Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Two novel sensor control schemes for multi-target tracking via delta generalised labelled multi-Bernoulli filtering

  • Author(s): Guanghua Zhang 1 ; Feng Lian 1 ; Chongzhao Han 1 ; Hui Chen 2 ; Na Fu 3
    • View affiliations
  • Source: Volume 12, Issue 9, December 2018, p. 1131 – 1139
    DOI:  10.1049/iet-spr.2018.5124 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 24/08/2017, Accepted 08/05/2018, Revised 04/05/2018, Published 27/07/2018

The study addresses the sensor control problem for multi-target tracking via delta generalised labelled multi-Bernoulli (-GLMB) filter, and proposes two novel single-sensor control schemes. One is that the Rényi divergence is used as the objective function to measure the information gain between the predicted and posterior densities of the -GLMB filter, and it is superior for the overall performance of the system. Since most of the sensor control schemes, including the scheme the authors proposed, are faced the curse of computation, thus the other novel scheme is proposed. This scheme, in which the sum of the statistical distances between the predicted states of targets and sensor is used as the objective function, evades the updated step of the multi-target filter, when computing the objective function for each admissible action. Moreover, these two sensor control schemes are applied to a distributed multi-sensor system, in which the proposed schemes are used for each sensor node and the generalised covariance intersection method is used to compute the fused multi-target posterior density. Finally, they adopt the sequential Monte-Carlo method in bearing and range multi-target tracking scenarios to illustrate the effectiveness of the proposed methods.

Inspec keywords: distributed sensors; tracking filters; target tracking; Monte Carlo methods

Other keywords: multitarget filter; sensor node; single-sensor control schemes; delta generalised labelled multiBernoulli filtering; sensor control problem; distributed multisensor system; multi-target tracking; fused multitarget posterior density

Subjects: Monte Carlo methods; Sensing devices and transducers; Transducers and sensing devices; Filtering methods in signal processing; Monte Carlo methods

References

    1. 1)
      • 25. Hoang, H.G., Ba, T.V.: ‘Sensor management for multi-target tracking via multi-Bernoulli filtering’, Automatica, 2014, 50, (4), pp. 1135–1142.
    2. 2)
      • 7. Vo, B.N., Ma, W.K.: ‘The Gaussian mixture probability hypothesis density filter’, IEEE Trans. Signal Process., 2006, 54, (11), pp. 4091–4104.
    3. 3)
      • 24. Ristic, B., Vo, B.N., Clark, D.: ‘A note on the reward function for PHD filters with sensor control’, IEEE Trans. Aerosp. Electron. Syst., 2011, 47, (2), pp. 1521–1529.
    4. 4)
      • 23. Ristic, B., Vo, B.T.: ‘Sensor control for multi-object state-space estimation using random finite sets’, Automatica, 2010, 46, (11), pp. 1812–1818.
    5. 5)
      • 16. Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A.: ‘Robust multi-Bernoulli sensor selection for multi-target tracking in sensor networks’, IEEE Signal Process. Lett., 2013, 20, (12), pp. 1167–1170.
    6. 6)
      • 32. Wang, B., Yi, W., Hoseinnezhad, R., et al: ‘Distributed fusion with multi-Bernoulli filter based on generalized covariance intersection’, IEEE Trans. Signal Process., 2015, 65, (1), pp. 242–255.
    7. 7)
      • 4. Hero, A.O., Kreucher, C.M., Blatt, D.: ‘Information theoretic approaches to sensor management’, in Hero, A.O., Castañón, D., Cochran, D., et al: ‘Foundations and applications of sensor management’ (Springer, New York, 2008), pp. 33–57.
    8. 8)
      • 3. Krishnamurthy, V.: ‘Algorithms for optimal scheduling and management of hidden Markov model sensors’, IEEE Trans. Signal Process., 2002, 50, (6), pp. 1382–1397.
    9. 9)
      • 11. Vo, B.N., Vo, B.T., Phung, D.: ‘Labeled random finite sets and the Bayes multi-target tracking filter’, IEEE Trans. Signal Process., 2014, 62, (24), pp. 6554–6567.
    10. 10)
      • 29. Fantacci, C., Vo, B.N., Vo, B.T., et al: ‘Consensus labeled random finite set filtering for distributed multi-object tracking’, 2015, preprint available online at arXiv:1501.01579v2, http://arxiv.org/abs/1501.01579.
    11. 11)
      • 34. Vo, B.N., Vo, B.T., Hoang, H.G.: ‘An efficient implementation of the generalized labeled multi-Bernoulli filter’, IEEE Trans. Signal Process., 2017, 65, (8), pp. 1975–1987.
    12. 12)
      • 9. Vo, B.T., Vo, B.N., Cantoni, A.: ‘The cardinality balanced multi-target multi-Bernoulli filter and its implementations’, IEEE Trans. Signal Process., 2009, 57, (2), pp. 409–423.
    13. 13)
      • 1. Hero, A.O., Castañón, D., Cochran, D., et al: ‘Foundations and applications of sensor management’ (Springer, New York, 2008).
    14. 14)
      • 13. Mullane, J., Vo, B.N., Adams, M., et al: ‘Random finite sets for robot mapping and SLAM’ (Springer, Berlin Heidelberg, 2011).
    15. 15)
      • 35. Schuhmacher, D., Vo, B.T., Vo, B.N.: ‘A consistent metric for performance evaluation of multi-object filters’, IEEE Trans. Signal Process., 2008, 56, (8), pp. 3447–3457.
    16. 16)
      • 5. Mahler, R.: ‘Statistical multisource-multitarget information fusion’ (Artech House, Norwood, MA, USA, 2007).
    17. 17)
      • 26. Kreucher, C., Hero, A.O., Kastella, K.: ‘A comparison of task driven and information driven sensor management for target tracking’. Proc. 44th IEEE Conf. Decision and Control, Seville, Spain, December 2005, pp. 4004–4009.
    18. 18)
      • 12. Ristic, B.: ‘Particle filters for random set models’ (Springer, New York, 2013).
    19. 19)
      • 18. Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A.: ‘Control of sensor with unknown clutter and detection profile using multi-Bernoulli filter’. Proc. Int. Conf. Information Fusion, Istanbul, Turkey, July 2013, pp. 1021–1028.
    20. 20)
      • 8. Mahler, R.: ‘PHD filters of higher order in target number’, IEEE Trans. Aerosp. Electron. Syst., 2008, 43, (4), pp. 1523–1543.
    21. 21)
      • 10. Vo, B.T., Vo, B.N.: ‘Labeled random finite sets and multi-object conjugate priors’, IEEE Trans. Signal Process., 2013, 61, (13), pp. 3460–3475.
    22. 22)
      • 14. Zhang, G.H., Lian, F., Han, C.Z., et al: ‘Convergence analysis for the Gaussian mixture implementation of the CBMeMBer filter’, Control Theory Appl., 2016, 33, (10), pp. 1405–1411.
    23. 23)
      • 21. Hoang, H.G., Vo, B.N., Ba, T.V., et al: ‘The Cauchy-Schwarz divergence for Poisson point processes’, IEEE Trans. Inf. Theory, 2015, 61, (8), pp. 240–243.
    24. 24)
      • 31. Battistelli, G., Chisci, L., Fantacci, C., et al: ‘Consensus CPHD filter for distributed multitarget tracking’, IEEE J. Sel. Topics Signal Process., 2013, 7, (3), pp. 508–520.
    25. 25)
      • 19. Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A.: ‘Sensor control for multi-object tracking using labeled multi-Bernoulli filter’, IEEE Trans. Signal Process., 2014, 63, (20), pp. 1–8.
    26. 26)
      • 6. Mahler, R.: ‘Advances in statistical multisource-multitarget information fusion’ (Artech House, Norwood, MA, USA, 2014).
    27. 27)
      • 20. Kampa, K., Hasanbelliu, E., Principe, J.C.: ‘Closed-form Cauchy-schwarz PDF divergence for mixture of gaussians’. Proc. Int. Joint Conf. Neural Networks, San Jose, CA, USA, July–August 2011, pp. 2578–2585.
    28. 28)
      • 30. Battistelli, G., Chisci, L., Fantacci, C., et al: ‘Distributed fusion of multitarget densities and consensus PHD/CPHD filters’. Proc. SPIE, Signal Processing, Sensor/Information Fusion, and Target Recognition, Baltimore, MD, USA, April 2015, pp. 1–15.
    29. 29)
      • 33. Beard, M., Vo, B.T., Vo, B.N., et al: ‘Sensor control for multi-target tracking using Cauchy-schwarz divergence’. Proc. Int. Conf. Information Fusion, Washington, DC, USA, July 2015, pp. 937–944.
    30. 30)
      • 27. Jiang, M., Yi, W., Kong, L.: ‘Multi-sensor control for multi-target tracking using Cauchy-schwarz divergence’. Proc. Int. Conf. Information Fusion, Heidelberg, Germany, July 2016, pp. 2059–2066.
    31. 31)
      • 2. Lovejoy, W.S.: ‘A survey of algorithmic methods for partially observed Markov decision processes’, Ann. Oper. Res., 1991, 28, (1), pp. 47–65.
    32. 32)
      • 22. Mahler, R.: ‘Multitarget sensor management of dispersed mobile sensors’, in Grundel, D., Murphey, R., Pardalos, P.M.: ‘Theory and algorithms for cooperative systems’ (World Scientific, River Edge, New Jersey, 2004), pp. 239–310.
    33. 33)
      • 15. Zhang, G.H., Han, C.Z., Lian, F., et al: ‘Cardinality balanced multi-target multi-Bernoulli filter for pairwise Markov model’, ACTA Automat. Sin., 2017, 43, (12), pp. 2100–2108.
    34. 34)
      • 17. Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A.: ‘Multi-Bernoulli sensor control for multi-target tracking’. Proc. IEEE Int. Conf. Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, VIC, Australia, April 2013, pp. 312–317.
    35. 35)
      • 28. Wang, X., Hoseinnezhad, R., Gostar, A.K., et al: ‘Multi-sensor control for multi-object Bayes filters’, Signal Process., 2018, 142, pp. 260–270, doi: 10.1016/j.sigpro.2017.07.031.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5124
Loading

Related content

content/journals/10.1049/iet-spr.2018.5124
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address