Log in

Relevant bibliographies by topics / Multisensor-multitarget tracking systems

Contents

Journal articles
Book chapters
Conference papers

Academic literature on the topic 'Multisensor-multitarget tracking systems'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Multisensor-multitarget tracking systems.'

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.

Journal articles on the topic "Multisensor-multitarget tracking systems"

1

Li, Song, Yongmei Cheng, Huibin Wang, and Shibo Gao. "Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (2020): 797–805. http://dx.doi.org/10.1051/jnwpu/20203840797.

Full text

Abstract:

In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased loc

APA, Harvard, Vancouver, ISO, and other styles

2

Zhong, Yuhao, Zhihao Yang, Ting Li, and Yuting Zhang. "An Information-Entropy-Based Hierarchical Serialization Allocation Method for UAV Tracking in 6G Networks." Wireless Communications and Mobile Computing 2022 (September 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/3233789.

Full text

Abstract:

Unmanned aerial vehicles (UAVs) play an important role in future 6G networks, which can be used to assist cellular networks in setting up temporary networks to provide communication services when network access demand is intense. It is critical to design a UAV tracking method with high efficiency and high precision under active sensor radiation control to build a reliable network of UAVs. Multisensor cooperative multitarget tracking for UAVs with high accuracy is an alternative solution to meet the performance requirements of 6G. In this paper, an information-entropy-based multisensor to multi

APA, Harvard, Vancouver, ISO, and other styles

3

Pao, Lucy Y. "Multisensor multitarget mixture reduction algorithms for tracking." Journal of Guidance, Control, and Dynamics 17, no. 6 (1994): 1205–11. http://dx.doi.org/10.2514/3.21334.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Aziz, Ashraf M., Murali Tummala, and Roberto Cristi. "Fuzzy logic data correlation approach in multisensor–multitarget tracking systems." Signal Processing 76, no. 2 (1999): 195–209. http://dx.doi.org/10.1016/s0165-1684(99)00008-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Balakrishnan, S. N., B. D. Tapley, and B. E. Schutz. "Enhancement of data separability in multisensor-multitarget tracking problems." Journal of Guidance, Control, and Dynamics 12, no. 6 (1989): 938–40. http://dx.doi.org/10.2514/3.20506.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Amelina, Natalia, Victoria Erofeeva, Oleg Granichin, et al. "Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances." IFAC-PapersOnLine 53, no. 2 (2020): 3589–95. http://dx.doi.org/10.1016/j.ifacol.2020.12.1756.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Xie, Jiahao, Shucai Huang, Daozhi Wei, and Zhaoyu Zhang. "Multisensor Dynamic Alliance Control Problem Based on Fuzzy Set Theory in the Mission of Target Detecting and Tracking." Journal of Sensors 2022 (September 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/7919808.

Full text

Abstract:

A multisensor alliance is established by the activation of tasks or occurrences. It is also characterized as a multisensor dynamic alliance since it originates with mission development and disintegrates with task accomplishment. To overcome the constraint that a single sensor can only gather a one-sided, little amount of erroneous target information, each sensor in the dynamic alliance has diverse information collection capabilities and implements a cooperative methodology to complete the target mission. This paper emphasizes on alliance formation in multisensor dynamic alliance control under

APA, Harvard, Vancouver, ISO, and other styles

8

Dezert, J., A. Tchamova, and P. Konstantinova. "The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context." Cybernetics and Information Technologies 15, no. 7 (2015): 88–98. http://dx.doi.org/10.1515/cait-2015-0092.

Full text

Abstract:

Abstract The main purpose of this paper is to apply and to test the performance of a new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the optimal data association solution for improving the performances of multisensor-multitarget tracking systems. The advantages of its implementation in an illustrative realistic surveillance context, when some of the association decisions are unreliable and doubtful and lead to potentially critical mistake, are discussed. A comparison with the results obtained o

APA, Harvard, Vancouver, ISO, and other styles

9

Zheng, Feng, Yu Tian, Weicong Zhan, Jiancheng Yu, and Kaizhou Liu. "A Gaussian mixture multiple-model belief propagation filter for multisensor-multitarget tracking." Signal Processing 220 (July 2024): 109473. http://dx.doi.org/10.1016/j.sigpro.2024.109473.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Xu, Zhixuan, Yu Wei, Xiaobao Qin, and Pengfei Guo. "The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection." Sensors 24, no. 5 (2024): 1508. http://dx.doi.org/10.3390/s24051508.

Full text

Abstract:

Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Multisensor-multitarget tracking systems"

1

LUH, PETER B., YAAKOV BAR-SHALOM, and KUO-CHU CHANG. "Centralized and Distributed Algorithms for Multitarget–Multisensor Tracking Systems." In Control and Dynamic Systems. Elsevier, 1989. http://dx.doi.org/10.1016/b978-0-12-012731-3.50007-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Multisensor-multitarget tracking systems"

1

Kim, Youngjoo, and Hyochoong Bang. "Airborne multisensor management for multitarget tracking." In 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2015. http://dx.doi.org/10.1109/icuas.2015.7152358.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Li, Y., L. W. Krakow, E. K. P. Chong, and K. N. Groom. "Dynamic Sensor Management for Multisensor Multitarget Tracking." In 2006 40th Annual Conference on Information Sciences and Systems. IEEE, 2006. http://dx.doi.org/10.1109/ciss.2006.286683.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Abdel-Aziz, A. M. "An all-neighbor fuzzy association approach in multisensor-multitarget tracking systems." In Proceedings of the Twenty-First National Radio Science Conference. IEEE, 2004. http://dx.doi.org/10.1109/nrsc.2004.240500.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Du, Wei, Huansheng Ning, Yuan Wei, and Jun Wang. "Fuzzy Double-Threshold Track Association Algorithm Using Adaptive Threshold in Distributed Multisensor-Multitarget Tracking Systems." In 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom). IEEE, 2013. http://dx.doi.org/10.1109/greencom-ithings-cpscom.2013.197.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Akselrod, D., A. Sinha, and T. Kirubarajan. "Hierarchical markov decision processes based distributed data fusion and collaborative sensor management for multitarget multisensor tracking applications." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4413675.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Mahler, Ronald. "Tracking “bunching” multitarget correlations." In 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2015. http://dx.doi.org/10.1109/mfi.2015.7295793.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Maier, Georg, Florian Pfaff, Christoph Pieper, et al. "Fast multitarget tracking via strategy switching for sensor-based sorting." In 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2016. http://dx.doi.org/10.1109/mfi.2016.7849538.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Pfaff, Florian, Gerhard Kurz, Christoph Pieper, et al. "Improving multitarget tracking using orientation estimates for sorting bulk materials." In 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2017. http://dx.doi.org/10.1109/mfi.2017.8170379.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Pollithy, Daniel, Marcel Reith-Braun, Florian Pfaff, and Uwe D. Hanebeck. "Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking." In 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2020. http://dx.doi.org/10.1109/mfi49285.2020.9235216.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Li, Fu-Xiang, and Yuan Zhu. "A Novelty Multitarget-Multisensor Tracking Algorithm with Out of Sequence Measurements for Automated Driving System on Highway Condition." In SAE 2023 Intelligent and Connected Vehicles Symposium. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7041.

Full text

Abstract:

<div class="section abstract"><div class="htmlview paragraph">Automated driving system is a multi-source sensor data fusion system. However different type sensor has different operating frequencies, different field of view, different detection capabilities and different sensor data transition delay. Aiming at these problems, this paper introduces the processing mechanism of out of sequence measurement data into the multi-target detection and tracking system based on millimeter wave radar and camera. After the comparison of ablation experiments, the longitudinal and lateral tracking

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!