Robust Kalman filter-based decentralised target search and prediction with topology maps

Robust Kalman filter-based decentralised target search and prediction with topology maps

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A novel distributed approach for searching and tracking of targets is presented for sensor network environments in which physical distance measurement using techniques such as signal strength is not feasible. The solution consists of a robust Kalman filter combined with a non-linear least-square method, and maximum likelihood topology maps. The primary input for estimating target location and direction of motion is provided by time stamps recorded by the sensor nodes when the target is detected within their sensing range. An autonomous robot following the target collects this information from sensors in its neighbourhood to determine its own path in search of the target. While the maximum likelihood topology coordinate space is a robust alternative to physical coordinates, it contains significant non-linear distortions when compared with physical distances between nodes. The authors overcome this using time stamps corresponding to target detection by nodes instead of relying on distances. The performance of the proposed algorithm is compared with recently proposed pseudo gradient algorithm based on hop count and received signal strength. Even though the proposed algorithm does not depend on distance measurements, the results show that it is able to track the target effectively even when the target changes its moving pattern frequently.


    1. 1)
      • 1. Grundel, D.A.: ‘Searching for a moving target: optimal path planning’. Proc. IEEE Networking, Sensing and Control, 2005, pp. 867872.
    2. 2)
      • 2. Deshpande, N., Grant, E., Henderson, T.C.: ‘Target localization and autonomous navigation using wireless sensor networks: a pseudogradient algorithm approach’, IEEE Syst. J., 2014, 8, (1), pp. 93103.
    3. 3)
      • 3. Gayan, S., Weeraddana, D.M., Gunathillake, A.: ‘Sensor network based adaptable system architecture for emergency situations’, Lect. Notes Inf. Theory, 2014, 2, (1), pp. 8591.
    4. 4)
      • 4. Can, Z., Demirbas, M.: ‘A survey on in-network querying and tracking services for wireless sensor networks’, Ad Hoc Netw., 2013, 11, (1), pp. 596610.
    5. 5)
      • 5. Beyme, S., Leung, C.: ‘Rollout algorithms for wireless sensor network-assisted target search’, IEEE Sens. J., 2015, 15, (7), pp. 38353845.
    6. 6)
      • 6. Lau, H., Huang, S., Dissanayake, G.: ‘Probabilistic search for a moving target in an indoor environment’. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, 2006, pp. 33933398.
    7. 7)
      • 7. Kagan, E., Goren, G., Ben-Gal, I.: ‘Probabilistic double-distance algorithm of search after static or moving target by autonomous mobile agent’. IEEE 26th Convention of Electrical and Electronics Engineers in Israel, 2010.
    8. 8)
      • 8. Wang, W., Ma, H., Wang, Y., et al: ‘Performance analysis based on least squares and extended Kalman filter for localization of static target in wireless sensor networks’, Ad Hoc Netw., 2015, 25, pp. 115.
    9. 9)
      • 9. Dhanapala, D.C., Jayasumana, A.P.: ‘Topology preserving maps: extracting layout maps of wireless sensor networks from virtual coordinates’, IEEE/ACM Trans. Netw., 2014, 22, (3), pp. 784797.
    10. 10)
      • 10. Gunathillake, A., Savkin, A., Jayasumana, A.P.: ‘Maximum likelihood topology maps for wireless sensor networks using an automated robot’. 41st IEEE Conf. Local Computer Networks, 2016.
    11. 11)
      • 11. Pathirana, P.N., Savkin, A.V., Jha, S.: ‘Location estimation and trajectory prediction for cellular networks with mobile base stations’, IEEE Trans. Veh. Technol., 2004, 53, (6), pp. 19031913.
    12. 12)
      • 12. Pathirana, P.N., Bulusu, N., Savkin, A.V., et al: ‘Node localization using mobile robots in delay-tolerant sensor networks’, IEEE Trans. Mobile Comput., 2005, 4, (3), pp. 285296.
    13. 13)
      • 13. Madsen, K., Nielsen, H.B., Tingleff, O.: ‘Methods for non-linear least squares problem’ (Technical University of Denmark, Lyngby, Denmark, 2004).
    14. 14)
      • 14. Lo, N., Berger, J., Noel, M.: ‘Toward optimizing static target search path planning’. IEEE Symp. Computational Intelligence for Security and Defence Applications, 2012, pp. 17.
    15. 15)
      • 15. Yoon, S., Soysal, O., Demirbas, M., et al: ‘Coordinated locomotion of mobile sensor networks’. 5th Annual IEEE Communications Society Conf. Sensor, Mesh and Ad Hoc Communications and Networks, 2008, pp. 126134.
    16. 16)
      • 16. Lu, X., Demirbas, M.: ‘Writing on water, a lightweight soft-state tracking framework for dense mobile ad hoc networks’. 5th IEEE Int. Conf. Mobile Ad Hoc and Sensor Systems, 2008, pp. 359364.
    17. 17)
      • 17. Yoon, S., Qiao, C.: ‘A new search algorithm using autonomous and cooperative multiple sensor nodes’. 26th IEEE Int. Conf. Computer Communications, 2007, pp. 937945.
    18. 18)
      • 18. Li, J., Jannotti, J., De-Couto, D.S.J., et al: ‘A scalable location service for geographic ad hoc routing’. 6th Annual Int. Conf. Mobile Computing and Networking, 2000.
    19. 19)
      • 19. Chong, E.K.P., Brewington, B.E.: ‘Decentralized rate control for tracking and surveillance networks’, Ad Hoc Netw., 2007, 5, (6), pp. 910928.
    20. 20)
      • 20. Bhatti, S., Xu, J., Memon, M.: ‘Clustering and fault tolerance for target tracking using WSNs’, IET Wirel. Sens. Syst., 2011, 1, (2), pp. 6673.
    21. 21)
      • 21. Pino-Povedano, S., Gonzalez-Serrano, F.J.: ‘Comparison of optimization algorithms in the sensor selection for predictive target tracking’, Ad Hoc Netw., 2014, 20, pp. 182192.
    22. 22)
      • 22. Njoya, A.N., Thron, C., Barry, J., et al: ‘Efficient scalable sensor node placement algorithm for fixed target coverage applications of WSNs’, IET Wirel. Sens. Syst., 2017, 7, (2), pp. 4454.
    23. 23)
      • 23. Li, W., Zhang, W.: ‘Sensor selection for improving accuracy of target localisation in wireless visual sensor networks’, IET Wirel. Sens. Syst., 2012, 2, (4), pp. 293301.
    24. 24)
      • 24. Misra, S., Singh, A., Chatterjee, S., et al: ‘QoS-aware sensor allocation for target tracking in sensor-cloud’, Ad Hoc Netw., 2015, 33, pp. 140153.
    25. 25)
      • 25. Eryildirim, A., Guldogan, M.B.: ‘A Bernoulli filter for extended target tracking using random matrices in a UWB sensor network’, IEEE Sens. J., 2016, 16, (11), pp. 43624373.
    26. 26)
      • 26. Sun, X., Koenig, S., Yeoh, W.: ‘Real-time adaptive A*’. Int. Joint Conf. Autonomous Agents and Multiagent Systems, 2008, pp. 281288.
    27. 27)
      • 27. Ishida, T., Korf, R.E.: ‘Moving-target search: a real-time search for changing goals’, IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, (6), pp. 609619.
    28. 28)
      • 28. Kagan, E., , Ben-Gal, I.: ‘Moving target search algorithm with informational distance measures’, Open Appl. Inf. J., 2013, 6, pp. 110.
    29. 29)
      • 29. Hazim, T., Karagiannidis, G.K., Tsiftsis, T.A.: ‘Probability of early detection of ultra-wideband positioning sensor networks’, IET Wirel. Sens. Syst., 2011, 1, (3), pp. 123128.
    30. 30)
      • 30. Dong, B., Wang, X.: ‘Adaptive mobile positioning in WCDMA networks’, EURASIP J. Wirel. Commun. Netw., 2005, 2005, (3), pp. 38773881.
    31. 31)
      • 31. Mahfouz, S., Mourad-Chehade, F., Honeine, P., et al: ‘Nonparametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks’, IEEE Sens. J., 2016, 16, (7), pp. 21152126.
    32. 32)
      • 32. Yang, X., Zhang, W.A., Yu, L., et al: ‘Multi-rate distributed fusion estimation for sensor network-based target tracking’, IEEE Sens. J., 2016, 16, (5), pp. 12331242.
    33. 33)
      • 33. Marian, T., Mokryn, O.O., Shavitt, Y.: ‘Sensing clouds: a distributed cooperative target tracking with tiny binary noisy sensors’, Ad Hoc Netw., 2013, 11, (8), pp. 23562366.
    34. 34)
      • 34. Liu, B.H., Chen, M.L., Tsai, M.J.: ‘Message-efficient location prediction for mobile objects in wireless sensor networks using a maximum likelihood technique’, IEEE Trans. Comput., 2010, 60, pp. 865878.
    35. 35)
      • 35. Zaidi, Z.R., Mark, B.L.: ‘Real-time mobility tracking algorithms for cellular networks based on Kalman filtering’, IEEE Trans. Mob. Comput., 2005, 4, (2), pp. 195208.
    36. 36)
      • 36. Xiao, Q., Xiao, B., Cao, J., et al: ‘Multihop range-free localization in anisotropic wireless sensor networks: a pattern-driven scheme’, IEEE Trans. Mob. Comput., 2010, 9, (11), pp. 15921607.
    37. 37)
      • 37. Chengdong, W., Shifeng, C., Yunzhou, Z., et al: ‘A RSSI-based probabilistic distribution localization algorithm for wireless sensor network’. 6th IEEE Joint Int. Information Technology and Artificial Intelligence Conf., 2011, vol. 1, pp. 333337.
    38. 38)
      • 38. Turner, D., Savage, S., Snoeren, A.C.: ‘On the empirical performance of self-calibrating Wifi location systems’. IEEE 36th Conf. Local Computer Networks, 2011, pp. 7684.
    39. 39)
      • 39. Pagano, S., Peirani, S., Valle, M.: ‘Indoor ranging and localisation algorithm based on received signal strength indicator using statistic parameters for wireless sensor networks’, IET Wirel. Sens. Syst., 2015, 5, (5), pp. 243249.
    40. 40)
      • 40. Gunathillake, A., Savkin, A., Jayasumana, A.P.: ‘Decentralized time-based target searching algorithm using sensor network topology maps’. 12th IEEE Int. Workshop on Performance and Management of Wireless and Mobile Networks, 2016.
    41. 41)
      • 41. Petersen, I.R., Savkin, A.V.: ‘Robust Kalman filtering for signals and systems with large uncertainties’, 1999.
    42. 42)
      • 42. ElHalawany, B.M., Abdel-Kader, H.M., TagEldeen, A., et al: ‘Modified a* algorithm for safer mobile robot navigation’. Int. Conf. Modelling, Identification Control, 2013, pp. 7478.
    43. 43)
      • 43. Nhat, V.D.M., Vo, N., Challa, S., et al: ‘Nonmetric MDS for sensor localization’. 3rd Int. Symp. Wireless Pervasive Computing, 2008, pp. 396400.

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