Abstract
A transmission censoring and information fusion approach is proposed for distributed nonlinear system state estimation in Dynamic Data Driven Applications Systems (DDDAS). In this approach, to conserve communication resources, based on the Jeffreys divergence between the prior and posterior probability density functions (PDFs) of the system state, only local posterior PDFs that are sufficiently different from their corresponding prior PDFs will be transmitted to a fusion center. To further reduce the communication cost, the local posterior PDFs are approximated by Gaussian mixtures, whose parameters are learned by an expectation-maximization algorithm. At the fusion center, the received PDFs will be fused via a generalized covariance intersection algorithm to obtain a global PDF. Numerical results for a multi-senor radar target tracking example are provided to demonstrate the effectiveness of the proposed censoring approach.
This work was supported in part by the AFOSR Dynamic Data and Information Processing Portfolio under Grant FA9550-22-1-0038.
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References
Ahmed, N.R.: Decentralized Gaussian mixture fusion through unified quotient approximations. ArXiv:1907.04008 (2019)
Appadwedula, S., Veeravalli, V.V., Jones, D.L.: Decentralized detection with censoring sensors. IEEE Trans. Signal Process 56(4), 1362–1373 (2008)
Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley, New York (2001)
Blasch, E., et al.: DDDAS-based joint nonlinear manifold learning for target localization. In: Proceedings of International Workshop on Structural Health Monitoring Conference (September 2017)
Chong, C.Y., Mori, S., Chang, K.C.: Distributed multitarget multisensor tracking. In: Bar-Shalom, Y. (ed.) Multitarget-Multisensor Tracking: Advanced Applications, pp. 247–295. Artech House (1990)
Conte, A., Niu, R.: Censoring in distributed radar tracking systems with various feedback models. In: 2015 18th International Conference on Information Fusion (Fusion), pp. 476–483 (2015)
Conte, A.S., Niu, R.: Censoring distributed nonlinear state estimates in radar networks. In: Pham, K.D., Chen, G. (eds.) Sensors and Systems for Space Applications IX, vol. 9838, pp. 127–142. International Society for Optics and Photonics, SPIE (2016)
Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds.): Handbook of Dynamic Data Driven Applications Systems: Volume 1. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-74568-4
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM Algorithm. J. Royal Stat. Society: Series B (Methodological) 39(1), 1–22 (1977). https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
Govaers, F., Koch, W.: An Exact solution to track-to-track-fusion at arbitrary communication rates. IEEE Trans. Aerosp. Electron. Syst. 48(3), 2718–2729 (2012). https://doi.org/10.1109/TAES.2012.6237623
Julier, S.: An empirical study into the use of Chernoff information for robust, distributed fusion of Gaussian mixture models. In: 2006 9th International Conference on Information Fusion (Fusion) (July 2006)
Julier, S., Uhlmann, J.: A non-divergent estimation algorithm in the presence of unknown correlations. In: Proceedings of the American Control Conference, pp. 2369–2373 (1997)
Msechu, E.J., Giannakis, G.B.: Sensor-centric data reduction for estimation with WSNs via censoring and quantization. IEEE Trans. Signal Process. 60(1), 400–414 (2012)
Ong, L., Bailey, T., Durrant-Whyte, H., Upcroft, B.: Decentralised particle filtering for multiple target tracking in wireless sensor networks. In: Proceedings of 2008 11th International Conference on Information Fusion (June 2008)
Zheng, Y., Niu, R., Varshney, P.K.: Sequential Bayesian estimation with censored data for multi-sensor systems. IEEE Trans. Signal Process. 62(10), 2626–2641 (2014)
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Niu, R. (2024). Transmission Censoring and Information Fusion for Communication-Efficient Distributed Nonlinear Filtering. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_24
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