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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

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

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

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...

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

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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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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 100080 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

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

Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

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Nuclear Science and Techniques 2023年第8期34卷 174-193页

作者： Assem Abdelhakim Department of Radiation Engineering National Center for Radiation Research and TechnologyEgyptian Atomic Energy AuthorityCairo 11787Egypt

maximum likelihood estimation(MLE)is an effective method for localizing radioactive sources in a given area.However,it requires an exhaustive search for parameter estimation,which is time-consuming.In this study,heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors.Hence,the time consumption of MLE would be effectively reduced.First,the radiation source was detected using the k-sigma method.Subsequently,the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source.A comparative study was performed in which the estimation accuracy and time consump-tion of the MLE were evaluated for traditional methods and heuristic techniques.The traditional MLE was performed via a grid search method using fixed and multiple resolutions.Additionally,four commonly used heuristic algorithms were applied:the firefly algorithm(FFA),particle swarm optimization(PSO),ant colony optimization(ACO),and artificial bee colony(ABC).The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program.The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE.The time consumption for the heuristic-based MLE was 0.75,0.03,0.02,and 0.059 s for FFA,PSO,ACO,and ABC,respectively.The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE.Hence,heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.

关键词： Radioactive source maximum likelihood estimation Multi-resolution MLE k-sigma Firefly algorithm Particle swarm optimization Ant colony optimization Artificial bee colony

Passive localization by a single moving observer using TOA only with unknown SRI:Observability analysis and algorithm

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Chinese Journal of Aeronautics 2023年第6期36卷 318-331页

作者： Min ZHANG Yuhao PEI Xi LI Fucheng GUO College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China

Passive localization by a single moving observer using Time of Arrival(TOA)only with an unknown Signal Repetition Interval(SRI)is investigated in this paper.Observability analysis is performed first.The observability condition for uniquely determining the emitter position and SRI is derived.The conditional Cramer-Rao Lower Bound(CRLB)is also analyzed.It is found that the ambiguity of the SRI integer of the first TOA does not affect the theoretical estimation precision of the emitter position and SRI.A Reference-Fixed Differential TOA(RFDTOA)-based Iterative maximum likelihood Estimator(IMLE)is proposed,which only needs O(M)computational operations.Theoretical analysis and simulation results show that the Mean Square Error(MSE)of the proposed algorithm could attain the CRLB with moderate Gaussian measurement noise.

关键词： Cramer-Rao lower bound maximum likelihood estimation Observability analysis Passive localization Signal Repetition Interval(SRI) Time of Arrival(TOA)

A practical filter error method for aerodynamic parameter estimation of aircraft in turbulence

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Chinese Journal of Aeronautics 2023年第2期36卷 17-28页

作者： Qing WANG Fengqi ZHENG Weiqi QIAN Di DING State Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Computational Aerodynamics Institute China Aerodynamics Research and Development CenterMianyang 62100China

It is common for aircraft to encounter atmospheric turbulence in flight tests.Turbulence is usually modeled as stochastic process noise in the flight dynamics equations.In this paper,parameter estimation of nonlinear dynamic system with both process and measurement noise was studied,and a practical filter error method was proposed.The linearized Kalman filter of first-order approximation was used for state estimation,in which the filter gain,along with the system parameters and the initial states,constituted the parameter vector to be estimated.The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method,and the sensitivities of observations to unknown parameters were calculated by finite difference approximation.Some practical aspects of the method application were discussed.The proposed filter error method was validated by the flight simulation data of a research aircraft.Then,the method was applied to the flight tests of a subscale aircraft,and the aerodynamic stability and control derivatives were estimated.All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach.It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.

关键词： Aircraft aerodynamics Atmosphere turbulence Flight tests Kalman filter maximum likelihood estimation Measurement noise Parameter estimation Stability and control derivatives