Non-myopic sensor scheduling to track multiple reactive targets
- Author(s): Zi-ning Zhang 1, 2 and Gan-lin Shan 1
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View affiliations
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Affiliations:
1:
Electronic Engineering Department, Shijiazhuang Mechanical Engineering College, No. 97, Hepingxilu Road, Shijiazhuang 050003, People's Republic of China;
2: Aerospace Control Center, Beijing 100094, People's Republic of China
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Affiliations:
1:
Electronic Engineering Department, Shijiazhuang Mechanical Engineering College, No. 97, Hepingxilu Road, Shijiazhuang 050003, People's Republic of China;
- Source:
Volume 9, Issue 1,
February 2015,
p.
37 – 47
DOI: 10.1049/iet-spr.2013.0187 , Print ISSN 1751-9675, Online ISSN 1751-9683
This study addresses the sensor scheduling problem of selecting and assigning sensors dynamically for multi-target tracking. The authors goal is to trade off the tracking accuracy and the interception risk in a period of time. The interception risk is incurred by the fact that the emission energy originating from a sensor can be intercepted by the target during the tracking mission. To react to sensor emission, the targets are able to switch between dynamic models. This non-myopic sensor scheduling problem is formulated as a partially observable Markov decision process, where the one-step reward is constructed by combining the tracking error with the interception probability and the information state is tracked by the interacting multiple model extended Kalman filtering. A novel sampling approach using the unscented transformation is proposed for long-term reward approximation. Numerical simulations illustrate the validity of the proposed scheduling scheme.
Inspec keywords: numerical analysis; nonlinear filters; Markov processes; sensors; target tracking; signal sampling; Kalman filters
Other keywords: sampling approach; interacting multiple model extended Kalman flltering; tracking mission; tracking accuracy; information state; numerical simulations; dynamic models; sensor emission; emission energy; tracking error; nonmyopic sensor scheduling; one-step reward; multiple reactive target tracking; unscented transformation; interception probability; long-term reward approximation; partially observable Markov decision process
Subjects: Sensing devices and transducers; Other numerical methods; Other numerical methods; Markov processes; Signal processing theory; Filtering methods in signal processing; Markov processes
References
-
-
1)
-
32. Haykin, S.: ‘Kalman filtering and neural networks’ (John Wiley and Sons Press, New York, 2001).
-
-
2)
-
12. Shogo, A., Yasushi, I., Koichi, H.: ‘Fast sensor scheduling for spatially distributed heterogeneous sensors’. Proc. American Control Conf., Saint Louis, MO, USA, June 2009, pp. 2785–2790.
-
-
3)
-
11. Shogo, A., Yasushi, I., Koichi, H.: ‘Fast sensor scheduling for spatially distributed sensors’, IEEE Trans. Autom. Control, 2011, 56, (8), pp. 1900–1905 (doi: 10.1109/TAC.2011.2141450).
-
-
4)
-
13. Kreucher, C., Hero, A.O., Kastella, K., Chang, D.: ‘Efficient methods of non-myopic sensor management for multitarget tracking’. Proc. 43rd IEEE Conf. on Decision and Control, Paradise Island, Bahamas, December 2004, pp. 722–727.
-
-
5)
-
9. Tian, K.S., Zhu, G.X.: ‘Sensor management based on fisher information gain’, J. Syst. Eng. Electron., 2006, 17, (3), pp. 31–534.
-
-
6)
-
4. Sunny, A., Kury, J.: ‘Distributed greedy scheduling for multihop wireless networks’. Proc. Seventh IEEE Int. Conf. Mobile Ad-hoc and Sensor Systems, San Francisco, CA, USA, November 2010, pp. 582–587.
-
-
7)
-
3. Oshman, Y.: ‘Optimal sensor selection strategy for discrete-time state estimators’, IEEE Trans. Aerosp. Electron. Syst., 1994, 30, (2), pp. 307–314 (doi: 10.1109/7.272256).
-
-
8)
-
34. Suman, C., Richard, S.E.: ‘Information space receding horizon control’. IEEE Symp. Adaptive Dynamic Programming and Reinforcement Learning, Paris, April 2011, pp. 302–309.
-
-
9)
-
14. Kreucher, C., Hero, A.O.: ‘Monte Carlo methods for sensor management in target tracking’. Proc. IEEE Workshop on Nonlinear Statistical Signal Processing, Cambridge, UK, September 2006, pp. 232–237.
-
-
10)
- A.G. Self , B.G. Smith . Intercept time and its prediction. IEE Proc. F , 215 - 222
-
11)
-
17. Aughenbaugh, J.M., LaCour, B.R.: ‘Sensor management for particle filter tracking’, IEEE Trans. Aerosp. Electron. Syst., 2011, 47, (1), pp. 503–523 (doi: 10.1109/TAES.2011.5705688).
-
-
12)
-
27. Brooks, A., Makarenko, A., Williams, S., Durrant-Whyte, H.: ‘Parametric POMDPs for planning in continuous state spaces’, Robot. Auton. Syst., 2006, 54, (2006), pp. 887–897 (doi: 10.1016/j.robot.2006.05.007).
-
-
13)
-
30. Secomandi, N.: ‘A rollout policy for the vehicle routing problem with stochastic demands’, Operations Research, 2001, 49, pp. 796–802 (doi: 10.1287/opre.49.5.796.10608).
-
-
14)
-
15. He, Y., Chong, E.K.P.: ‘Sensor scheduling for target tracking: a Monte Carlo sampling approach’, Digital Signal Process., 2005, 16, (2006), pp. 533–545.
-
-
15)
-
25. Rafael, T.M., Miguel, A.Z., Antonio, F.G.: ‘IMM-EKF based road vehicle navigation with low cost GPS/INS’. Proc. Int. Conf. Multisensor Fusion and Integration for Intelligent Systems, Heidelberg, Germany, September 2006, pp. 433–438.
-
-
16)
-
2. Logothetis, A., Isaksson, A.: ‘On sensor scheduling via information theoretic criteria’. Proc. American Control Conf., San Diego, CA, USA, June 1999, pp. 2402–2406.
-
-
17)
-
19. Shogo, A., Yasushi, I., Koichi, H.: ‘Fast sensor scheduling with communication costs for sensor networks’. Proc. American Control Conf., Baltimore, MD, USA, June 2010, pp. 295–300.
-
-
18)
-
21. Hero, A.O., Castan, D.A., Cochran, D., Kastella, K.: ‘Foundations and applications of sensor management’ (Springer Press, 2008).
-
-
19)
- X.R. Li , V.P. Jilkov . Survey of maneuvering target tracking. Part I: Dynamic models. IEEE Trans. Aerosp. Electron. Syst. , 4 , 1333 - 1364
-
20)
-
17. Li, Y., Krakow, L.W., Chong, E.K.P., Groom, K.N.: ‘Approximate stochastic dynamic programming for sensor scheduling to track multiple targets’, Digit. Signal Process., 2007, 19, (2009), pp. 978–989.
-
-
21)
-
7. Hernandez, M.L., Kirubara, T., Bar-Shalom, Y.: ‘Efficient multi-sensor resource management using Cramér–Rao lower bounds’. Proc. SPIE Signal and Data Processing of Small Targets, 2002, vol. 4728, pp. 394–409.
-
-
22)
-
18. Li, Y., Krakow, L.W., Chong, E.K.P., Groom, K.N.: ‘Dynamic sensor management for multisensor multitarget tracking’. Proc. Annual Conf. Information Sciences and Systems, Princeton, NJ, USA, March 2006, pp. 1397–1402.
-
-
23)
-
10. Shogo, A., Yasushi, I., Koichi, H.: ‘Fast sensor scheduling for networked sensor systems’. Proc. 47th IEEE Conf. Decision and Control, Cancun, Mexico, December 2008, pp. 459–463.
-
-
24)
-
16. He, Y., Chong, E.K.P.: ‘Sensor scheduling for target tracking in sensor networks’. Proc. 43rd IEEE Conf. Decision and Control, Paradise Island, Bahamas, December 2004, pp. 743–748.
-
-
25)
-
29. Bertsekas, D.P., Castanon, D.A.: ‘Rollout algorithms for stochastic scheduling problems’, J. Heuristics, 1998, 5, (1999), pp. 89–108.
-
-
26)
-
6. Wang, H., Pottie, G., Yao, K., Estrin, D.: ‘Entropy-based sensor selection heuristic for target localization’. Proc. Third Int. Symp. Information Processing in Sensor Networks, Berkeley, CA, USA, April 2004, pp. 36–45.
-
-
27)
-
22. Stein, S., Johansen, D.: ‘A statistical description of coincidences among random pulse trains’, Proc. IRE, 1985, 46, (5), pp. 827–830 (doi: 10.1109/JRPROC.1958.286935).
-
-
28)
-
26. Mahendra, M., Barbara, F.L.S.: ‘IMM estimator for ground target tracking with variable measurement sampling intervals’. Proc. Int. Conf. Information Fusion, Florence, Italy, July 2006, pp. 1–8.
-
-
29)
- S.J. Julier , J.K. Uhlmann . Unscented filtering and nonlinear estimation. Proc. IEEE , 3 , 401 - 422
-
30)
-
31. Ebcin, S., Veth, M.: ‘Tightly-coupled image-aided inertial navigation using the unscented Kalman filter’. 20th Int. Technical Meeting of the Satellite Division of the Institute of Navigation, September 2007, pp. 1851–1860.
-
-
31)
- E. Mazor , A. Averbuch , Y. Bar-Shalom , J. Dayan . Interacting multiple model methods in target tracking: a survey. IEEE Trans. Aerosp. Electron. Syst. , 1 , 103 - 123
-
32)
-
20. Zhang, S., Xiao, W.D., Marcelo, H.A.J., Chen, K.T.: ‘IMM filter based sensor scheduling for maneuvering target tracking in wireless sensor networks’. Proc. Int. Conf. Intelligent Sensors, Sensor Networks and Information, Melbourne, Qld, December 2007, pp. 287–292.
-
-
33)
-
8. Kalandros, M., Pao, L.Y.: ‘Covariance control for multisensor systems’, IEEE Trans. Aerosp. Electron. Syst., 2004, 38, (4), pp. 1138–1157 (doi: 10.1109/TAES.2002.1145739).
-
-
34)
-
1. Kreucher, C., Kastella, K., Hero, A.O.: ‘A Bayesian method for integrated multitarget tracking and sensor management’. Proc. Int. Conf. Information Fusion, Piscataway, NJ, USA, July 2003, pp. 132–139.
-
-
35)
-
3. Dissanayake, G., Newman, P., Clark, S., et al: ‘A solution to the simultaneous localization and map building (SLAM) problem’, IEEE Trans. Robot. Autom., 2001, 17, (3), pp. 229–241 (doi: 10.1109/70.938381).
-
-
1)