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Journal articles on the topic 'Bayesian filter ; particle filtering ; tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Hiranmayi, Penumarty, Kola Sai Gowtham, S. Koteswara Rao, and V. Gopi Tilak. "Tracking of pendulum using particle filter with residual resampling." International Journal of Engineering & Technology 7, no. 2.7 (2018): 12. http://dx.doi.org/10.14419/ijet.v7i2.7.10246.

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The phenomenon of simple harmonic motion is more vigilantly explained using a simple pendulum. The angular motion of a pendulum is linear in nature. But the analysis of the motion along the horizontal direction is non-linear. To estimate this, several algorithms like the Kalman filter, Extended Kalman Filter etc. are adopted. Here in this paper, Particle filter is chosen which is a method to form Monte Carlo approximations to the solutions of Bayesian filtering equations. Sequential importance resampling based Particle filters are used where the filtering distributions are multi-nodal or consi

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2

Tao, Wu, Yong Sheng Xu, and Xiao Yan Wang. "Particle Filtering Algorithm Based on Dynamic Multi-Feature Fusion." Applied Mechanics and Materials 741 (March 2015): 373–77. http://dx.doi.org/10.4028/www.scientific.net/amm.741.373.

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The target tracking technology in image sequence is of great meanings in the military and civilian areas, by using Monte Carlo method to complete the Bayesian recursive, particle filter is widely used in the systems of non-linear and non - Gaussian and good results are gained. However, particle filter there are also disadvantages in terms of sample impoverishment, the choosing of proper proposal distribution, real time and so on. In this paper, the particle filter is utilized to in the feature fusion of the moving target, and the experimental results show that the proposed algorithm has certai

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3

Closas, Pau, Carles Fernández-Prades, José Diez, and David de Castro. "Nonlinear Bayesian Tracking Loops for Multipath Mitigation." International Journal of Navigation and Observation 2012 (October 17, 2012): 1–15. http://dx.doi.org/10.1155/2012/359128.

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This paper studies Bayesian filtering techniques applied to the design of advanced delay tracking loops in GNSS receivers with multipath mitigation capabilities. The analysis includes tradeoff among realistic propagation channel models and the use of a realistic simulation framework. After establishing the mathematical framework for the design and analysis of tracking loops in the context of GNSS receivers, we propose a filtering technique that implements Rao-Blackwellization of linear states and a particle filter for the nonlinear partition and compare it to traditional delay lock loop/phase

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4

Han, Yulan, and Chongzhao Han. "A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/7424538.

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To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target state and association hypothesis. Furthermore, a joint proposal distribution is defined for the multiple extended target state and association hypothesis. Then, the Bayesian framework of multiple extended target tracking is implemented by the particle filtering which could release the high computational

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5

Jeon, Byunghwan. "Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images." Sensors 21, no. 18 (2021): 6087. http://dx.doi.org/10.3390/s21186087.

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Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust ‘vesselness’ measurement is essential for extracting coronary centerlines. Importantly, we

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Zhong, Lei, Yong Li, Wei Cheng, and Yi Zheng. "Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics." Sensors 20, no. 13 (2020): 3669. http://dx.doi.org/10.3390/s20133669.

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A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose

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Yardim, Caglar, Peter Gerstoft, and Zoi-Heleni Michalopoulou. "Geophysical signal processing using sequential Bayesian techniques." GEOPHYSICS 78, no. 3 (2013): V87—V100. http://dx.doi.org/10.1190/geo2012-0180.1.

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Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential observations. They provide a formulation in which the geophysical parameters that characterize dynamic, nonstationary processes are continuously estimated as new data become available. This is done by using prediction from previous estimates of geophysical parameters, updates stemming from physical and statistical models that relate seismic measurements to the unknown geophysical parameters. In addition, these techniques provide the evolving uncertainty in the estimates in the form of posterior pro

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8

Yang, Dong He. "Face Tracking Based on Particle Filtering and α-β-γ Filtering". Applied Mechanics and Materials 651-653 (вересень 2014): 2306–9. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2306.

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In view of the traditional particle filter algorithm cannot guarantee effective tracking in the case of target rotation or obscured. The study proposes a tracking method based on α-β-γ filter and particle filter. The algorithm uses α-β-γ filtering prediction position as the next frame image target candidate model of computing center of particle filter algorithm. The algorithm uses α-β-γ filtering prediction position as the next frame image target candidate model of computing center of particle filter. To reduce the number of iterations of particle filter algorithm, strengthen the real-time tra

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9

Zhu, Hong Bo, Hai Zhao, Dan Liu, and Chun He Song. "Compressed Iterative Particle Filter for Target Tracking." Applied Mechanics and Materials 55-57 (May 2011): 91–94. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.91.

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Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep th

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10

Zhan, Ronohui, Qin Xin, and Wan Jianwei. "Modified unscented particle filter for nonlinear Bayesian tracking." Journal of Systems Engineering and Electronics 19, no. 1 (2008): 7–14. http://dx.doi.org/10.1016/s1004-4132(08)60038-9.

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11

van Leeuwen, Peter Jan. "Particle Filtering in Geophysical Systems." Monthly Weather Review 137, no. 12 (2009): 4089–114. http://dx.doi.org/10.1175/2009mwr2835.1.

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Abstract The application of particle filters in geophysical systems is reviewed. Some background on Bayesian filtering is provided, and the existing methods are discussed. The emphasis is on the methodology, and not so much on the applications themselves. It is shown that direct application of the basic particle filter (i.e., importance sampling using the prior as the importance density) does not work in high-dimensional systems, but several variants are shown to have potential. Approximations to the full problem that try to keep some aspects of the particle filter beyond the Gaussian approxim

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12

Zhang, Jian, and Wan Juan Song. "The Framework Base on Bayesian Predictive Filtering Algorithm in VR/AR." Applied Mechanics and Materials 568-570 (June 2014): 1122–25. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1122.

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Tracking system is a vital aspect of Virtual Reality and Augmented Reality, the efficiency of tracking system is determined by the implementation of framework and the predictive filtering algorithm. As a result of the better applicability of Bayesian predictive filtering algorithm in simulation of non-linear system model, this paper proposes a framework for Bayesian predictive filter, which includes predictive filtering layer and denotation layer, and according to every layer’s function, analyses the implementation of framework. The optimal simulation count is worked out by the experiment. The

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13

Hoteit, Ibrahim, Xiaodong Luo, and Dinh-Tuan Pham. "Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters." Monthly Weather Review 140, no. 2 (2012): 528–42. http://dx.doi.org/10.1175/2011mwr3640.1.

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This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman fil

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14

Li, Shao Mei, Kai Wang, Chao Gao, and Ya Wen Wang. "Reverse Validation Based Adaptive Particle Filter Algorithm for Object Tracking." Advanced Materials Research 1044-1045 (October 2014): 1302–8. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1302.

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To improves tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, tracking object frame by frame via color histogram and particle filtering. Secondly, reversely validating the tracking result based on particle filtering. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this

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15

Tung, Tony, and Takashi Matsuyama. "Visual Tracking Using Multimodal Particle Filter." International Journal of Natural Computing Research 4, no. 3 (2014): 69–84. http://dx.doi.org/10.4018/ijncr.2014070104.

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Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body p

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16

Brandes, T. Scott, Nilanjan Dasgupta, and Lawrence Carin. "Variational Bayesian particle filtering for underwater target localization and tracking." Journal of the Acoustical Society of America 125, no. 4 (2009): 2578. http://dx.doi.org/10.1121/1.4783800.

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17

Gao, Song, Chao Bo Chen, and Qian Gong. "Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering." Advanced Materials Research 989-994 (July 2014): 2212–15. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2212.

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As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm

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18

Jiao, Zhun, and Rong Zhang. "Improved Particle Filter for Integrated Navigation System." Applied Mechanics and Materials 543-547 (March 2014): 1278–81. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1278.

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As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved particle filter (IPF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for SINS/GPS integrated navigation system. The simulation results confirm that the improved particle filter outperforms th

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19

Liu, Bin, and Chengpeng Hao. "Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/489121.

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The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlin

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20

Zhai, Yan, Xiao Bo Guo, and Yong Gang Yan. "Unscented Particle Filter Algorithm for Ballistic Target Tracking." Applied Mechanics and Materials 686 (October 2014): 359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.686.359.

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At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

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21

Zhao, Jun Wei, Ming Jun Zhang, Yong Gang Yan, and Yong Peng Yan. "Unscented Particle Filter Algorithm for Ballistic Target Tracking." Applied Mechanics and Materials 130-134 (October 2011): 369–72. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.369.

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At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

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22

Hou, Yi Min, Yong Liang Zhao, Ting Ting Sun, and Jian Ming Di. "Research on Kalman Particle Filter-Based Tracking Algorithm." Advanced Materials Research 461 (February 2012): 571–74. http://dx.doi.org/10.4028/www.scientific.net/amr.461.571.

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In the application of computer vision technique, target tracking in image sequences was an important research subject. This paper describes the particle filter and introduces a tracking algorithm based on Kalman particle filter. The algorithm improves the traditional particle filter, whose non-linear and non-Gaussian may result in non-robustness of tracking process. Kalman particle filter use kalman filter to predict the particle’s state and generate the proposal distribution, the state of each particle evolved by the Kalman prediction equations and update equations, increasing the robustness

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23

Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (2018): 169–75. http://dx.doi.org/10.1051/jnwpu/20183610169.

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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended

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24

Ge, Huilin, Zhiyu Zhu, and Kang Lou. "Tracking Video Target via Particle Filtering on Manifold." Information Technology and Control 48, no. 4 (2019): 538–44. http://dx.doi.org/10.5755/j01.itc.48.4.23939.

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Most of existing particle filtering-based video target tracking algorithms are in Euclidean space, when object posture and scale size changes, and to track high dimensional system, it is difficult to guarantee the tracking effect. This paper describes the covariance descriptor to represent the object image region, the geometric deformation of the object image region can be realized by an affine transformation, and the affine transformation matrix is one element of the Lie group. Then particle filter algorithm based on lie group of manifold is proposed , the video tracking system state lies dir

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25

Ali, Wasiq, Yaan Li, Zhe Chen, Muhammad Asif Zahoor Raja, Nauman Ahmed, and Xiao Chen. "Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking." Entropy 21, no. 11 (2019): 1088. http://dx.doi.org/10.3390/e21111088.

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In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate stat

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Li, Juan, Hui Juan Hao, and Mao Li Wang. "The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion." Advanced Materials Research 628 (December 2012): 440–44. http://dx.doi.org/10.4028/www.scientific.net/amr.628.440.

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This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

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Jiang, Haonan, and Yuanli Cai. "Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking." Sensors 18, no. 10 (2018): 3241. http://dx.doi.org/10.3390/s18103241.

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Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filt

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28

Gao, Qing Hua, Jie Wang, and Ming Lu Jin. "A Novel Modified Particle Filter Algorithm." Advanced Materials Research 204-210 (February 2011): 1895–99. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1895.

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The particle filter (PF) algorithm provides an effective solution to the non-linear and non-Gaussian filtering problem. However, when the motion noises or observation noises are strong, the degenerate phenomena will occur, which leads to poor estimation. In this paper, we propose a modified particle filter (MPF) algorithm for improving the estimated precision through a particle optimization method. After calculating the coarse estimation with the traditional PF, we optimize the particles according to their weights and relative positions, then, move the particles toward the optimal probability

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29

Zhang, Lieping, Jinghua Nie, Shenglan Zhang, Yanlin Yu, Yong Liang, and Zuqiong Zhang. "Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization." Journal of Electrical and Computer Engineering 2021 (September 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/2838971.

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Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering tim

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Zhang, Zeng Ping, and Shu Hua Li. "Research of Video Tracking Algorithm Based on the Blob Analysis." Advanced Materials Research 383-390 (November 2011): 1185–89. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1185.

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To the video that contains the target, a method is proposed to create the background model based on the mixed Gauss. And the target locating method based on the blob analysis and blob filtering, the anti-noise ability and filter robustness of tracking is improved. The kalman filter and the particle filter are separately used to pass and update the foreground target’s posterior probability distribution. Finally the kalman filter and the particle filter's are compared and that builds the foundation of the further development.

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31

Wang, Yazhao. "Multitarget Tracking by Improved Particle Filter Based on Unscented Transform." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/483913.

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This paper considers the problem of multitarget tracking in cluttered environment. To reduce the dependency on the noise priori knowledge, an improved particle filtering (PF) data association approach is presented based on the filter (HF). This approach can achieve higher robustness in the condition that the measurement noise prior is unknown. Because of the limitations of the HF in nonlinear tracking, we first present the unscented filter (HUF) by embedding the unscented transform (UT) into the extended filter (HEF) structure. Then the HUF is incorporated into the Rao-Blackwellized particle f

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Liu, Zhigang, Jin Shang, and Xufen Hua. "Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter." Wireless Communications and Mobile Computing 2020 (June 20, 2020): 1–9. http://dx.doi.org/10.1155/2020/8865298.

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In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method o

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Su, Ling Dong, Ming Yue Zhai, and Zhi Yu Zhu. "Target Tracking Based Adaptive Particle Filter in Binary Wireless Sensor Networks." Advanced Materials Research 756-759 (September 2013): 2281–87. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2281.

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Since real-time and communication amount is crucial for the wireless sensor network target tracking, the performance of target tracking in the wireless sensor network is critically depended on real-time and communication amount reduction. This paper presents a target tracking method based on distributed adaptive particle filtering in binary wireless sensor network. Based on dynamic clustering, the adaptive particle filter receives the observations from children nodes and formulates the local estimate with the cluster head as the processing center. Simulation results show that the method can ef

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Chen, Pengyun, Jianlong Chang, Yujie Han, and Meini Yuan. "Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering." International Journal of Advanced Robotic Systems 16, no. 1 (2019): 172988141882157. http://dx.doi.org/10.1177/1729881418821576.

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To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.

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Du, Sijie, Hongxin Xu, and Tianping Li. "Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion." Journal of Sensors 2020 (November 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/8846977.

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In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Cams

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Jiang, Defu, Ming Liu, Yiyue Gao, Yang Gao, Wei Fu, and Yan Han. "Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking." Sensors 18, no. 12 (2018): 4416. http://dx.doi.org/10.3390/s18124416.

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The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gau

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Hu, Fan Jun, and Wen Jun Shi. "Infrared Moving Multi-Target Tracking Based on Particle Filter and FCM." Applied Mechanics and Materials 347-350 (August 2013): 3792–96. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3792.

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An efficient approach based on particle filter and FCM is presented to realize moving infrared multi-target tracking under Island shore background. Some possible targets can be obtained and saved by processing IR data through denoising by median filter, extracting edge, identifying and eliminating sea-sky line, morphological filtering and etc. Data association and robust multi-target tracking can be realized by the proposed particle filter and FCM algorithm. The proposed approach is validated to track multi-target effectively by using actual infrared image sequences with Island shore backgroun

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38

Huansheng, N., C. Weishi, and L. Jing. "Radar target tracking in cluttered environment based on particle filtering." Aeronautical Journal 114, no. 1155 (2010): 309–14. http://dx.doi.org/10.1017/s0001924000003754.

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Abstract This paper deals with the problem of radar target tracking in cluttered environment from plane position indicator (PPI) radar images collected by low-cost incoherent radar. For this purpose a new five-step technique is proposed, including background subtraction, clutter suppression, measurements extraction, tracking and data fusion; the tracking step uses a particle filtering based data association method. Radar measurements, including target information and clutter interference, are checked whether it belongs to tracking target by data association with Kalman predicted state. If the

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Guo, Wei, Qingjie Zhao, and Dongbing Gu. "Visual Tracking Using an Insect Vision Embedded Particle Filter." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/573131.

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Particle filtering (PF) based object tracking algorithms have drawn great attention from lots of scholars. The core of PF is to predict the possible location of the target via the state transition model. One commonly adopted approach is resorting to prior motion cues under the smooth motion assumption, which performs well when the target moves with a relatively stable velocity. However, it would possibly fail if the target is undergoing abrupt motion. To address this problem, inspired by insect vision, we propose a simple yet effective visual tracking framework based on PF. Utilizing the neuro

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Tirri, Anna Elena, Giancarmine Fasano, Domenico Accardo, and Antonio Moccia. "Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/280478.

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Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering

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Chen, Zhimin, Mengchu Tian, Yuming Bo, and Xiaodong Ling. "Infrared small target detection and tracking algorithm based on new closed-loop control particle filter." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 4 (2018): 1435–56. http://dx.doi.org/10.1177/0954410017753445.

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The problem of particle impoverishment could be always found in standard particle filter, additionally a large number of particles are required for accurate estimation. as it is difficult to meet the demand of modern infrared search and tracking system. To solve this problem, an improved infrared small target detection and tracking method based on closed-loop control bat algorithm optimized particle filter is proposed. Firstly, bat algorithm is introduced into the particle filtering in this method. Particles are used to simulate the process that an individual bat hunts and avoids obstacles so

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Wang, Xiaoli, Liangqun Li, and Weixin Xie. "A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking." Sensors 19, no. 9 (2019): 2208. http://dx.doi.org/10.3390/s19092208.

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In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial

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Akyildiz, Ömer Deniz, and Joaquín Míguez. "Nudging the particle filter." Statistics and Computing 30, no. 2 (2019): 305–30. http://dx.doi.org/10.1007/s11222-019-09884-y.

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Abstract We investigate a new sampling scheme aimed at improving the performance of particle filters whenever (a) there is a significant mismatch between the assumed model dynamics and the actual system, or (b) the posterior probability tends to concentrate in relatively small regions of the state space. The proposed scheme pushes some particles toward specific regions where the likelihood is expected to be high, an operation known as nudging in the geophysics literature. We reinterpret nudging in a form applicable to any particle filtering scheme, as it does not involve any changes in the res

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Zhou, Ning, Lawrence Lau, Ruibin Bai, and Terry Moore. "A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking." Remote Sensing 13, no. 1 (2021): 132. http://dx.doi.org/10.3390/rs13010132.

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In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning a

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Ni, Peng, Bo Zhang, Yafei Song, and Mingliang Zhang. "Multisensor Distributed Dynamic Programming Method for Collaborative Warning and Tracking." Mathematical Problems in Engineering 2020 (May 15, 2020): 1–19. http://dx.doi.org/10.1155/2020/2818416.

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Multisensor distributed dynamic programming for collaborative warning and tracking during antimissile combat serves to meet the tracking accuracy requirements of all ballistic targets in the battlefield under the circumstance of a limited total amount of sensor resources. This paper proposes a method of multisensor distributed dynamic programming for collaborative warning and tracking based on game theory. First, starting from the target tracking algorithm, according to the characteristics of antimissile multisensor combat planning, the box particle filter (BPF) theory capable of distributed f

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Kai, Zhang, and Gan Lin Shan. "Nonlinear Non-Gaussian Filtering Algorithm Based on Cubature Kalman and Particle Filter." Applied Mechanics and Materials 380-384 (August 2013): 1323–26. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1323.

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To resolve the nonlinear non-Gaussian tracking problem effectively, a novel filtering algorithm based on Cubature Kalman Filter (CKF) and Particle Filters (PF) is proposed, which is called Cubature Kalman Particle Filter (CPF). CKF is used to generate the importance density function for PF. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points. It need not compute the Jacobian matrix. Moreover, it makes efficient use of the latest observation information into system state transition density, thus greatly improving the filter

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Wang, Yi, Ming Qing Xiao, and Jia Yong Fang. "Integrate Uncertainty in the Process of Prognostics for Electronics." Applied Mechanics and Materials 69 (July 2011): 132–37. http://dx.doi.org/10.4028/www.scientific.net/amm.69.132.

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Elements of uncertainty in the electronics Prognostics process were studied. A method for electronics Dynamic Damage Optimal Estimation and prognostics based on Particle Filtering were proposed. Under the effect of time stress, the electronics cumulative damage is the result of the continuous effect of the stress, as a result, a HMM based electronics dynamic damage model was built at first place, analytical results of uncertainties in the process of prognostics were given and thus a Bayesian based filter system was built. Bayesian Filter change the problem of uncertainty into an optimal estima

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Yin, Jian Jun, and Jian Qiu Zhang. "Convolution PHD Filtering for Nonlinear Non-Gaussian Models." Advanced Materials Research 213 (February 2011): 344–48. http://dx.doi.org/10.4028/www.scientific.net/amr.213.344.

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A novel probability hypothesis density (PHD) filter, called the Gaussian mixture convolution PHD (GMCPHD) filter was proposed. The PHD within the filter is approximated by a Gaussian sum, as in the Gaussian mixture PHD (GMPHD) filter, but the model may be non-Gaussian and nonlinear. This is implemented by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. The analysis results show the lower complexity, more amenable for parallel implementation of the GMCPHD filter than the convolution PHD (CPHD) filter and the ability to deal with complex obser

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Opiela, Miroslav, and František Galčík. "Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones." Sensors 20, no. 18 (2020): 5343. http://dx.doi.org/10.3390/s20185343.

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Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched

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Naujoks, Benjamin, Torsten Engler, Martin Michaelis, Thorsten Luettel, and Hans-Joachim Wuensche. "Considering measurement uncertainty in dynamic object tracking for autonomous driving applications." tm - Technisches Messen 85, no. 12 (2018): 764–78. http://dx.doi.org/10.1515/teme-2018-0018.

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Abstract Measurement uncertainty plays an important role in every real-world perception task. This paper describes the influence of measurement uncertainty in state estimation, which is the main part of Dynamic Object Tracking. Its base is the probabilistic Bayesian Filtering approach. Practical examples and tools for choosing the correct filter implementation including measurement models and their conversion, for different kinds of sensors are presented.

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