The authors propose a new algorithm for distributed target tracking by sensor networks with sensors having limited sensing range (radars, sonars, cameras), using an adaptive multi-step consensus scheme. The proposed distributed adaptation strategy represents the core element of the algorithm, providing: (i) asymptotic consensus gains giving the desired importance to the nodes observing the target, and (ii) fast convergence rate of the consensus scheme, enabling efficient implementation. Another contribution of this study is a theoretical study of the algorithm properties, including asymptotic stability and reduction of noise influence. The given simulation results show that the proposed algorithm outperforms the existing algorithms, preserving, at the same time, much lower communication and computation requirements.

References

1. 1)
  - 24. Boers, Y., Driessen, H.: ‘Modified Riccati equation and its application to target tracking’, IEE Proc. Radar Sonar Navig., 2006, 153, pp. 7–12.
2. 2)
  - 1. Bar-Shalom, Y., Li, X.R.: ‘Multitarget-multisensor tracking: principles and techniques’ (YBS Publishing, Storrs, CT, 1995).
3. 3)
  - 23. Fortmann, T.E., Bar-Shalom, Y., Scheffe, M., et al: ‘Detection thresholds for tracking in clutter – aconnection between estimation and signal processing’, IEEE Trans. Autom. Control, 1985, 30, pp. 221–229.
4. 4)
  - 5. Stanković, S.S., Stanković, M.S., Stipanović, D.M.: ‘Consensus based overlapping decentralized estimation with missing observations and communication faults’, Automatica, 2009, 45, pp. 1397–1406.
5. 5)
  - 18. Godsil, C., Royle, G.: ‘Algebraic graph theory’ (Springer Verlag, New York, 2001).
6. 6)
  - 6. Ilić, N., Stanković, M.S., Stanković, S.S.: ‘Adaptive consensus-based distributed target tracking in sensor networks with limited sensing range’, IEEE Trans. Control Syst. Tech., 2014, 22, pp. 778–785.
7. 7)
  - 4. Kamal, A.T., Farrell, J.A., Roy-Chowdhury, A.K.: ‘Information weighted consensus filters and their application in distributed camera networks’, IEEE Trans. Autom. Control, 2013, 58, pp. 3112–3125.
8. 8)
  - 13. Cattivelli, F.S., Sayed, A.H.: ‘Diffusion strategies for distributed Kalman filtering and smoothing’, IEEE Trans. Autom. Control, 2010, 55, pp. 2069–2084.
9. 9)
  - 26. Boyd, S., Ghosh, A., Prabhakar, B., et al: ‘Randomized Gossip algorithms’, IEEE Trans. Inf. Theory, 2006, 52, pp. 2508–2530.
10. 10)
  - 22. Bitmead, R.R., Gevers, M.R., Petersen, I.R.: ‘Monotonicity and stabilizability properties of solutions of the Riccati difference equation: propositions, lemmas, theorems, fallacious conjectures and counterexamples’, Syst. Control Lett., 1985, 5, pp. 309–315.
11. 11)
  - 15. Stanković, M.S., Ilić, N., Stanković, S.S.: ‘Distributed stochastic approximation: weak convergence and network design’, IEEE Trans. Autom. Control, 2016, 61, pp. 4069–4074.
12. 12)
  - 17. Xiao, L., Boyd, S.: ‘Fast linear iterations for distributed averaging’, Syst. Control Lett., 2004, 53, pp. 65–78.
13. 13)
  - 20. Amato, F., Celentano, G., Garofalo, F.: ‘New sufficient conditions for the stability of slowly varying linear systems’, IEEE Trans. Autom. Control, 1993, 38, pp. 1409–1411.
14. 14)
  - 19. Stanković, S.S., Ilić, N., Ali, K.O.A., et al: ‘Distributed consensus based IPDAF for tracking in vision networks’. Proc. IEEE Conf. Multisensor Fusion and Integration for Intelligent System (MFI), Baden-Baden, Germany, 2016, pp. 304–309.
15. 15)
  - 10. Tsitsiklis, J.N., Bertsekas, D.P., Athans, M.: ‘Distributed asynchronous deterministic and stochastic gradient optimization algorithms’, IEEE Trans. Autom. Control, 1986, 31, pp. 803–812.
16. 16)
  - 2. Olfati-Saber, R.: ‘Distributed Kalman filtering for sensor networks’. Proc. IEEE Conf. Decision and Control, New Orleans, LA, USA, 2007, pp. 5492–5498.
17. 17)
  - 25. Chen, M.Z.Q., Zhang, L., Su, H., et al: ‘Stabilizing solution and parameter dependence of modified algebraic Riccati equations with application to discrete-time network synchronization’, IEEE Trans. Autom. Control, 2016, 61, pp. 228–233.
18. 18)
  - 11. Horn, R.A., Johnson, C.A.: ‘Matrix analysis’ (Cambridge Univ. Press, Cambridge, 1985).
19. 19)
  - 14. Battistelli, G., Chisci, L.: ‘Stability of consensus extended Kalman filter for distributed state estimation’, Automatica, 2016, 68, pp. 169–178.
20. 20)
  - 16. Olshevsky, A., Tsitsiklis, J.N.: ‘Convergence speed in distributed consensus and avergaing’, SIAM Rev., 2011, 53, pp. 747–772.
21. 21)
  - 3. Olfati-Saber, R., Sandell, N.F.: ‘Distributed tracking in sensor networks with limited sensing range’. Proc. American Control Conf., Seattle, WA, USA, 2008, pp. 3157–3162.
22. 22)
  - 12. Ren, W., Beard, R.W.: ‘Consensus seeking in multi-agent systems using dynamically changing interaction topologies’, IEEE Trans. Autom. Control, 2005, 50, pp. 655–661.
23. 23)
  - 9. Olfati-Saber, R., Fax, A., Murray, R.: ‘Consensus and cooperation in networked multi-agent systems’, Proc. IEEE, 2007, 95, pp. 215–233.
24. 24)
  - 21. Rugh, W.J.: ‘Linear system theory’ (Prentice Hall, Upper Saddle River, NJ, 1996).
25. 25)
  - 8. Hlinka, O., Sluciak, O., Hlawatsch, F., et al: ‘Likelihood consensus and its application to distributed particle filtering’, IEEE Trans. Signal Process., 2012, 60, pp. 4334–4349.
26. 26)
  - 7. Sinopoli, B., Schenato, L., Franceschetti, M., et al: ‘Kalman filtering with intermittent observations’, IEEE Trans. Autom. Control, 2004, 49, pp. 1453–1464.

Distributed target tracking in sensor networks using multi-step consensus

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