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A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain Research Articles

Yun Zhu, Shuang Liang, Xiaojun Wu, Honghong Yang,yunzhu@snnu.edu.cn,xjwu@snnu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 8,   Pages 1114-1126 doi: 10.1631/FITEE.2000209

Abstract: We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set (RFS) theory. Specifically, we propose an adjusted version of the joint probabilistic data association (JPDA) filter, known as the nearest-neighbor set JPDA (NNSJPDA). The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback–Leibler divergence, with the goal of improving the accuracy of the marginalization. Next, the distribution of the target-label vector is considered. The transition matrix of the target-label vector can be obtained after the switching of the posterior density. This transition matrix varies with time, causing the propagation of the distribution of the target-label vector to follow a non-homogeneous . We show that the chain is inherently doubly stochastic and deduce corresponding theorems. Through examples and simulations, the effectiveness of NNSJPDA is verified. The results can be easily generalized to other data association approaches under the same RFS framework.

Keywords： 目标跟踪；滤波理论；随机有限集理论；贝叶斯方法；马尔可夫链    

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Elevator Configuration Based on the Markov Network Queuing Model

Zong Qun,Cheng Yiju,Song Junyuan

Strategic Study of CAE 2003, Volume 5, Issue 10,   Pages 69-72

Abstract:

The article applies the Markov network theory to build the elevator traffic model. Based on the model elevator configuration parameters of the serving stations are calculated, comparing it with traditional method, the result shows better effect of the method. Meanwhile the result shows the validity and feasibility of the method applied to elevator configuration.

Keywords： Markov network queuing theory     elevator traffic model     optimizing elevator configuration    

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Recent advances in multisensor multitarget tracking using random finite set Review Articles

Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu,dktm131@163.com,t.c.li@nwpu.edu.cn,zoyofo@163.com,fanhongqi@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 1,   Pages 1-140 doi: 10.1631/FITEE.2000266

Abstract: In this study, we provide an overview of recent advances in multisensor based on the (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic- and geometric-, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.

Keywords： Multitarget tracking     Multisensor fusion     Average fusion     Random finite set     Optimal fusion    

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Joint tracking and classification of extended targets with complex shapes Research Articles

Liping Wang, Ronghui Zhan, Yuan Huang, Jun Zhang, Zhaowen Zhuang,zhanrh@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 839-861 doi: 10.1631/FITEE.2000061

Abstract: This paper addresses the problem of (JTC) of a single with a complex shape. To describe this complex shape, the spatial extent state is first modeled by star-convex shape via a (RHM), and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation, while the Euclidean distance metric of the normalized is applied to obtain the analytical solution of the updated class probability. Consequently, the resulting method is called the “JTC-RHM method.” Besides, the proposed JTC-RHM is integrated into a framework to solve the JTC of a single in the presence of detection uncertainty and clutter, resulting in a JTC-RHM-Ber filter. Specifically, the recursive expressions of this filter are derived. Simulations indicate that: (1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly, compared with the JTC method based on the random matrix model; (2) the proposed method performs better in target state estimation than the star-convex RHM based tracking method; (3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation, and can achieve target classification correctly.

Keywords： 扩展目标；傅里叶描述子；联合跟踪与分类；随机超曲面模型；伯努利滤波器    

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Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery None

Ting-ting JIN, Xiao-qiang SHE, Xiao-lan QIU, Bin LEI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 253-264 doi: 10.1631/FITEE.1700462

Abstract:

Classification of intertidal area in synthetic aperture radar (SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value (GEV) statistical model of the polarization features and the Markov random field (MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

Keywords： Intertidal classification     Polarimetric synthetic aperture radar     Finite mixture model     Markov random field     Generalized extreme value model    

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Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang, Bin Gong, Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract:

Many properties of natural fractures are uncertain, such as their spatial distribution, petrophysical properties, and fluid flow performance. Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulation, and hence to support reservoir performance predictions. The application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic cases. In field applications, however, one of the main problems is that the Bayesian prior is falsified, because it fails to predict past reservoir production data. In this paper, we show how a global sensitivity analysis (GSA) can be used to identify why the prior is falsified. We then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate model to match the production history. We apply these two approaches to a complex fractured oil and gas reservoir where all uncertainties are jointly considered, including the petrophysical properties, rock physics properties, fluid properties, discrete fracture parameters, and dynamics of pressure and transmissibility. We successfully identify several reasons for the falsification. The results show that the methods we propose are effective in quantifying uncertainty in the modeling and flow simulation of a fractured reservoir. The uncertainties of key parameters, such as fracture aperture and fault conductivity, are reduced.

Keywords： Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

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Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables Research Articles

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1234-1246 doi: 10.1631/FITEE.2000426

Abstract:

It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with . Specifically, a is proposed where quality-oriented are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

Keywords： 软测量；有监督贝叶斯网络；隐变量；局部加权建模；质量预测    

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Forecast of fire accidents based on Grey-Markov model

Mao Zhanli,ZhuYi,Yang Bozhong,ZhuLei

Strategic Study of CAE 2010, Volume 12, Issue 1,   Pages 98-101

Abstract:

The occurrence of fire accidents is influenced by many complex factors, and it has the characteristics of random and fluctuation, so grey model and Markov model are combined together to establish a new Grey-Markov model in this paper .The paper adopts grey model and Markov model to show grey feature and random separately, at last the model is used to predict fire accidents in countryside. The result shows the forecast precision of Grey-Markov model is higher than the forecast precision of grey model, the model can satisfy the demand in forecast precision, and it can be used for fire accidents forecast.

Keywords： Grey model     Markov model     fire accidents     forecast    

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Application of Valuation Networks in Making the Decision to Build a Reactor for Electric Power Company

Wang Huating,Feng Junweng,Gao Peng ,Wang Jian

Strategic Study of CAE 2007, Volume 9, Issue 12,   Pages 4-9

Abstract:

How to use experience and test result to make the best choice is a decision problem for a electric power company when deciding to build a reactor,  and valuation networks is a new method for representing and solving Bayesian decision problems. This article used Valuation Networks to analyze the reactor problem of a given company,  which valuation network representing and solving process is demonstrated in detail.

Keywords： decision theory     valuation networks     influence diagrams     Bayesian decision    

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The application of advanced threshold denoising tothe MMW target radiation signal

Fan Qinghui,Li Xingguo

Strategic Study of CAE 2008, Volume 10, Issue 7,   Pages 153-157

Abstract:

In this paper based on the characteristics of millimeter wave radiation signal for wavelet transform, non-negative wavelet coefficient is used as the wavelet coefficient of the signal. For a given threshold value, the wavelet coefficient which is smaller than the threshold is set zero and the wavelet coefficient which is larger than the threshold is set the difference between the coefficient and a constant a.The method for valuing a is inferred by the variance function of signal, and the experiments show that it has good ability of removing the noise in MMW target radiation signal.

Keywords： The application of advanced threshold denoising tothe MMW target radiation signal    

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Capacity of VoIP in IEEE 802.11 Wireless LAN

Chen Liquan,Hu Aiqun,Zhou Xueli

Strategic Study of CAE 2005, Volume 7, Issue 7,   Pages 81-85

Abstract:

Voice transmitted over wireless LAN faces serious challenge because of the fluctuation of the wireless channel. In this paper, the transmission of voice over wireless LAN is firstly analyzed. Taking collision probability into account, the stochastic analysis based on a Markov chain to calculate the upper bound number of simultaneous VoIP calls that can be supported in a single cell of an IEEE 802. llb/a/g network is proposed. The upper bound capacities of G. 711, G. 729 and G. 723.1 code transmitted over 802. llb/a/g are figured out. The simulation results from NS2 simulator have validated the analysis values.

Keywords： wireless LAN     voice over IP     capacity     Markov chain    

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Modeling and Analysis of Fault-tolerant System With Rejuvenation

Guo Chenghao,Liu Fengyu

Strategic Study of CAE 2007, Volume 9, Issue 10,   Pages 75-79

Abstract:

A fault-tolerant system experiences a crash due to hardware components' faults or progressive performance degradation as an "aging"  phenomenon,  because of running continuously for very long periods.  This paper considers both hardware components' faults and "aging"  phenomenon, proposes composing redundant structure and rejuvenation schedule in the fault-tolerant system, formalizes the system with Non-Markovian stochastic Petri nets,  and evaluates quantitatively the performance of a system based on this model.

Keywords： fault-tolerant system     software rejuvenation     software aging     redundant strategy     Non-Markovian stochastic Petri nets    

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A two-echelon （S-1，S） inventory model for repairable items based on markovian arrival process

Chen Tong,Li Fang and Di Peng

Strategic Study of CAE 2015, Volume 17, Issue 5,   Pages 113-119

Abstract:

This paper investigates a two-echolon inventory system with （S-1，S） policy that consists of several same repairable items and single repair facility， and assumes that the item demand occur according to a markovian arrival process （MAP）， the repair time， ship time and procurement time follow the general distribution which is represented by phase-type （PH） distribution. Then a inventory optimization model with better description ability and analytical performance is given， and the probability distribution of backorder is obtained. Finally， a numerical example was given to illustrate the effectiveness of the model.

Keywords： （S-1，S） policy；two-echelon inventory；repairable item；markovian arrival process    

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ApproximateGaussian conjugacy: parametric recursive filtering under nonlinearity,multimodality, uncertainty, and constraint, and beyond Review

Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1913-1939 doi: 10.1631/FITEE.1700379

Abstract: Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity.

Keywords： Kalman filter     Gaussian filter     Time series estimation     Bayesian filtering     Nonlinear filtering     Constrained filtering     Gaussian mixture     Maneuver     Unknown inputs    

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Performance analysis of the stop-and-wait automatic repeat request protocol under Markovian interruptions Regular Papers-Research Articles

Dashdondov KHONGORZUL, Yong-Ki KIM, Mi-Hye KIM

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1296-1306 doi: 10.1631/FITEE.1700185

Abstract: The performance of an integrated packet voice/data multiplexer using a stop-and-wait (SW) automatic repeat request (ARQ) protocol is discussed. We assume that the input for the data traffic is exponentially distributed in increments via the Poisson process, with each data packet transmitted within an individual slot time. Another assumption is that there is only a single voice signal, which has a higher priority over the data packet, and whose traffic is given via an on-off Markov process. Whenever the voice signal is active, the output link is used and will be blocked for the data packet. We introduce the concept of buffer occupancy to simplify the analysis, and discover that data multiplexers using the SW ARQ protocol exhibit a behavior of queueing delay and buffering when the interruption signal is given via a Markov process. Simulation results verify the validity of the analytical results.

Keywords： Stop-and-wait ARQ protocol     Markovian interruptions     Poisson distribution     Buffer occupancy     Waiting time    

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Title Author Date Type Operation

A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain

Yun Zhu, Shuang Liang, Xiaojun Wu, Honghong Yang,yunzhu@snnu.edu.cn,xjwu@snnu.edu.cn

Journal Article

Elevator Configuration Based on the Markov Network Queuing Model

Zong Qun,Cheng Yiju,Song Junyuan

Journal Article

Recent advances in multisensor multitarget tracking using random finite set

Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu,dktm131@163.com,t.c.li@nwpu.edu.cn,zoyofo@163.com,fanhongqi@nudt.edu.cn

Journal Article

Joint tracking and classification of extended targets with complex shapes

Liping Wang, Ronghui Zhan, Yuan Huang, Jun Zhang, Zhaowen Zhuang,zhanrh@nudt.edu.cn

Journal Article

Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

Ting-ting JIN, Xiao-qiang SHE, Xiao-lan QIU, Bin LEI

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang, Bin Gong, Jef Caers

Journal Article

Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables

Journal Article

Forecast of fire accidents based on Grey-Markov model

Mao Zhanli,ZhuYi,Yang Bozhong,ZhuLei

Journal Article

Application of Valuation Networks in Making the Decision to Build a Reactor for Electric Power Company

Wang Huating,Feng Junweng,Gao Peng ,Wang Jian

Journal Article

The application of advanced threshold denoising tothe MMW target radiation signal

Fan Qinghui,Li Xingguo

Journal Article

Capacity of VoIP in IEEE 802.11 Wireless LAN

Chen Liquan,Hu Aiqun,Zhou Xueli

Journal Article

Modeling and Analysis of Fault-tolerant System With Rejuvenation

Guo Chenghao,Liu Fengyu

Journal Article

A two-echelon （S-1，S） inventory model for repairable items based on markovian arrival process

Chen Tong,Li Fang and Di Peng

Journal Article

ApproximateGaussian conjugacy: parametric recursive filtering under nonlinearity,multimodality, uncertainty, and constraint, and beyond

Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO

Journal Article

Performance analysis of the stop-and-wait automatic repeat request protocol under Markovian interruptions

Dashdondov KHONGORZUL, Yong-Ki KIM, Mi-Hye KIM

Journal Article