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An improved particle filtering algorithm based on observation inversion optimal sampling

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Journal of Central South University 2009年第5期16卷 815-820页

作者：胡振涛潘泉杨峰程咏梅 College of Automation Northwestern Polytechnical University

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter,an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly,virtual observations were generated from the latest observation,and two sampling strategies were presented. Then,the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally,the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method,sampling particles can make full use of the latest observation information and the priori modeling information,so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter,the extended Kalman particle filter and the unscented particle filter.

关键词： particle filter proposal distribution re-sampling observation inversion

Multi-target state-estimation technique for the particle probability hypothesis density filter

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Science China(Information Sciences) 2012年第10期55卷 2318-2328页

作者： LIN LiangKui 1,2,XU Hui 1,SHENG WeiDong 1 & AN Wei 1 1 School of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China 2 95507 Unit,Chinese People's Liberation Army,Guiyang 550031,China School of Electronic Science and Engineering National University of Defense Technology Changsha China 95507 Unit Chinese People’s Liberation Army Guiyang China

A simple yet effective state-estimation algorithm is presented and demonstrated to have advantages over previous standard clustering techniques used for the particle probability hypothesis density *** idea behind the proposed algorithm is that it uses the latest available information(i.e.,the measurements) to direct particle *** particle likelihood and target number estimation,computed during probability hypothesis density recursion,are both used to partition particles into clusters,and the center of each cluster gives the state estimation of an individual *** results indicate that the proposed algorithm outperforms the standard clustering approach using the k-means algorithm,achieving higher accuracy and shorter computational time.

关键词： probability hypothesis density state estimation particle filter multi-target tracking

Target Tracking in Standoff Jammer Using Unscented Kalman filter and particle Fiter with Negative Information

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Journal of Shanghai Jiaotong university(Science) 2014年第2期19卷 181-189页

作者：侯静景占荣羊彦 College of Electronic and Information Northwestern Polytechnical University

To handle the problem of target tracking in the presence of standoff jamming(SOJ), a Gaussian sum unscented Kalman filter(GSUKF) and a Gaussian sum particle filter(GSPF) using negative information(scans or dwells with no measurements) are implemented separately in this paper. The Gaussian sum likelihood which is derived from a sensor model accounting for both the positive and the negative information is used. GSUKF is implemented by fusing the state estimate of two or three UKF filters with proper weights which are explicitly derived in this paper. Other than GSUKF, the Gaussian sum likelihood is directly used in the weight update of the GSPF. Their performances are evaluated by comparison with the Gaussian sum extended Kalman filter(GSEKF)implementation. Simulation results show that GSPF outperforms the other filters in terms of track loss and track accuracy at the cost of large computation complexity. GSUKF and GSEKF have comparable performance; the superiority of one over another is scenario dependent.

关键词： target tracking standoff jamming(SOJ) negative information unscented Kalman filter(UKF) particle filter

Uncertainty Assessment of Soil Erosion Model Using particle filtering

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Journal of Mountain Science 2015年第4期12卷 828-840页

作者： Yeonsu KIM Giha LEE Hyunuk AN Jae E YANG International Water Resources Research Institute Chungnam National University Dept.of Construction & Disaster Prevention Eng. Kyungpook National University Dept.of Agricultural & Rural Eng. Chungnam National University Dept.of Biological Environment Kangwon National University

Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and ***, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling *** of uncertainty involved in modeling include data, model structural, and parameter *** deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the *** proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at *** provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter ***-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon ***, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.

关键词： Data assimilation particle filter Soil erosion modeling Parameter estimation Time variant parameter Mountainous catchment

Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

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High Technology Letters 2014年第1期20卷 34-41页

作者：胡振涛 Liu Xianxing Li Jie Institute of Image Processing and Pattern Recognition Henan University

The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： multi-sensor information fusion particle filter weight optimization predictionand update

Underwater Acoustic Source localization method based on TDOA with particle filtering

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Underwater Acoustic Source localization method based on TDOA...

American Option Pricing Using particle filtering Under Stochastic Volatility Correlated Jump Model

第26届中国控制与决策会议

作者： XU Yaosong Wang Dandan Fu Hua Faculty of Electrical and Control Engineering Liaoning Technical University

ISBN: (纸本)9781479937097

Multipath propagation and reverberation of underwater acoustic signal affect the accuracy of localization estimation based on time difference of arrival（TDOA）. To solve the problem, a novel algorithm is proposed to improve the accuracy of localization estimation of underwater acoustic source. Based on the signals received at an array of sensors, a general framework for acoustic source localization using particle ltering is proposed. A generic particle ltering framework was derived. The simulation results demonstrate the superiority of the proposed method. The resulting particle lter is shown to outperform traditional acoustic source localization method.

关键词： Underwater Acoustic Localization Multipath Propagation Reverberation Time Difference of Arrival(TDOA) particle filter

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Journal of Systems Science and Information 2014年第6期2卷 505-519页

作者： Bin SONG Enqi LIANG Bing LIU School of Management Science and Engineering Central University of Finance and Economics China Securities Co. Ltd. Investment Department Derivatives Trading Department

A particle filter based method to price American option under partial observation framework is introduced. Assuming the underlying price process is driven by unobservable latent factors, the pricing methodology should contain inference on latent factors in addition to the original least-squares Monte Carlo approach of Longstaff and Schwartz. Sequential Monte Carlo is a widely applied technique to provide such inference. Applications on stochastic volatility models has been introduced by Rambharat, who assume that volatility is a latent stochastic process, and capture information about it using particle filter based “summary vectors”. This paper investigates this particle filter based pricing methodology, with an extension to a stochastic volatility jump model, stochastic volatility correlated jump model(SVCJ), and auxiliary particle filter(APF) introduced first by Pitt and Shephard. In the APF algorithm of SVCJ model, it also provides a modification version to enhance the performance in the resampling step. A detailed implementation and numerical examples of the algorithm are *** algorithm is also applied to empirical data.

关键词： American options sequential Monte Carlo particle filter latent variable stochastic volatility jump auxiliary particle filter

Quadrature Kalman particle fitler

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Journal of Systems Engineering and Electronics 2010年第2期21卷 175-179页

作者： Chunlincl Wu Chongzhao Han School of Electronic and Control Engineering Chang'an University Xi'an 710064 P. R. China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China

In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm.

关键词： particle filter statistical linear regression quadrature Kalman filter importance density function.

A new particle filtering algorithms applied to GPS/DR integrated navigation

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A new particle filtering algorithms applied to GPS/DR integr...