The leader firstly converts the nonlinear measurements collected from the scheduled sensors into a linear observation model in target state using maximum likelihood estimation-based localization, then applies a standard Kalman filter to recursively update the current target state and predict the future target location.Lastly, an information measure based on the Fisher information matrix（FIM） is proposed to select the most informative sensors and one of them is designated as the leader for the next time tracking so as to achieve more tracking accuracy under the energy constraint.

关键词： Collaborative Target Tracking maximum likelihood estimation Kalman Filtering Fisher Information Matrix Sensor Selection

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