151 115
Full Length Article
Fusion: Practice and Applications
Volume 8 , Issue 2, PP: 51-70 , 2022 | Cite this article as | XML | Html |PDF

Title

Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques

Authors Names :   A. Audumbar Pise   1 *     Radhika Kapshikar   2  

1  Affiliation :  School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, Gauteng, South Africa

    Email :  anil.pise@thefinalmile.com


2  Affiliation :  Department of Computer Science and Software Engineering, University of Johannesburg, South Africa

    Email :   radhika.Kapshikar@multichoice.co.za



Doi   :   https://doi.org/10.54216/FPA.080205

Received: May 05, 2022 Accepted: September 14, 2022

Abstract :

Target detection using multi fusion data is one of the common techniques used in military as well as defence units. The usage of a wide variety of sensors is now possible due to modern data fusion technology. The major problem is the existing multi-sensor fusion technique is loss of data and delay is message transfer. To overcome the existing problems, proposed work includes optimization, machine learning, and soft computing techniques. Multi Sensor Data Fusion (MSDF) is becoming an increasingly significant field of study and is being explored by a broad range of individuals. Data defects, outliers, misleading data, conflicting data, and data association are some data fusion concerns. In addition to the statistical advantages of more independent observations, the precision of an observation may be improved by using a variety of different types of sensors. Target tracking has earned a lot of attention in recent years in the realm of surveillance and measurement systems, particularly those in which the state of a target is approximated based on measurements. Academics as well as implementers in the fields of radar, sonar, and satellite surveillance are interested in the bearings-only tracking (BOT) problem. The BOT is the sole option available in many surveillance systems, such as those found aboard submarines. Significant difficulties arise because of the constrained observability of target states based only on bearing measurements. The work that is suggested tackles the limitations of EKF and its derivatives in controlling MSDF within the context of BOT. Specifically, the study identifies divergence as a primary challenge and works to devise solutions for it. It is recommended that two key methods of fusion, data level and feature level (or state level), be investigated in depth. This is in recognition of the fact that the MSDF may increase observability, thereby reducing the tendency of the tracking algorithm to diverge and realizing a better estimate of the states. The Information Filter, which is a casting of the Kalman Filter, and its expansions are employed via extensive simulation to lessen the influence of initial assumptions on the convergence of MSDF tracking algorithms. This is accomplished by using the Kalman Filter.

Keywords :

Tree-Based Fusion Technique; Potential Energy Efficient Data Fusion; LEACH; Wireless Sensor Network.

References :

[1] D. L. Hall and J. Llinas, ―An introduction to multisensor data fusion,‖ Proceedings of the IEEE,

vol. 85, no. 1, pp. 6-23, 1997. DOI: 10.1109/5.554205.

[2] B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, ―Multisensory data fusion: a review of

the state-of-the-art,‖ Inform. Fuss., vol. 14, no. 1, pp. 28-44, 2013. DOI:

https://doi.org/10.1016/j.inffus.2011.08.001.

[3] S.-L. Sun and Z.-L. Deng, ―Multi-sensor optimal information fusion Kalman filter,‖

Automatica, vol. 40, no. 6, pp. 1017–1023, 2004. DOI:

https://doi.org/10.1016/j.automatica.2004.01.014.

[4] P. Vadakkepat and L. Jing, ―Improved particle filter in sensor fusion for tracking randomly

moving object,‖ IEEE Trans. Instrum. Meas., vol. 55, no. 5, pp. 1823-1832, Oct. 2006. DOI:

10.1109/TIM.2006.881569.

[5] J. Yuan, H. Chen, F. Sun, and Y. Huang, ―Multisensor information fusion for people tracking

with a mobile robot: A particle filtering approach,‖ IEEE Trans. Instrum. Meas., vol. 64, no. 9,

pp. 2427-2442, Sep. 2015. DOI: 10.1109/TIM.2015.2407512.

[6] R. C. Luo, Y. T. Chou, C. T. Liao, C. C. Lai, and A. C. Tsai, ―NCCU security warrior: An

intelligent security robot system,‖ in Proc. 33rd Annu. Conf. IEEE Ind. Electron. Soc., Nov.

2007, pp. 2960–2965. DOI: 10.1109/IECON.2007.4460380.

[7] M. Kam, X. Zhu, and P. Kalata, ―Sensor fusion for mobile robot navigation,‖ Proceedings of the

IEEE, vol. 85, no. 1, pp. 108-119, Jan. 1997. DOI: 10.1109/JPROC.1997.554212.

[8] E. Waltz and J. Llinas, Multisensor Data Fusion. Norwood, MA, USA: Artech House, 1990

ISBN: 978-0890-06277-7.

[9] M. Markin, C. Harris, M. Bernhardt, J. Austin, M. Bedworth, P. Greenway, R. Johnston, A.

Little, and D. Lowe, ―Technology foresight on data fusion and data processing,‖ Publication of

The Royal Aeronautical Society, 1997.

[10] R. C. Luo and M. G. Kay, ―A tutorial on multisensor integration and fusion,‖ in Proc. 16th

Annu. Conf. IEEE Ind. Electron. Soc., 1990, vol. 1, pp. 707–722. DOI:

10.1109/IECON.1990.149228.

[11] R. Alami, R. Chatila, S. Fleury, M. Ghallab, and F. Ingrand, ―An architecture for autonomy,‖ Int.

Journ. Robot. Res., vol. 17, no. 4, pp. 315–337, Apr. 1998.

[12] M. D. Bedworth and J. O’Brien, ―The omnibus model: A new architecture for data fusion?‖ in

Proc. 2nd Int. Conf. Inform. Fus. (FUSION’99), Helsinki, Finland, Jul. 1999. DOI:

https://doi.org/10.1177/027836499801700402.

[13] R. C. Luo and C.-C. Chang, ―Multisensor fusion and integration: A review on approaches and its

applications in mechatronics,‖ IEEE Trans. Ind. Informat., vol. 8, no. 1, pp. 1385- 1393, Mar.

2008. DOI: 10.1109/TII.2011.2173942.

[14] A. Agah, ―Human interactions with intelligent systems: Research taxonomy,‖ Comput. Electric.

Eng., vol. 27, no. 1, pp. 71-107, Nov. 2000. DOI: https://doi.org/10.1016/S0045-

7906(00)00009-4.

[15] A. Grau, M. Indri, L. Lo Bello, T. Sauter ―Industrial robotics in factory automation: From the

early stage to the Internet of Things,‖ in Proc. 43rd Annu. Conf. IEEE Ind. Electron. Soc.,

Beijing, China, Nov. 2017, pp. 6159–6164. DOI: 10.1109/IECON.2017.8217070.

[16] S. Y. Lee, K. Y. Lee, S. H. Lee, J. W. Kim, and C. S. Han, ―Human–robot cooperation control

for installing heavy construction materials,‖ Autonom. Robots, vol. 22, no. 3, pp. 305–319, Mar.

2007. DOI: 10.1007/s10514-006-9722-z.

[17] H. Wang and X. Liu, ―Haptic interaction for mobile assistive robots,‖ IEEE Trans. Instrum.

Meas., vol. 60, no. 11, pp. 3501–3509, Nov. 2011. DOI: 10.1109/TIM.2011.2161141.

[18] K. Kosuge and Y. Hirata, ―Human–robot interaction,‖ in Proc. IEEE Int. Conf. ROBIO,

Shenyang, China, Aug. 2004, pp. 8–11. DOI: 10.1109/ROBIO.2004.1521743.

[19] L. Iocchi, J. Ruiz-del-Solar, and T. van der Zant, ―Domestic service robots in the real world,‖ J.

Intell. Robot. Syst., vol. 66, no. 1/2, pp. 183–186, Apr. 2012. DOI:

https://doi.org/10.1007/s10846-011-9628-7.

[20] P. Vaddakkepat, P. Lim, L. C. De Silva, L. Jing, and L. L. Ling, ―Multimodal approach to

human-face detection and tracking,‖ IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1385- 1393,

Mar. 2008. DOI: 10.1109/TIE.2007.903993.

[21] C. Micheloni, G. L. Foresti, C. Piciarelli, and L. Cinque, ―An autonomous vehicle for video

surveillance of indoor environments,‖ IEEE Trans. Veh. Technol., vol. 56, no. 2, pp. 487–498,

Mar. 2007. DOI: 10.1109/TVT.2007.891478.

[22] O. Zoidi, A. Tefas, and I. Pitas, ―Visual object tracking based on local steering kernels and color

histograms,‖ IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 5, pp. 870–882, May 2013.

DOI: 10.1109/TCSVT.2012.2226527.

[23] W. Choi, C. Pantofaru, and S. Savarese, ―A general framework for tracking multiple people from

a moving Camera,‖ IEEE Trans. Patt. Anal. Mach. Intell., vol. 35, no. 7, pp. 487–498, Jul. 2013.

DOI: 10.1109/TPAMI.2012.248.

[24] Z. Wang, L. Zheng, and H. Ye, ―Design and implementation of a following robot system based

on monocular vision,‖ in Proc. IEEE 2nd Adv. Inform. Tech., Electron. Automat. Cont. Conf.

(IAEAC), Chongqing, China, Mar. 2017, pp. 1360–1363. DOI: 10.1109/IAEAC.2017.8054236.

[25] C.-S. Fahn, C.-P. Lee, and Y.-S. Yeh, ―A real-time pedestrian legs detection and tracking system

used for autonomous mobile robots,‖ in Proc. Int. Conf. Appl. Sys. Innov. (ICASI), Sapporo,

Japan, May 2017, pp. 1122–1125. DOI: 10.1109/ICASI.2017.7988208.

[26] P. Kondaxakis, H. Baltzakis, and P. Trahanias, ―Learning moving objects in a multi-target

tracking scenario for mobile robots that use laser range measurements,‖ in Proc. IEEE/RSJ Int.

Conf. Intell. Robots Syst., St. Louis, MO, USA, Oct. 2009, pp. 1667–1672. DOI:

10.1109/IROS.2009.5353913.

[27] C. T. Chou, J.-Y. Li, M.-F. Chang, and L. C. Fu, ―Multi-robot cooperation based human tracking

system using laser range finder,‖ in Proc. IEEE Int. Conf. Robot. Autom., Shanghai, China, May

2011, pp. 532–537. DOI: 10.1109/ICRA.2011.5980484.

[28] H. T. Duong and Y. S. Suh, ―Human gait tracking for normal people and walker users using a

2D LiDAR,‖ IEEE Sens. Journ., vol. 20, no. 11, pp. 6191-6199, Jun. 2020. DOI:

10.1109/JSEN.2020.2975129.

[29] N. Kawarazaki, L. T. Kuwae, and T. Yoshidome, ―Development of human following mobile

robot system using laser range scanner,‖ Proced. Comp. Sci., vol. 76, pp. 455-460, 2015. DOI:

https://doi.org/10.1016/j.procs.2015.12.310.

[30] D. Li, L. Li, Y. Li, F. Yang, and X. Zuo, ―A multi-type features method for leg detection in 2-D

laser range data,‖ IEEE Sens. Journ., vol. 18, no. 4, pp. 1675-1684, Feb. 2018. DOI:

10.1109/JSEN.2017.2784900.

[31] N. Bellotto and H. Hu, ―Multisensor-based human detection and tracking for mobile service

robots,‖ IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 1, pp. 167-181, Feb. 2009.

DOI: 10.1109/TSMCB.2008.2004050

[32] C.-C. Wang, C. Thorpe, M. Hebert, S. Thrun, and H. Durrant-Whyte, ―Simultaneous

localization, mapping and moving object tracking,‖ Int. J. Robot. Res., vol. 26, no. 9, pp. 889-

916, Sep. 2007. DOI: https://doi.org/10.1177/0278364907081229.

[33] K. Rebai, A. Benabderrahmane, O. Azouaoui, and N. Ouadah, ―Moving obstacles detection and

tracking with laser range finder,‖ in’Proc. Int. Conf. Adv. Robot., Munich, Germany, Jun. 2009,

pp. 1–6, ISBN: 978-3839-60035-1.

[34] M. Montemerlo, S. Thun, and W. Whittaker, ―Conditional particle filters for simultaneous

mobile robot localization and people-tracking,‖ in Proc. IEEE Int. Conf. Robot. Autom.,

Washington, DC, USA, May 2002, pp. 695–701. DOI: 10.1109/ROBOT.2002.1013439.

[35] Z. Xu, R. Fitch, and S. Sukkarieh, ―Decentralised coordination of mobile robots for target

tracking with learnt utility models,‖ in Proc. IEEE Int. Conf. Robot. Autom., Karlsruhe,

Germany, May 2013, pp. 2014–2020. DOI: 10.1109/ICRA.2013.6630846.

[36] D. Schulz, W. Burgard, and D. Fox, ―People tracking with mobile robots using samplebased

joint probabilistic data association filters,‖ Int. J. Robot. Res., vol. 22, no. 2, pp. 99- 116, Feb.

2003. DOI: https://doi.org/10.1177/0278364903022002002.

[37] R. C. Luo, Y. J. Chen, C. T. Liao, and A. C. Tsai, ―Mobile robot based human detection and

tracking using range and intensity data fusion,‖ in Proc. IEEE Workshop Adv. Robot. Social

Impacts, Hsinchu, Taiwan, Dec. 2007, pp. 1–6. DOI: 10.1109/ARSO.2007.4531416.

[38] M. Kleinehagenbrock, S. Lang, J. Fritsch, F. Lomker, G. A. Fink, and G. Sagerer, ―Person

tracking with a mobile robot based on multi-modal anchoring,‖ in Proc. 11th IEEE Int.

Workshop Robot Human Interact. Commun., Berlin, Germany, Sep. 2002, pp. 423-429. DOI:

10.1109/ROMAN.2002.1045659.


Cite this Article as :
A. Audumbar Pise , Radhika Kapshikar, Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques, Fusion: Practice and Applications, Vol. 8 , No. 2 , (2022) : 51-70 (Doi   :  https://doi.org/10.54216/FPA.080205)