Journal articles: 'Multi-bernoulli'

1

Ouyang, C., C. Li, and H. Ji. "Improved multi-target multi-Bernoulli filter." IET Radar, Sonar & Navigation 6, no. 6 (July 1, 2012): 458–64. http://dx.doi.org/10.1049/iet-rsn.2011.0377.

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Vo, Ba-Tuong, Ba-Ngu Vo, Reza Hoseinnezhad, and Ronald P. S. Mahler. "Robust Multi-Bernoulli Filtering." IEEE Journal of Selected Topics in Signal Processing 7, no. 3 (June 2013): 399–409. http://dx.doi.org/10.1109/jstsp.2013.2252325.

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Li, Shijie, and Humin Lei. "Measurement-Driven Multi-Target Multi-Bernoulli Filter." Mathematical Problems in Engineering 2018 (July 22, 2018): 1–9. http://dx.doi.org/10.1155/2018/6515608.

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A measurement-driven multi-target multi-Bernoulli (MeMBer) filter which modifies the MeMBer filter by the measurements information is proposed in this paper. The proposed filter refines both the legacy estimates and the data-induced estimates of the MeMBer filter. For the targets under the legacy track set, the detection probabilities derived from the measurements are employed to refine the multi-target distribution. And for the targets under the data-induced track set, the multi-target distribution is further improved by the modified existence probabilities of the legacy tracks. Unlike the cardinality balanced MeMBer (CBMeMBer) filter, the proposed filter removes the cardinality bias in the MeMBer filter by utilizing the measurements information. Simulation results show that, compared with the traditional methods, the proposed filter can improve the stability and accuracy of the estimates and does not need the high detection probability hypothesis.

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Li, Dong, Chenping Hou, and Dongyun Yi. "Multi-Bernoulli smoother for multi-target tracking." Aerospace Science and Technology 48 (January 2016): 234–45. http://dx.doi.org/10.1016/j.ast.2015.11.017.

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Saucan, Augustin-Alexandru, Mark J. Coates, and Michael Rabbat. "A Multisensor Multi-Bernoulli Filter." IEEE Transactions on Signal Processing 65, no. 20 (October 15, 2017): 5495–509. http://dx.doi.org/10.1109/tsp.2017.2723348.

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Garcia-Fernandez, Angel F., Lennart Svensson, Jason L. Williams, Yuxuan Xia, and Karl Granstrom. "Trajectory Poisson Multi-Bernoulli Filters." IEEE Transactions on Signal Processing 68 (2020): 4933–45. http://dx.doi.org/10.1109/tsp.2020.3017046.

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YUAN, Changshun, Jun WANG, Peng LEI, and Jinping SUN. "Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli Random Finite Set." Chinese Journal of Electronics 27, no. 1 (January 1, 2018): 115–22. http://dx.doi.org/10.1049/cje.2017.10.010.

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Zhu, Yun, Jun Wang, and Shuang Liang. "Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking." Sensors 19, no. 4 (February 25, 2019): 980. http://dx.doi.org/10.3390/s19040980.

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This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network.

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Zhang, Zijing, Fei Zhang, and Chuantang Ji. "Multi-robot cardinality-balanced multi-Bernoulli filter simultaneous localization and mapping method." Measurement Science and Technology 33, no. 3 (December 23, 2021): 035101. http://dx.doi.org/10.1088/1361-6501/ac3784.

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Abstract In order to improve the simultaneous localization and mapping (SLAM) accuracy of mobile robots in complex indoor environments, the multi-robot cardinality-balanced multi-Bernoulli filter SLAM (MR-CBMber-SLAM) method is proposed. First of all, this method introduces a multi-Bernoulli filter based on the random finite set (RFS) theory to solve the complex data association problem. This method aims to overcome the problem that the multi-Bernoulli filter will overestimate the aspect of SLAM map feature estimation, and combines the strategy of balancing cardinality with a multi-Bernoulli filter. What is more, in order to further improve the accuracy and operating efficiency of SLAM, a multi-robot strategy and a multi-robot Gaussian information-fusion method are proposed. In the experiment, the MR-CBMber-SLAM method is compared with the multi-vehicle probability hypothesis density SLAM (MV-PHD-SLAM) method. The experimental results show that the MR-CBMber-SLAM method is better than MV-PHD-SLAM method. Therefore, it effectively verifies that the MR-CBMber-SLAM method is more adaptable to a complex indoor environment.

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Kim, Taekyun, and Dae Kim. "A note on degenerate multi-poly-Bernoulli numbers and polynomials." Applicable Analysis and Discrete Mathematics, no. 00 (2022): 5. http://dx.doi.org/10.2298/aadm200510005k.

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In this paper, we consider the degenerate multi-poly-Bernoulli numbers and polynomials which are defined by means of the multiple polylogarithms and degenerate versions of the multi-poly-Bernoulli numbers and polynomials. We investigate some properties for those numbers and polynomials. In addition, we give some identities and relations for the degenerate multi-poly- Bernoulli numbers and polynomials.

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11

Do, Cong-Thanh, Tran Thien Dat Nguyen, and Hoa Van Nguyen. "Robust multi-sensor generalized labeled multi-Bernoulli filter." Signal Processing 192 (March 2022): 108368. http://dx.doi.org/10.1016/j.sigpro.2021.108368.

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12

Muhiuddin, G., W. A. Khan, U. Duran, and D. Al-Kadi. "Some Identities of the Degenerate Multi-Poly-Bernoulli Polynomials of Complex Variable." Journal of Function Spaces 2021 (June 1, 2021): 1–8. http://dx.doi.org/10.1155/2021/7172054.

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In this paper, we introduce degenerate multi-poly-Bernoulli polynomials and derive some identities of these polynomials. We give some relationship between degenerate multi-poly-Bernoulli polynomials degenerate Whitney numbers and Stirling numbers of the first kind. Moreover, we define degenerate multi-poly-Bernoulli polynomials of complex variables, and then, we derive several properties and relations.

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13

Granstrom, Karl, Peter Willett, and Yaakov Bar-Shalom. "Approximate Multi-Hypothesis Multi-Bernoulli Multi-Object Filtering Made Multi-Easy." IEEE Transactions on Signal Processing 64, no. 7 (April 2016): 1784–97. http://dx.doi.org/10.1109/tsp.2015.2500884.

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14

Deusch, Hendrik, Stephan Reuter, and Klaus Dietmayer. "The Labeled Multi-Bernoulli SLAM Filter." IEEE Signal Processing Letters 22, no. 10 (October 2015): 1561–65. http://dx.doi.org/10.1109/lsp.2015.2414274.

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15

GAO, Lin, Jian HUANG, Wen SUN, Ping WEI, and Hongshu LIAO. "Multi-Sensor Multi-Target Bernoulli Filter with Registration Biases." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E99.A, no. 10 (2016): 1774–81. http://dx.doi.org/10.1587/transfun.e99.a.1774.

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16

Yi, Wei, Meng Jiang, Reza Hoseinnezhad, and Bailu Wang. "Distributed multi‐sensor fusion using generalised multi‐Bernoulli densities." IET Radar, Sonar & Navigation 11, no. 3 (March 2017): 434–43. http://dx.doi.org/10.1049/iet-rsn.2016.0227.

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17

Yin, Jianjun, and Jianqiu Zhang. "The Central Difference Multi-target Multi-bernoulli Filtering Algorithms." Information Technology Journal 10, no. 11 (October 15, 2011): 2168–74. http://dx.doi.org/10.3923/itj.2011.2168.2174.

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18

Papi, Francesco, Ba-Ngu Vo, Ba-Tuong Vo, Claudio Fantacci, and Michael Beard. "Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities." IEEE Transactions on Signal Processing 63, no. 20 (October 2015): 5487–97. http://dx.doi.org/10.1109/tsp.2015.2454478.

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19

KANEKO, MASANOBU, and HIROFUMI TSUMURA. "MULTI-POLY-BERNOULLI NUMBERS AND RELATED ZETA FUNCTIONS." Nagoya Mathematical Journal 232 (May 8, 2017): 19–54. http://dx.doi.org/10.1017/nmj.2017.16.

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We construct and study a certain zeta function which interpolates multi-poly-Bernoulli numbers at nonpositive integers and whose values at positive integers are linear combinations of multiple zeta values. This function can be regarded as the one to be paired up with the $\unicode[STIX]{x1D709}$-function defined by Arakawa and Kaneko. We show that both are closely related to the multiple zeta functions. Further we define multi-indexed poly-Bernoulli numbers, and generalize the duality formulas for poly-Bernoulli numbers by introducing more general zeta functions.

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20

Si, Weijian, Hongfan Zhu, and Zhiyu Qu. "A Novel Structure for a Multi-Bernoulli Filter without a Cardinality Bias." Electronics 8, no. 12 (December 5, 2019): 1484. http://dx.doi.org/10.3390/electronics8121484.

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The original multi-target multi-Bernoulli (MeMBer) filter for multi-target tracking (MTT) is shown analytically to have a significant bias in its cardinality estimation. A novel cardinality balance multi-Bernoulli (CBMeMBer) filter reduces the cardinality bias by calculating the exact cardinality of the posterior probability generating functional (PGFl) without the second assumption of the original MeMBer filter. However, the CBMeMBer filter can only have a good performance under a high detection probability, and retains the first assumption of the MeMBer filter, which requires measurements that are well separated in the surveillance region. An improved MeMBer filter proposed by Baser et al. alleviates the cardinality bias by modifying the legacy tracks. Although the cardinality is balanced, the improved algorithm employs a low clutter density approximation. In this paper, we propose a novel structure for a multi-Bernoulli filter without a cardinality bias, termed as a novel multi-Bernoulli (N-MB) filter. We remove the approximations employed in the original MeMBer filter, and consequently, the N-MB filter performs well in a high clutter intensity and low signal-to-noise environment. Numerical simulations highlight the improved tracking performance of the proposed filter.

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21

Bayad, Abdelmejid, and Yoshinori Hamahata. "Multiple polylogarithms and multi-poly-Bernoulli polynomials." Functiones et Approximatio Commentarii Mathematici 46, no. 1 (March 2012): 45–61. http://dx.doi.org/10.7169/facm/2012.46.1.4.

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22

Fatemi, Maryam, Karl Granstrom, Lennart Svensson, Francisco J. R. Ruiz, and Lars Hammarstrand. "Poisson Multi-Bernoulli Mapping Using Gibbs Sampling." IEEE Transactions on Signal Processing 65, no. 11 (June 1, 2017): 2814–27. http://dx.doi.org/10.1109/tsp.2017.2675866.

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23

He, Xiangyu, and Guixi Liu. "Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error Compensation." Sensors 16, no. 9 (August 31, 2016): 1399. http://dx.doi.org/10.3390/s16091399.

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24

Si, Weijian, Hongfan Zhu, and Zhiyu Qu. "Multi‐sensor Poisson multi‐Bernoulli filter based on partitioned measurements." IET Radar, Sonar & Navigation 14, no. 6 (April 28, 2020): 860–69. http://dx.doi.org/10.1049/iet-rsn.2019.0510.

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25

Cament, Leonardo, Javier Correa, Martin Adams, and Claudio Pérez. "The histogram Poisson, labeled multi-Bernoulli multi-target tracking filter." Signal Processing 176 (November 2020): 107714. http://dx.doi.org/10.1016/j.sigpro.2020.107714.

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26

Yi, Wei, Suqi Li, Bailu Wang, Reza Hoseinnezhad, and Lingjiang Kong. "Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter." IEEE Transactions on Signal Processing 68 (2020): 241–56. http://dx.doi.org/10.1109/tsp.2019.2957638.

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27

Hoang, Hung Gia, and Ba Tuong Vo. "Sensor management for multi-target tracking via multi-Bernoulli filtering." Automatica 50, no. 4 (April 2014): 1135–42. http://dx.doi.org/10.1016/j.automatica.2014.02.007.

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28

Vo, Ba-Ngu, Ba-Tuong Vo, and Michael Beard. "Multi-Sensor Multi-Object Tracking With the Generalized Labeled Multi-Bernoulli Filter." IEEE Transactions on Signal Processing 67, no. 23 (December 1, 2019): 5952–67. http://dx.doi.org/10.1109/tsp.2019.2946023.

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29

Mahler, Ronald. "Exact Closed-Form Multitarget Bayes Filters." Sensors 19, no. 12 (June 24, 2019): 2818. http://dx.doi.org/10.3390/s19122818.

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The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion has inspired work by dozens of research groups in at least 20 nations; and FISST publications have been cited tens of thousands of times. This review paper addresses a recent and cutting-edge aspect of this research: exact closed-form—and, therefore, provably Bayes-optimal—approximations of the multitarget Bayes filter. The five proposed such filters—generalized labeled multi-Bernoulli (GLMB), labeled multi-Bernoulli mixture (LMBM), and three Poisson multi-Bernoulli mixture (PMBM) filter variants—are assessed in depth. This assessment includes a theoretically rigorous, but intuitive, statistical theory of “undetected targets”, and concrete formulas for the posterior undetected-target densities for the “standard” multitarget measurement model.

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30

Zhu, Yun, Mahendra Mallick, Shuang Liang, and Junkun Yan. "Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements." Remote Sensing 14, no. 13 (June 29, 2022): 3131. http://dx.doi.org/10.3390/rs14133131.

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The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) (2)error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) (2)error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most.

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31

Lee, Hyerim, Jaeho Choi, Sejong Heo, and Kunsoo Huh. "Centralized Multi-Sensor Poisson Multi-Bernoulli Mixture Tracker for Autonomous Driving." IFAC-PapersOnLine 55, no. 14 (2022): 40–45. http://dx.doi.org/10.1016/j.ifacol.2022.07.580.

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32

Zhu, Jihong, Mingli Lu, and Fei Wang. "Multi-cell multi-Bernoulli tracking method based on fractal measurement model." International Journal of Modelling, Identification and Control 37, no. 3/4 (2021): 240. http://dx.doi.org/10.1504/ijmic.2021.121838.

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Zhu, Jihong, Mingli Lu, and Fei Wang. "Multi-cell multi-Bernoulli tracking method based on fractal measurement model." International Journal of Modelling, Identification and Control 37, no. 3/4 (2021): 240. http://dx.doi.org/10.1504/ijmic.2021.10045903.

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34

Do, Cong-Thanh, Tran Thien Dat Nguyen, Diluka Moratuwage, Changbeom Shim, and Yon Dohn Chung. "Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter." Signal Processing 196 (July 2022): 108532. http://dx.doi.org/10.1016/j.sigpro.2022.108532.

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35

Baser, Erkan, Thia Kirubarajan, Murat Efe, and Bhashyam Balaji. "Improved multi‐target multi‐Bernoulli filter with modelling of spurious targets." IET Radar, Sonar & Navigation 10, no. 2 (February 2016): 285–98. http://dx.doi.org/10.1049/iet-rsn.2015.0169.

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36

Ba-Tuong Vo, Ba-Ngu Vo, and A. Cantoni. "The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations." IEEE Transactions on Signal Processing 57, no. 2 (February 2009): 409–23. http://dx.doi.org/10.1109/tsp.2008.2007924.

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37

Liu, Rang, Hongqi Fan, Tiancheng Li, and Huaitie Xiao. "A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking." Sensors 19, no. 19 (September 28, 2019): 4226. http://dx.doi.org/10.3390/s19194226.

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A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.

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38

Zhan, Rong Hui, and Sheng Qi Liu. "Multitarget Track-Before-Detect via MeMBer Filtering and Track Consistency Test." Applied Mechanics and Materials 457-458 (October 2013): 848–55. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.848.

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This paper deals with the problem of time-varying multitarget track-before-detect (TBD) using image observation model. The multitarget state is formulated as random finite set (RFS) and its posterior distribution is approximated by multi-Bernoulli parameters, which are recursively evaluated using sequential Monte Carlo approach. The state estimates are first extracted from the updated Bernoulli components with moderate existence probabilities, allowing for all the true targets and false alarms. The extracted target states are then distilled using track consistency test strategy to remain only the true tracks. Simulation results show the improved performance of the proposed method over the traditional multitarget multi-Bernoulli (MeMBer) filter as well as its capability to provide the identity of individual target.

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39

Zhao, Cong, and Jingshan Li. "A Bernoulli Model of Multi-Product Manufacturing Systems." IFAC Proceedings Volumes 46, no. 9 (2013): 1268–73. http://dx.doi.org/10.3182/20130619-3-ru-3018.00235.

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40

Dzhoha, A. S. "Bernoulli multi-armed bandit problem under delayed feedback." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2021): 20–26. http://dx.doi.org/10.17721/1812-5409.2021/1.2.

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Online learning under delayed feedback has been recently gaining increasing attention. Learning with delays is more natural in most practical applications since the feedback from the environment is not immediate. For example, the response to a drug in clinical trials could take a while. In this paper, we study the multi-armed bandit problem with Bernoulli distribution in the environment with delays by evaluating the Explore-First algorithm. We obtain the upper bounds of the algorithm, the theoretical results are applied to develop the software framework for conducting numerical experiments.

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41

Frohle, Markus, Karl Granstrom, and Henk Wymeersch. "Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking." IEEE Access 8 (2020): 126414–27. http://dx.doi.org/10.1109/access.2020.3008007.

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42

Hu, Qi, Hongbing Ji, and Yongquan Zhang. "Tracking multiple extended targets with multi‐Bernoulli filter." IET Signal Processing 13, no. 4 (June 2019): 443–55. http://dx.doi.org/10.1049/iet-spr.2018.5125.

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43

Dunne, Darcy, and Thia Kirubarajan. "Multiple Model Multi-Bernoulli Filters for Manoeuvering Targets." IEEE Transactions on Aerospace and Electronic Systems 49, no. 4 (October 2014): 2679–92. http://dx.doi.org/10.1109/taes.2014.6619957.

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Dunne, Darcy, and Thia Kirubarajan. "Multiple Model Multi-Bernoulli Filters for Manoeuvering Targets." IEEE Transactions on Aerospace and Electronic Systems 49, no. 4 (October 2013): 2679–92. http://dx.doi.org/10.1109/taes.2013.6621845.

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Jiang, Tongyang, Meiqin Liu, Zhen Fan, and Senlin Zhang. "On multiple-model extended target multi-Bernoulli filters." Digital Signal Processing 59 (December 2016): 76–85. http://dx.doi.org/10.1016/j.dsp.2016.08.002.

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46

Ren, Yayun, and Benlian Xu. "A Quantitative Analysis on Two RFS-Based Filtering Methods for Multicell Tracking." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/495765.

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Multiobject filters developed from the theory of random finite sets (RFS) have recently become well-known methods for solving multiobject tracking problem. In this paper, we present two RFS-based filtering methods, Gaussian mixture probability hypothesis density (GM-PHD) filter and multi-Bernoulli filter, to quantitatively analyze their performance on tracking multiple cells in a series of low-contrast image sequences. The GM-PHD filter, under linear Gaussian assumptions on the cell dynamics and birth process, applies the PHD recursion to propagate the posterior intensity in an analytic form, while the multi-Bernoulli filter estimates the multitarget posterior density through propagating the parameters of a multi-Bernoulli RFS that approximates the posterior density of multitarget RFS. Numerous performance comparisons between the two RFS-based methods are carried out on two real cell images sequences and demonstrate that both yield satisfactory results that are in good agreement with manual tracking method.

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47

Cho, Wonhyeong, Myeong-Seon Gil, Mi-Jung Choi, and Yang-Sae Moon. "Storm-based distributed sampling system for multi-source stream environment." International Journal of Distributed Sensor Networks 14, no. 11 (November 2018): 155014771881269. http://dx.doi.org/10.1177/1550147718812698.

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As a large amount of data streams occur rapidly in many recent applications such as social network service, Internet of Things, and smart factory, sampling techniques have attracted many attentions to handle such data streams efficiently. In this article, we address the performance improvement of binary Bernoulli sampling in the multi-source stream environment. Binary Bernoulli sampling has the n:1 structure where n sites transmit data to 1 coordinator. However, as the number of sites increases or the input stream explosively increases, the binary Bernoulli sampling may cause a severe bottleneck in the coordinator. In addition, bidirectional communication over different networks among the coordinator and sites may incur excessive communication overhead. In this article, we propose a novel distributed processing model of binary Bernoulli sampling to solve these coordinator bottleneck and communication overhead problems. We first present a multiple-coordinator structure to solve the coordinator bottleneck. We then present a new sampling model with an integrated framework and shared memory to alleviate the communication overhead. To verify the effectiveness and scalability of the proposed model, we perform its actual implementation in Apache Storm, a real-time distributed stream processing system. Experimental results show that our Storm-based binary Bernoulli sampling improves performance by up to 1.8 times compared with the legacy method and maintains high performance even when the input stream largely increases. These results indicate that the proposed distributed processing model is an excellent approach that solves the performance degradation problem of binary Bernoulli sampling and verifies its superiority through the actual implementation on Apache Storm.

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48

Hoak, Anthony, Henry Medeiros, and Richard Povinelli. "Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods." Sensors 17, no. 3 (March 3, 2017): 501. http://dx.doi.org/10.3390/s17030501.

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49

Gostar, Amirali K., Reza Hoseinnezhad, and Alireza Bab-Hadiashar. "Robust Multi-Bernoulli Sensor Selection for Multi-Target Tracking in Sensor Networks." IEEE Signal Processing Letters 20, no. 12 (December 2013): 1167–70. http://dx.doi.org/10.1109/lsp.2013.2283735.

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50

Liang, Ma, Du Yong Kim, and Xue Kai. "Multi-Bernoulli filter for target tracking with multi-static Doppler only measurement." Signal Processing 108 (March 2015): 102–10. http://dx.doi.org/10.1016/j.sigpro.2014.09.013.

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Journal articles on the topic 'Multi-bernoulli'

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