Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)

Volume 8 , Issue 2 , PP: 51-70, 2022 | Cite this article as | XML | Html | PDF | Full Length Article

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

A. Audumbar Pise 1 * , Radhika Kapshikar 2

  • 1 School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, Gauteng, South Africa - (anil.pise@thefinalmile.com)
  • 2 Department of Computer Science and Software Engineering, University of Johannesburg, South Africa - ( 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 :
    Audumbar, A.. , Kapshikar, Radhika. Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques. Journal of Fusion: Practice and Applications, vol. 8, no. 2, 2022, pp. 51-70. DOI: https://doi.org/10.54216/FPA.080205
    Audumbar, A. Kapshikar, R. (2022). Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques. Journal of Fusion: Practice and Applications, 8( 2), 51-70. DOI: https://doi.org/10.54216/FPA.080205
    Audumbar, A.. Kapshikar, Radhika. Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques. Journal of Fusion: Practice and Applications 8, no. 2 (2022): 51-70. DOI: https://doi.org/10.54216/FPA.080205
    Audumbar, A. , Kapshikar, R. (2022) . Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques. Journal of Fusion: Practice and Applications , 8( 2) , 51-70 . DOI: https://doi.org/10.54216/FPA.080205
    Audumbar A. , Kapshikar R. [2022]. Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques. Journal of Fusion: Practice and Applications. 8( 2): 51-70. DOI: https://doi.org/10.54216/FPA.080205
    Audumbar, A. Kapshikar, R. "Multi-Sensor Data Fusion for Target Tracking Using Machine Learning Techniques," Journal of Fusion: Practice and Applications, vol. 8, no. 2, pp. 51-70, 2022. DOI: https://doi.org/10.54216/FPA.080205