Log in

Relevant bibliographies by topics / Bernoulli filter / Journal articles

To see the other types of publications on this topic, follow the link: Bernoulli filter.

Journal articles on the topic 'Bernoulli filter'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Bernoulli filter.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Mahler, Ronald. "The Pairwise-Markov Bernoulli Filter." IEEE Access 8 (2020): 168229–45. http://dx.doi.org/10.1109/access.2020.3022752.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Törő, Olivér, Tamás Bécsi, Szilárd Aradi, and Péter Gáspár. "IMM Bernoulli Gaussian Particle Filter." IFAC-PapersOnLine 51, no. 22 (2018): 274–79. http://dx.doi.org/10.1016/j.ifacol.2018.11.554.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Du, Haocui, and Weixin Xie. "Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter." Sensors 20, no. 18 (September 20, 2020): 5387. http://dx.doi.org/10.3390/s20185387.

Full text

Abstract:

The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter.

APA, Harvard, Vancouver, ISO, and other styles

6

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

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Wang, Ping, Liang Ma, and Kai Xue. "Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8742897.

Full text

Abstract:

Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer.

APA, Harvard, Vancouver, ISO, and other styles

12

Jinfeng Chen, Hong Ma, Chengguo Liang, and Yufeng Zhang. "OTHR multipath tracking using the bernoulli filter." IEEE Transactions on Aerospace and Electronic Systems 50, no. 3 (July 2014): 1974–90. http://dx.doi.org/10.1109/taes.2013.120659.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Feihu, Daniel Clarke, and Alois Knoll. "Visual odometry based on a Bernoulli filter." International Journal of Control, Automation and Systems 13, no. 3 (March 28, 2015): 530–38. http://dx.doi.org/10.1007/s12555-014-0192-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Li, Bo, and Jianli Zhao. "Auxiliary particle Bernoulli filter for target tracking." International Journal of Control, Automation and Systems 15, no. 3 (May 22, 2017): 1249–58. http://dx.doi.org/10.1007/s12555-016-0010-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

URIBE-MURCIA, KAREN, and YURIY S. SHMALIY. "UFIR State Estimator for Network Systems with Two-Step Delayed and Lost Data." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 17 (August 6, 2021): 81–86. http://dx.doi.org/10.37394/232014.2021.17.11.

Full text

Abstract:

Wireless communication over networks often produces issues associated with delayed and missing data. In this paper, we consider one-step and two-step delays. The state space model is transformed to have no delay with new system and observation matrices. To mitigate the effect, we develop the unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory H∞ filter for Bernoulli-distributed delays with possible packet dropouts. A comparative study of the filters developed is provided under the uncertain noise and transmission probability. Numerical simulation is conducted employing a GPSbased tracking network system. A better performance of the UFIR filter is demonstrated experimentally

APA, Harvard, Vancouver, ISO, and other styles

16

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Ristic, Branko, Luke Rosenberg, Du Yong Kim, Xuezhi Wang, and Jason Williams. "Bernoulli track‐before‐detect filter for maritime radar." IET Radar, Sonar & Navigation 14, no. 3 (February 7, 2020): 356–63. http://dx.doi.org/10.1049/iet-rsn.2019.0480.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Cai, Fei, Hongqi Fan, Zaiqi Lu, and Qiang Fu. "Bernoulli filter for range and Doppler ambiguous radar." IET Signal Processing 9, no. 9 (December 2015): 647–54. http://dx.doi.org/10.1049/iet-spr.2015.0013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lian, Feng, Chen Li, Chongzhao Han, and Hui Chen. "Convergence Analysis for the SMC-MeMBer and SMC-CBMeMBer Filters." Journal of Applied Mathematics 2012 (2012): 1–25. http://dx.doi.org/10.1155/2012/584140.

Full text

Abstract:

The convergence for the sequential Monte Carlo (SMC) implementations of the multitarget multi-Bernoulli (MeMBer) filter and cardinality-balanced MeMBer (CBMeMBer) filters is studied here. This paper proves that the SMC-MeMBer and SMC-CBMeMBer filters, respectively, converge to the true MeMBer and CBMeMBer filters in the mean-square sense and the corresponding bounds for the mean-square errors are given. The significance of this paper is in theory to present the convergence results of the SMC-MeMBer and SMC-CBMeMBer filters and the conditions under which the two filters satisfy mean-square convergence.

APA, Harvard, Vancouver, ISO, and other styles

21

Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets." Journal of Applied Mathematics 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/727430.

Full text

Abstract:

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.

APA, Harvard, Vancouver, ISO, and other styles

22

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Su, Z. Z., H. B. Ji, and Y. Q. Zhang. "An Improved Measurement-Oriented Marginal Multi-Bernoulli/Poisson Filter." Radioengineering 27, no. 1 (April 12, 2019): 191–98. http://dx.doi.org/10.13164/re.2019.0191.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Bryant, Daniel S., Ba-Tuong Vo, Ba-Ngu Vo, and Brandon A. Jones. "A Generalized Labeled Multi-Bernoulli Filter With Object Spawning." IEEE Transactions on Signal Processing 66, no. 23 (December 1, 2018): 6177–89. http://dx.doi.org/10.1109/tsp.2018.2872856.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Lu, Ming Li, and Jian Shi. "Multiple cell tracking by generalised labelled multi-Bernoulli filter." International Journal of Computer Applications in Technology 61, no. 4 (2019): 273. http://dx.doi.org/10.1504/ijcat.2019.10024870.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Shi, Jian, and Ming Li Lu. "Multiple cell tracking by generalised labelled multi-Bernoulli filter." International Journal of Computer Applications in Technology 61, no. 4 (2019): 273. http://dx.doi.org/10.1504/ijcat.2019.103296.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Papi, F., V. Kyovtorov, R. Giuliani, F. Oliveri, and D. Tarchi. "Bernoulli Filter for Track-Before-Detect using MIMO Radar." IEEE Signal Processing Letters 21, no. 9 (September 2014): 1145–49. http://dx.doi.org/10.1109/lsp.2014.2325566.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Si, Weijian, Hongfan Zhu, and Zhiyu Qu. "Robust Poisson Multi-Bernoulli Filter With Unknown Clutter Rate." IEEE Access 7 (2019): 117871–82. http://dx.doi.org/10.1109/access.2019.2936864.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Garcia-Fernandez, Angel F., Jason L. Williams, Karl Granstrom, and Lennart Svensson. "Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation." IEEE Transactions on Aerospace and Electronic Systems 54, no. 4 (August 2018): 1883–901. http://dx.doi.org/10.1109/taes.2018.2805153.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Kropfreiter, Thomas, Florian Meyer, and Franz Hlawatsch. "A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation." IEEE Transactions on Aerospace and Electronic Systems 56, no. 3 (June 2020): 2478–88. http://dx.doi.org/10.1109/taes.2019.2941104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Li, Bo. "An improved Bernoulli particle filter for single target tracking." Multidimensional Systems and Signal Processing 29, no. 3 (January 17, 2017): 799–819. http://dx.doi.org/10.1007/s11045-017-0471-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Guoping Shi, 时国平, and 钱叶册 Yece Qian. "Application of Bernoulli filter in bearings-only tracking scenarios." Infrared and Laser Engineering 50, no. 2 (2021): 20200343. http://dx.doi.org/10.3788/irla.28_2020-0343.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Guoping Shi, 时国平, and 钱叶册 Yece Qian. "Application of Bernoulli filter in bearings-only tracking scenarios." Infrared and Laser Engineering 50, no. 2 (2021): 20200343. http://dx.doi.org/10.3788/irla20200343.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Huang, Yuan, Liping Wang, Xueying Wang, and Wei An. "Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking." Applied Sciences 10, no. 1 (December 20, 2019): 99. http://dx.doi.org/10.3390/app10010099.

Full text

Abstract:

The likelihood determined by the distance between measurements and predicted states of targets is widely used in many filters for data association. However, if the actual motion model of targets is not coincided with the preset dynamic motion model, this criterion will lead to poor performance when close-space targets are tracked. For rigid target tracking task, the structure of rigid targets can be exploited to improve the data association performance. In this paper, the structure of the rigid target is represented as a hypergraph, and the problem of data association is formulated as a hypergraph matching problem. However, the performance of hypergraph matching degrades if there are missed detections and clutter. To overcome this limitation, we propose a joint probabilistic hypergraph matching labeled multi-Bernoulli (JPHGM-LMB) filter with all undetected cases being considered. In JPHGM-LMB, the likelihood is built based on group structure rather than the distance between predicted states and measurements. Consequently, the probability of each target associated with each measurement (joint association probabilities) can be obtained. Then, the structure information is integrated into LMB filter by revising each single target likelihood with joint association probabilities. However, because all undetected cases is considered, proposed approach is usable in real time only for a limited number of targets. Extensive simulations have demonstrated the significant performance improvement of our proposed method.

APA, Harvard, Vancouver, ISO, and other styles

35

Gan, Linhai, and Gang Wang. "Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter." Journal of Sensors 2019 (May 19, 2019): 1–12. http://dx.doi.org/10.1155/2019/9278725.

Full text

Abstract:

As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.

APA, Harvard, Vancouver, ISO, and other styles

36

Chen, Zhongyue, and Wen Xu. "Joint Passive Detection and Tracking of Underwater Acoustic Target by Beamforming-Based Bernoulli Filter with Multiple Arrays." Sensors 18, no. 11 (November 18, 2018): 4022. http://dx.doi.org/10.3390/s18114022.

Full text

Abstract:

In this paper, improved Bernoulli filtering methods are developed to deal with the problem of joint passive detection and tracking of an underwater acoustic target with multiple arrays. Three different likelihood calculation methods based on local beamforming results are proposed for the Bernoulli filter updating. Firstly, multiple peaks, including both mainlobe and sidelobe peaks, are selected to form the direction-of-arrival (DOA) measurement set, and then the Bernoulli filter is used to extract the target track. Secondly, to make full use of the informations in the beamforming output, not only the DOAs but also their intensities, the beam powers are used as the input measurement sets of the filter, and an approach based on Pearson correlation coefficient (PCC) is developed for distinguishing between signal and noise. Lastly, a hybrid method of the former two is proposed in the case of fewer then three arrays. The tracking performances of the three methods are compared in simulations and experiment. The simulations with three distributed arrays show that, compared with the DOA-based method, the beam-based method and the hybrid method can both improve the target tracking accuracy. The processing results of the shallow water experimental data collected by two arrays show that the hybrid method can achieve a better tracking performance.

APA, Harvard, Vancouver, ISO, and other styles

37

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

38

Nguyen, Tran, and Du Kim. "GLMB Tracker with Partial Smoothing." Sensors 19, no. 20 (October 12, 2019): 4419. http://dx.doi.org/10.3390/s19204419.

Full text

Abstract:

In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters.

APA, Harvard, Vancouver, ISO, and other styles

39

Zhou, Ying, Shuming Yang, and Qiang Zang. "H∞Filter Design for Large-Scale Systems with Missing Measurements." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/945705.

Full text

Abstract:

This paper is concerned withH∞filter design problem for large-scale systems with missing measurements. The occurrence of missing measurements is assumed to be a Bernoulli distributed sequence with known probability. The new full-dimensional filter is designed to make the filter error system exponentially mean-square stable and achieve a prescribedH∞performance. Sufficient conditions are derived in terms of linear matrix inequality (LMI) for the existence of the filter, and the parameters of filter are obtained by solving the LMI. Finally, the numerical simulation results illustrate the effectiveness of the proposed scheme.

APA, Harvard, Vancouver, ISO, and other styles

40

LI Miao, 李淼, 龙云利 LONG Yun-li, 李骏 LI Jun, 安玮 AN Wei, and 周一宇 ZHOU Yi-yu. "Oversampling point target track-before-detect by Multi-Bernoulli filter." Optics and Precision Engineering 23, no. 12 (2015): 3446–55. http://dx.doi.org/10.3788/ope.20152312.3446.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Shen, Xinglin, Zhiyong Song, Hongqi Fan, and Qiang Fu. "General Bernoulli Filter for Arbitrary Clutter and Target Measurement Processes." IEEE Signal Processing Letters 25, no. 10 (October 2018): 1525–29. http://dx.doi.org/10.1109/lsp.2018.2865675.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Vo, Ba-Ngu, Ba-Tuong Vo, and Hung Gia Hoang. "An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter." IEEE Transactions on Signal Processing 65, no. 8 (April 15, 2017): 1975–87. http://dx.doi.org/10.1109/tsp.2016.2641392.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Qin, Yong, Yong Liu, Kangjie Li, Baojuan Zou, and Baohong Liu. "Maneuvering Target Tracking Based on Multiple-model Multipath Bernoulli Filter." IOP Conference Series: Materials Science and Engineering 790 (April 7, 2020): 012111. http://dx.doi.org/10.1088/1757-899x/790/1/012111.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Vo, Ba Tuong, Chong Meng See, Ning Ma, and Wee Teck Ng. "Multi-Sensor Joint Detection and Tracking with the Bernoulli Filter." IEEE Transactions on Aerospace and Electronic Systems 48, no. 2 (2012): 1385–402. http://dx.doi.org/10.1109/taes.2012.6178069.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Cai, Fei, Hongqi Fan, and Qiang Fu. "Bernoulli Filter for Extended Target in Clutter Using Poisson Models." Chinese Journal of Electronics 24, no. 2 (April 1, 2015): 326–31. http://dx.doi.org/10.1049/cje.2015.04.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Törő, Olivér, Tamás Bécsi, Szilárd Aradi, and Péter Gáspár. "IMM Bernoulli Filter for Cooperative Object Tracking in Road Traffic." IFAC-PapersOnLine 51, no. 9 (2018): 355–60. http://dx.doi.org/10.1016/j.ifacol.2018.07.058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!