Abstract
In a multiple sensor tracking scenario, measurements originated from the target of interest are necessarily aligned and fused to provide accurate information about the target. The process known as registration and fusion is generally casted as a joint parameter and state estimation problem for a single target case. The standard solution to this problem is the augmented Kalman filter (AKF) which takes the parameters as variables in the state vector. Despite of its easy implementation, the AKF is not favorable for large number of unknown parameters, as is the case for multiple sensors. Moreover, it is prone to numerical inaccuracy or divergence in application. In this paper, we evaluate the divergence problem of the AKF in simultaneous registration and fusion for the Radar/ESM sensors. Furthermore, we propose an expectation-maximization (EM) method in the maximum likelihood estimation (MLE) framework. In particular, to account for the maneuverability of the target, the interacting multiple model (IMM) filter implemented either by an extended Kalman filter (EKF) or by an unscented Kalman filter (UKF) is embedded into the conditional expectation evaluation in the E-step. The proposed joint registration and fusion method is thus called EM-IMM. Analysis shows that the EM method is convergent and furthermore leads to asymptotically unbiased estimate in an approximation sense. To evaluate the estimation performance, a direct inverse computation algorithm of Fisher information matrix (FIM) in posterior Cramer-Rao bound (PCRB) is also developed. Simulation results are given to demonstrate the effectiveness of the proposed method.
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References
Bar-Shalom, Y.: Multiarget-Multisensor Tracking: Advanced Applications. Artech House, Norwood (1990)
Makarenko, A.A., Kaupp, T., Durrant-Whyte, H.F.: Scalable human-robot interactions in active sensor networks. IEEE Pervasive Computing 2(4), 63–71 (2001)
Blom, H.A.P., Hogendoorn, R.A., Doorn, B.A.V.: Design of a multisensor tracking system for advanced air traffic control. In: Bar-Shalom, Y. (ed.) Multitarget-Multisensor Tracking: Applications and Advances, vol. II, pp. 31–63. Artech House (1992)
Blackman, S., Pop, R.: Design and Analysis of Modern Tracking Systems. Artech House, Norwood (1999)
Bar-Shalom, Y., Li, X.R.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS, Storrs (1995)
Dana, M.P.: Registration: a prerequisite for multiple sensor tracking. In: Bar-Shalom, Y. (ed.) Multiarget-Multisensor Tracking: Advanced Applications, Artech House, Norwood (1990)
Leung, H., Blanchette, M., Harrison, C.: A least squares fusion of multiple radar data. In: Proc. of RADAR, pp. 364–369 (1994)
Zhou, Y., Leung, H., Blanchette, M.: Sensor alignment with Earth-centered Earth-fixed (ECEF) coordinate system. IEEE Trans. Aerospace and Electronic Systems 35(2), 410–418 (1999)
Zhou, Y., Leung, H., Yip, P.C.: An exact maximum likelihood registration algorithm for data fusion. IEEE Trans. Signal Processing 45(6), 1560–1572 (1997)
Okello, N., Ristic, B.: Maximum likelihood registration for multiple dissimilar sensors. IEEE Trans. Aerospace and Electronic Systems 39(3), 1074–1083 (2003)
Cruz, E.D., Alouani, A., Rice, T., Blair, W.: Sensor registration in multisensor systems. In: Proceedings of SPIE, vol. 1698, pp. 382–393 (1992)
Zhou, Y., Leung, H., Bosse, E.: Registration of mobile sensors using the parallelized extended Kalman filter. Optical Engineering 36(3), 780–788 (1997)
Nabaa, N., Bishop, R.H.: Solution to a multisensor tracking problem with sensor registration errors. IEEE Trans. Aerospace and Electronic Systems 35(1), 354–363 (1999)
Tenney, R., Hebbert, R., Sandell, N.J.: A tracking filter for maneuvering sources. IEEE Trans. Automatic Control 22(2), 246–251 (1977)
Aidala, V.J.: Kalman filter behavior in bearings-only tracking applications. IEEE Trans. Aerospace and Electronic Systems 15(1), 29–39 (1979)
Ljung, L.: Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Trans. Automatic Control 24(1), 36–50 (1979)
Wan, E.A., van der Merwe, R.: The unscented Kalman filter. In: Haykin, S. (ed.) Kalman filter and Neural networks, ch. 7. Wiley Publishing, Chichester (2001)
Li, W., Leung, H., Zhou, Y.: Space-time registration of radar and ESM using unscented Kalman filter. IEEE Trans. Aerospace and Electronic Systems 40(3), 824–836 (2004)
Friedland, B.: Treatment of bias in recursive filtering. IEEE Trans. Automatic Control 14(4), 359–367 (1969)
Zhou, Y., Mickeal, J., Ford, B.: A neural network based approach for ESM/Radar track association. In: Proc. of the 7th Int. Conf. on Information Fusion, vol. 3, pp. 1238–1244 (2004)
Doorn, B.A.V., Blom, H.A.P.: Systematic error estimation in multisensor fusion systems. In: Proc. SPIE Symp. Signal and Data Processing of Small Targets, vol. 1954, pp. 450–461 (1993)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statiscal Soc., Ser. B 39(1), 1–38 (1977)
Logothetis, A., Krishnamurthy, V., Holst, J.: A Bayesian EM algorithm for optimal tracking of a maneuvering target in clutter. Signal Processing 82, 473–490 (2002)
Molnar, K.J., Modestino, J.W.: Application of the EM algorithm for the multitarget/multisensor tracking problem. IEEE Trans. Signal Processing 46(1), 115–129 (1998)
Pulford, G.W., Scala, B.F.L.: MAP estimation of target manoeuvre sequence with the expectation-maximization algorithm. IEEE Trans. Aerospace and Electronic Systems 38(2), 367–377 (2002)
Krishnamurthy, V., Dey, S.: Reduced spatio-temporal complexity MMPP and image-based tracking filters for maneuvering targets. IEEE Trans. Aerospace and Electronic Systems 39(4), 1277–1291 (2002)
Zia, A., Kirubarajan, T., Reilly, J.P., Yee, D., Punithakumar, K., Shirani, S.: An EM algorithm for nonlinear state estimation with model uncertainties. IEEE Trans. Signal Processing 56(3), 921–936 (2008)
Huang, D., Leung, H.: An expectation-maximization based interacting multiple model approach for cooperative driving systems. IEEE Trans. Intelligent Transportation Systems 6, 206–228 (2005)
Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley, New York (2001)
Helmick, R.E., Blair, W.D., Hoffman, S.A.: Fixed-interval smoothing for Markovian switching systems. IEEE Trans. Information Theory 41(6), 1845–1855 (1995)
Huang, D., Fujiyama, N., Sugimoto, S.: Blind image identification and restoration for noisy blurred images based on discrete sine transform. IEICE Trans. Information and Systems E86-D, 727–735 (2003)
Tichavsky, P., Muravchik, C.H., Nehorai, A.: Posterior Cramér-Rao bounds for discrete-time nonlinear filtering. IEEE Trans. Signal Processing 46(5), 1386–1396 (1998)
Bessell, A., Ristic, B., Farina, A., Wang, X., Arulampalam, M.S.: Error performance bounds for tracking a manoeuvring target. In: Proc. of the 6th Conf. Information Fusion, vol. 2, pp. 903–910 (2003)
Farina, A., Ristic, B., Timmoneri, L.: Cramér-Rao bound for nonlinear filtering with P d  < 1 and its application to target tracking. IEEE Trans. Signal Processing 50(8), 1916–1924 (2002)
Ristic, B., Arulampalam, M.S.: Tracking a manoeuvring target using angle-only measurements; algorithms and performance. Signal Processing 83, 1223–1238 (2003)
Ristic, B., Farina, A., Hernandez, M.: Cramér-Rao lower bound for tracking multiple targets. IEE Proc.-Radar Sonar Naving 151(3), 129–134 (2004)
Hernandez, M., Ristic, B., Farina, A., Sathyan, T., Kirubarajan, T.: Performance measure for Markovian switching systems using best-fitting Gaussian distributions. IEEE Trans. Aerospace and Electronic Systems 44(2), 724–747 (2008)
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Huang, D., Leung, H. (2010). An EM-IMM Method for Simultaneous Registration and Fusion of Multiple Radars and ESM Sensors. In: Mukhopadhyay, S.C., Leung, H. (eds) Advances in Wireless Sensors and Sensor Networks. Lecture Notes in Electrical Engineering, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12707-6_5
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DOI: https://doi.org/10.1007/978-3-642-12707-6_5
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