Relevant bibliographies by topics / IMM algoritmus

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  1. Journal articles

Academic literature on the topic 'IMM algoritmus'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "IMM algoritmus"

1

Musicki, D., and S. Suvorova. "Tracking in clutter using IMM-IPDA-based algorithms." IEEE Transactions on Aerospace and Electronic Systems 44, no. 1 (2008): 111–26. http://dx.doi.org/10.1109/taes.2008.4516993.

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2

Jovandic, Igor, Zeljko Djurovic, and Branko Kovacevic. "Adaptive filtering algorithms in target tracking applications." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 317–26. http://dx.doi.org/10.2298/fuee0303317j.

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Comparison of several target tracking algorithms is presented. Namely discrete noise level adjustment (DNLA), variable state dimension (VSD) and interacting multiple model (IMM) algorithms are discussed. Target trajectory, target models, filtering algorithms and simulation results are given. The cumulative estimation error criterion is used in order to compare the algorithms.
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3

Raghavan, V., K. R. Pattipati, and Y. Bar-Shalom. "Efficient L-D factorization algorithms for PDA, IMM, and IMMPDA filters." IEEE Transactions on Aerospace and Electronic Systems 29, no. 4 (1993): 1297–310. http://dx.doi.org/10.1109/7.259533.

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4

Altmann, Daniel M. "New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome." Immunology 154, no. 3 (2018): 329–30. http://dx.doi.org/10.1111/imm.12956.

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5

Raymund, T. D. "Ionospheric tomography algorithms." International Journal of Imaging Systems and Technology 5, no. 2 (1994): 75–85. http://dx.doi.org/10.1002/ima.1850050204.

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6

Ho, Tan-Jan. "Switched IMM-EV Algorithms for State Estimation of Some Jump Markov Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 1396–401. http://dx.doi.org/10.3182/20080706-5-kr-1001.00239.

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7

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 covar
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8

Wang, Yan, Wenjia Ren, Long Cheng, and Jijun Zou. "A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment." Sensors 20, no. 14 (2020): 3941. http://dx.doi.org/10.3390/s20143941.

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As the progress of electronics and information processing technology continues, indoor localization has become a research hotspot in wireless sensor networks (WSN). The adverse non-line of sight (NLOS) propagation usually causes large measurement errors in complex indoor environments. It could decrease the localization accuracy seriously. A traditional grey model considers the motion characteristics but does not take the NLOS propagation into account. A robust interacting multiple model (R-IMM) could effectively mitigate NLOS errors but the clipping point is hard to choose. In order to easily
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9

Jilkov, V. P., D. S. Angelova, and T. A. Semerdjiev. "Design and comparison of mode-set adaptive IMM algorithms for maneuvering target tracking." IEEE Transactions on Aerospace and Electronic Systems 35, no. 1 (1999): 343–50. http://dx.doi.org/10.1109/7.745704.

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10

Belidina, Eliza, H. Bunyamin, and Asep Setia. "Perancangan Aplikasi Fiqih Nikah dan Talak Menurut Mazhab Imam Syafi’i Berbasis Android." Jurnal Algoritma 11, no. 1 (2014): 59–66. http://dx.doi.org/10.33364/algoritma/v.11-1.59.

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