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Journal articles on the topic 'Interactive Multiple Model algorithm'

Author: Grafiati

Published: 28 June 2021

Last updated: 12 February 2022

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1

Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (February 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 kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

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2

Lim, Jaechan, Hun-Seok Kim, and Hyung-Min Park. "Interactive-Multiple-Model Algorithm Based on Minimax Particle Filtering." IEEE Signal Processing Letters 27 (2020): 36–40. http://dx.doi.org/10.1109/lsp.2019.2954000.

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Zhu, Meiyu, Guanyu Chen, and Weicun Zhang. "Maneuvering target tracking with improved interactive multiple model algorithm." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 373–76. http://dx.doi.org/10.5954/icarob.2021.os6-5.

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4

Li, Jihan, Xiaoli Li, and Kang Wang. "Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach." Advances in Meteorology 2019 (October 15, 2019): 1–11. http://dx.doi.org/10.1155/2019/1279565.

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Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5is the main particulate matter in air pollution. Therefore, how to predict PM2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5prediction, and it is effective.

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Funkhouser, Thomas, Seth Teller, Carlo Séquin, and Delnaz Khorramabadi. "The UC Berkeley System for Interactive Visualization of Large Architectural Models." Presence: Teleoperators and Virtual Environments 5, no. 1 (January 1996): 13–44. http://dx.doi.org/10.1162/pres.1996.5.1.13.

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Realistic-looking architectural models with furniture may consist of millions of polygons and require gigabytes of data—far than today's workstations can render at interactive frame rates or store in physical memory. We have developed data structures and algorithms for identifying a small portion of a large model to load into memory and render during each frame of an interactive walkthrough. Our algorithms rely upon an efficient display database that represents a building model as a set of objects, each of which can be described at multiple levels of detail, and contains an index of spatial cells with precomputed cell-to-cell and cell-to-object visibility information. As the observer moves through the model interactively, a real-time visibility algorithm traces sightline beams through transparent cell boundaries to determine a small set of objects potentially visible to the observer. An optimization algorithm dynamically selects a level of detail and rendering algorithm with which to display each potentially visible object to meet a userspecified target frame time. Throughout, memory management algorithms predict observer motion and prefetch objects from disk that may become visible during imminent frames. This paper describes an interactive building walkthrough system that uses these data structures and algorithms to maintain interactive frame rates during visualization of very large models. So far, the implementation supports models whose major occluding surfaces are axis-aligned rectangles (e.g., typical buildings). This system is able to maintain over twenty frames per second with little noticeable detail elision during interactive walkthroughs of a building model containing over one million polygons.

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Wan, Jian, Peiwen Ren, and Qiang Guo. "Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking." Symmetry 11, no. 6 (June 5, 2019): 767. http://dx.doi.org/10.3390/sym11060767.

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Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the “snake-like” maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage–Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).

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7

Liu, J., and R. Li. "Hierarchical adaptive interacting multiple model algorithm." IET Control Theory & Applications 2, no. 6 (June 1, 2008): 479–87. http://dx.doi.org/10.1049/iet-cta:20070340.

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8

Qu, HongQuan, LiPing Pang, and ShaoHong Li. "A novel interacting multiple model algorithm." Signal Processing 89, no. 11 (November 2009): 2171–77. http://dx.doi.org/10.1016/j.sigpro.2009.04.033.

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9

Shen, Nan, Liang Chen, Xiangchen Lu, Hao Hu, Yuanjin Pan, Zhouzheng Gao, Xiaoyan Liu, Zhaoliang Liu, and Ruizhi Chen. "Online displacement extraction and vibration detection based on interactive multiple model algorithm." Mechanical Systems and Signal Processing 155 (June 2021): 107581. http://dx.doi.org/10.1016/j.ymssp.2020.107581.

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10

Pan, Quan, Yan Liang, Gang Liu, Hongcai Zhang, and Guanzhong Dai. "Performance analysis of interacting multiple model algorithm." IFAC Proceedings Volumes 32, no. 2 (July 1999): 3932–37. http://dx.doi.org/10.1016/s1474-6670(17)56671-9.

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11

Liu, Hong Jiang. "Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm." Applied Mechanics and Materials 190-191 (July 2012): 906–10. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.906.

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In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

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12

Xue, Li, Yulan Han, and Chunning Na. "Robust Interacting Multiple Model Unscented Particle Filter for Navigation." Mathematical Problems in Engineering 2020 (November 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/8871358.

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In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

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13

Ruan, Yanhua, and Lang Hong. "Use of the interacting multiple model algorithm with multiple sensors." Mathematical and Computer Modelling 44, no. 3-4 (August 2006): 332–41. http://dx.doi.org/10.1016/j.mcm.2006.01.020.

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14

Xue, Rui, Daniel (Jian) Sun, and Shukai Chen. "Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach." Discrete Dynamics in Nature and Society 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/682390.

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Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

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15

Zhou, Wei dong, Jia nan Cai, Long Sun, and Chen Shen. "An Improved Interacting Multiple Model Algorithm Used in Aircraft Tracking." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/813654.

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There are some problems in traditional interacting multiple model algorithms (IMM) when used in target tracking systems. For instance, the mode transition matrix is inaccurate and cannot be determined when the sojourn times are not known. To solve these problems, an optimal mode transition matrix IMM (OMTM-IMM) algorithm is proposed in this paper. The linear minimum variance theory is used to calculate the mode transition matrix which depends on the continuous system state rather than the sojourn times in this algorithm. Moreover, the correlation of the subfilter is considered; hence the covariance matrices are utilized to compute mode transition matrix. In this algorithm, the model probability is defined as a diagonal matrix which is combined with the filters outputs; thus the effects produced by each state can be distinguished. Finally, to verify the superiority of the new algorithm, the theoretical proof and simulation results are given. They show that the OMTM-IMM algorithm can improve the tracking accuracy and can be utilized in the complex environment.

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16

Wang, Hong, and Jia Deng. "Interacting Multiple Model LK Tracking." Applied Mechanics and Materials 644-650 (September 2014): 1733–36. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1733.

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The nonlinear motion state of object seriously affects the object tracking characteristics in complex motion scene. In this paper, we propose an interacting multiple model LK (IMM-LK) tracking algorithm to enhance the performance of tracking nonlinear moving object. LK tracking approach is based on the localized gradient obtaining stable optical-flow feature, based on LK, we build several motion models of the tracked object that interact with each other in the tracking process. The method extracts different model's object features, estimates the object state and calculates the matching rate of each model with the current motion model using theory of minimum variance. Combining with the optimal transfer matrix then we can track the nonlinear moving object. The proposed IMM-LK algorithm performs favorably against conventional LK tracking on the performance of tracking nonlinear moving object.

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17

Wang, Xianghua, Xinyu Yang, Zheng Oin, and Huijie Yang. "Hierarchical interacting multiple model algorithm based on improved current model." Journal of Systems Engineering and Electronics 21, no. 6 (December 2010): 961–67. http://dx.doi.org/10.3969/j.issn.1004-4132.2010.06.006.

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18

Li, X. R., and Y. Bar-Shalom. "Performance prediction of the interacting multiple model algorithm." IEEE Transactions on Aerospace and Electronic Systems 29, no. 3 (July 1993): 755–71. http://dx.doi.org/10.1109/7.220926.

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19

He, Xiu Ling, and Jing Song Yang. "Curve Model of Adaptive Interaction Model Algorithm Tracking Method." Applied Mechanics and Materials 738-739 (March 2015): 344–49. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.344.

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Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and high cost efficiency, it becomes generally applicable algorithm for curve tracking model. But, the target mobility is very high in practice, Single target tracking model is no longer applicable curve tracking model. To improve the accuracy of tracking, the adaptive grid interacting multiple model (AGIMM) algorithm was given. The algorithm has two fatal weaknesses in the practical application. First, the process of maneuvering target tracking, when the model changes and gradient, the tracking precision is not high ;Second, because the changing model structure is very large model sets, the algorithm is complexity and system processing speed is very slow, which cannot be widely used. In order to improve the algorithm and its scope of application, The paper proposed the adaptive Kalman filter adaptive interacting multiple model algorithm (AKFAIMM).The algorithm introduced the parameter in the adaptive Kalman filter, and adjusted parameter in maneuvering target tracking, the parameter was adjusted Continuously in the curve motion model, it could greatly improve the tracking precision and the application of the model. Second, to improve the algorithm complexity. The paper improved on turning curve. The angular velocity estimation method replaced centripetal acceleration estimation method. The estimation method reduced the number of model set and reduced greatly of computation.

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20

Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.

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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

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21

Tian, Ye, Hong Jiang, Quan Xin Ding, and Guo Wei Liang. "Turn Rate Estimation Based Adaptive IMM Algorithm for Maneuvering Target Tracking." Advanced Materials Research 383-390 (November 2011): 5609–14. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5609.

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A turn rate estimation based adaptive interactive multiple model algorithm is put forward to solve model-set mismatch problem of target tracking algorithm applying to high maneuvering target. By considering both the estimation and the estimated variance of target’s turn rate, model-set is selected according to a rule based on the coefficient of variance of turn rate estimation. When turn rate estimation is acceptable, model-set is constructed according to turn rate estimation to reduce competition among models. When turn rate estimation is unacceptable, standard IMM algorithm model-set is applied to increase coverage of model-set. Simulation shows this algorithm improves tracking performance especially for high maneuvering targets.

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22

Mihaylova, Ludmila, and Emil Semerdjiev. "An Interacting Multiple Model Algorithm for Stochastic Systems Control." Information & Security: An International Journal 2 (1999): 102–12. http://dx.doi.org/10.11610/isij.0209.

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23

Johnston, L. A., and V. Krishnamurthy. "An improvement to the interacting multiple model (IMM) algorithm." IEEE Transactions on Signal Processing 49, no. 12 (2001): 2909–23. http://dx.doi.org/10.1109/78.969500.

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24

Sun, Jie, Chaoshu Jiang, Zhuming Chen, and Wei Zhang. "Interacting multiple model algorithm based on joint likelihood estimation." Journal of Electronics (China) 28, no. 4-6 (November 2011): 427–32. http://dx.doi.org/10.1007/s11767-012-0734-x.

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25

Liu, Ya Lei, and Xiao Hui Gu. "Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array." Applied Mechanics and Materials 300-301 (February 2013): 407–13. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.407.

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Abstract. In order to improve the tracking accuracy of 3D dynamic acoustic array to 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The 3D motion acoustic array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

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26

Li, Zhen-Xing, Yun Wang, Jin-Mang Liu, Ni Peng, and Lin-Hai Gan. "Variable-structure interacting multiple-model estimation for group targets tracking with random matrices." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 7 (February 9, 2017): 1201–11. http://dx.doi.org/10.1177/0954410016688123.

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In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of different structures to constitute a candidate model set and selects a minimum difference model from target state as the present extended model from the set of candidates at real time. Second, in the filtering process of VSIMM, the fusion estimation of extension state is implemented by the scalar coefficients weighting method, where weight coefficient is calculated by the trace of the corresponding covariance matrix of extension state. The performances of the proposed EMA-VSIMM and BMA-VSIMM algorithms are evaluated via simulation of a generic group targets maneuvering tracking problem.

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27

Okkan, Umut, and Umut Kirdemir. "Towards a hybrid algorithm for the robust calibration of rainfall–runoff models." Journal of Hydroinformatics 22, no. 4 (May 8, 2020): 876–99. http://dx.doi.org/10.2166/hydro.2020.016.

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Abstract In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall–runoff models (dynamic water balance model and abcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence speed. A variance decomposition-based method – analysis of variance (ANOVA) – was also used to quantify the dynamic sensitivity of HPSO parameters. Accordingly, the individual and interactive uncertainties of the parameters defined in the HPSO are relatively low.

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28

Shi, Xinping, and Hong Sheng Xia. "Model and interactive algorithm of bi-level multi-objective decision-making with multiple interconnected decision makers." Journal of Multi-Criteria Decision Analysis 10, no. 1 (January 2001): 27–34. http://dx.doi.org/10.1002/mcda.285.

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29

He Keke, Li Zhenzhen, and Tang Zhenmin. "Model Error and Iterated Extended Kalman Filter based Interacting Multiple Model Algorithm." International Journal of Digital Content Technology and its Applications 5, no. 7 (July 31, 2011): 1–7. http://dx.doi.org/10.4156/jdcta.vol5.issue7.1.

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30

Liu, Ruilan, and Xiaohui Chen. "Maneuvering target tracking algorithm based on fuzzy interacting multiple model." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 26, no. 10 (February 18, 2013): 846–50. http://dx.doi.org/10.3724/sp.j.1187.2012.00846.

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31

Xu, Wei-ming, and Yan-chun Liu. "Algorithm of Multirate Interacting Multiple Model for Underwater Target Tracking." Journal of Electronics & Information Technology 30, no. 3 (March 2, 2011): 581–84. http://dx.doi.org/10.3724/sp.j.1146.2006.01309.

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32

Ding, Z., and L. Hong. "An interacting multiple model algorithm with a switching Markov chain." Mathematical and Computer Modelling 25, no. 1 (January 1997): 1–9. http://dx.doi.org/10.1016/s0895-7177(96)00180-x.

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33

DENG, Xiao-long, Jian-ying XIE, and Hong-wei NI. "Interacting Multiple Model Algorithm with the Unscented Particle Filter (UPF)." Chinese Journal of Aeronautics 18, no. 4 (November 2005): 366–71. http://dx.doi.org/10.1016/s1000-9361(11)60257-4.

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34

Hwang, Inseok, Chze Eng Seah, and Sangjin Lee. "A Study on Stability of the Interacting Multiple Model Algorithm." IEEE Transactions on Automatic Control 62, no. 2 (February 2017): 901–6. http://dx.doi.org/10.1109/tac.2016.2558156.

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35

Munir, A. "Adaptive interacting multiple model algorithm for tracking a manoeuvring target." IEE Proceedings - Radar, Sonar and Navigation 142, no. 1 (1995): 11. http://dx.doi.org/10.1049/ip-rsn:19951528.

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36

Wang, Quanhui, En Fan, and Pengfei Li. "Fuzzy-Logic-Based, Obstacle Information-Aided Multiple-Model Target Tracking." Information 10, no. 2 (February 2, 2019): 48. http://dx.doi.org/10.3390/info10020048.

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Incorporating obstacle information into maneuvering target-tracking algorithms may lead to a better performance when the target when the target maneuver is caused by avoiding collision with obstacles. In this paper, we propose a fuzzy-logic-based method incorporating new obstacle information into the interacting multiple-model (IMM) algorithm (FOIA-MM). We use convex polygons to describe the obstacles and then extract the distance from and the field angle of these obstacle convex polygons to the predicted target position as obstacle information. This information is fed to two fuzzy logic inference systems; one system outputs the model weights to their probabilities, the other yields the expected sojourn time of the models for the transition probability matrix assignment. Finally, simulation experiments and an Unmanned Aerial Vehicle experiment are carried out to demonstrate the efficiency and effectiveness of the proposed algorithm.

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Shahzadi, Gulfam, Fariha Zafar, and Maha Abdullah Alghamdi. "Multiple-Attribute Decision-Making Using Fermatean Fuzzy Hamacher Interactive Geometric Operators." Mathematical Problems in Engineering 2021 (June 24, 2021): 1–20. http://dx.doi.org/10.1155/2021/5150933.

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Fermatean fuzzy set (FFS) is a more efficient, flexible, and generalized model to deal with uncertainty, as compared to intuitionistic and Pythagorean fuzzy models. This research article presents a novel multiple-attribute decision-making (MADM) technique based on FFS. Aggregation operators (AOs), for example, Dombi, Einstein, and Hamacher, are frequently being used in the MADM process and are considered useful tools for evaluating the given alternatives. Among these, one of the most effective is the Hamacher operator. The salient feature of this operator is that it reduces the impact of negative information and provides more accurate results. Motivated by the primary characteristics of the Hamacher operator, we apply Hamacher interactive aggregation operators based on FFSs to determine the best alternative. Using Hamacher’s norm operations, we introduce some new geometric operators, namely, Fermatean fuzzy Hamacher interactive weighted geometric (FFHIWG) operator, Fermatean fuzzy Hamacher interactive ordered weighted geometric (FFHIOWG) operator, and Fermatean fuzzy Hamacher interactive hybrid weighted geometric (FFHIHWG) operator. Some important results and properties of the proposed AOs are discussed, and to achieve the optimal alternative, the proposed MADM technique is carried out in a real-life application of the medical field. An algorithm of the proposed technique is also developed. The significance of the proposed method is that Fermatean fuzzy Hamacher interactive geometric (FFHIG) operators deal with the relationship among belongingness degree (BD) and nonbelongingness degree (NBD) of the objects, which perform a crucial role in decision-making (DM). At last, to show the exactness and validity of the proposed work, a comparative analysis of the proposed model and the existing models is presented.

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Tang, Xinmin, Wenjie Zhao, and Shangfeng Gao. "Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors." International Journal of Distributed Sensor Networks 16, no. 2 (February 2020): 155014772090456. http://dx.doi.org/10.1177/1550147720904563.

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To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced.

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Ran, Hui Zhen. "Research on Internet Online Evaluation System Based on OPC Technology and Progressive Level Model." Advanced Materials Research 846-847 (November 2013): 1864–67. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1864.

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Because of the appearance of mobile office and distributed office in Internet communication system, which has brought great opportunities for development of real-time and interactive online evaluation system. In this background, this paper designs an OPC technology which is compatible with multiple internet distributed interface, it can realize the distributed online evaluation. This paper designs a direct switching algorithm procedure of OPC with multiple interfaces, so it can realize the evaluation of interactive and real-time. About the model and the level, this paper uses hierarchical model to evaluate network. Finally based on the evaluation of ideological education online as an example, we conduct the performance of the Internet online system for practice research, it is found that the Internet online evaluation system has well real-time and interactive features, it provides the theory reference for the research of internet ideological and political education.

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Du, Jin Song, and Xin Bi. "An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method." Advanced Materials Research 718-720 (July 2013): 1286–89. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1286.

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In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform (CV) and uniformly accelerated motion (CA) model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accuracy, and enhances the real-time performance of system, the simulation results show that, the algorithm for tracking precision compared to the traditional IMM method has bigger improvement.

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Yuan, Gannan, Wei Zhu, Wei Wang, and Bo Yin. "Maneuvering Target Tracking Algorithm Based on Interacting Multiple Models." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/810613.

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Aiming at improving the accuracy and quick response of the filter in nonlinear maneuvering target tracking problems, the Interacting Multiple Models Cubature Information Filter (IMMCIF) is proposed. In IMMCIF, the Cubature Information Filter (CIF) is brought into Interacting Multiple Model (IMM), which can not only improve the accuracy but also enhance the quick response of the filter. CIF is a multisensor nonlinear filtering algorithm; it evaluates the information vector and information matrix rather than state vector and covariance, which can reduce the error of nonlinear filtering algorithm. IMM disposes all the models simultaneously through Markov Chain, which can enhance the quick response of the filter. Finally, the simulation results show that the proposed filter exhibits fast and smooth switching when disposing different maneuver models; it performs better than the IMMCKF and IMMUKF on tracking accuracy.

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Liu, Hongqiang, Zhongliang Zhou, and Lei Yu. "Maneuvering Acceleration Estimation Algorithm Using Doppler Radar Measurement." Mathematical Problems in Engineering 2018 (June 4, 2018): 1–13. http://dx.doi.org/10.1155/2018/4984186.

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An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

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Tanaka, Misato, Yasunari Sasaki, Mitsunori Miki, and Tomoyuki Hiroyasu. "Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/302573.

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We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of the area by a probabilistic model. To confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an experiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a set of pseudomultiple preferences. With this experiment, we proved that when there are multiple preferences, the proposed method searches faster and more diversely than the conventional one. The second experiment was a subjective experiment. This experiment showed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences.

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GHAZAL, M., and A. DOUSTMOHAMMADI. "A Novel Robust Interacting Multiple Model Algorithm for Maneuvering Target Tracking." Advances in Electrical and Computer Engineering 17, no. 3 (2017): 35–42. http://dx.doi.org/10.4316/aece.2017.03005.

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Xiao, Song, Xian Si Tan, and Hong Wang. "Interacting Multiple Mode Tracking Algorithm Based on Modified Coordinate Turn Model." Applied Mechanics and Materials 610 (August 2014): 534–39. http://dx.doi.org/10.4028/www.scientific.net/amm.610.534.

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The continuing success of near space hypersonic aircraft flight test has become a real threat to China's space attack-defense system, In view of the problem that the single model cannot track such target effectively, an interacting multiple model (IMM) tracking algorithm based on modified cornering model (MCT) was proposed. First the characteristics of near space hypersonic target were analyzed, and then the target real-time angular velocity according to the target motion equation was estimated, finally the near space hypersonic target tracking through the IMM was carried out. The Monte Carlo simulation results show that the IMM tracking algorithm can effectively track near space hypersonic target, and the tracking accuracy and stability are superior to single model, it has certain practical significance.

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Blom, H. A. P., and Y. Bar-Shalom. "The interacting multiple model algorithm for systems with Markovian switching coefficients." IEEE Transactions on Automatic Control 33, no. 8 (1988): 780–83. http://dx.doi.org/10.1109/9.1299.

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Bing Chen and J. K. Tugnait. "Interacting multiple model fixed-lag smoothing algorithm for Markovian switching systems." IEEE Transactions on Aerospace and Electronic Systems 36, no. 1 (2000): 243–50. http://dx.doi.org/10.1109/7.826326.

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Zhang, Yuan, Chen Guo, Hai Hu, Shubo Liu, and Junbo Chu. "An algorithm of the adaptive grid and fuzzy interacting multiple model." Journal of Marine Science and Application 13, no. 3 (August 27, 2014): 340–45. http://dx.doi.org/10.1007/s11804-014-1266-6.

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Liu, Xia, Fei Long, Wenjie Zhang, and Lu Guo. "Modular Interacting Multiple Model Based on Extended Viterbi Algorithm for Maneuvering Target Tracking." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/374054.

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A new maneuvering target tracking algorithm is investigated, which is modeled as a class of Markov jump linear systems (MJLS). Drawing on the experience of combination idea of the extended Viterbi algorithm (EV) and the interacting multiple model algorithm (IMM), a modular interacting multiple model based on extended Viterbi (MIMMEV) is presented. The MIMMEV algorithm consists ofNindependent interacting multiple model-extended Viterbi (IMM-EV). Furthermore, these IMM-EV filters are independent and working in parallel in the MIMMEV algorithm. According to the derived probability, the estimated state of every moment is the weighted sum of each estimator at the corresponding time. Simulation results demonstrate that the proposed algorithm improves the tracking precision and reduces the computational burden compared with traditional IMM and IMM-EV.

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Caselli, E., F. Ortega, M. Santiago, J. Marcazzó, M. Lester, and P. Molina. "A novel algorithm to solve the differential equation describing the non-interactive multiple-trap system model in thermoluminescence." Radiation Effects and Defects in Solids 173, no. 5-6 (April 16, 2018): 339–52. http://dx.doi.org/10.1080/10420150.2018.1462357.

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