Abstract
For modern tracking systems, the tracking target generally has the characteristics of high speed and mobility. Tracking targets has always been a challenging problem, especially tracking high speed and strong maneuvering targets, which is difficult in theory and practice. An interactive multi-model (IMM) based on iterative unbiased conversion measurement Kalman filter (IUCMKF) is proposed. The new algorithm takes advantages of the interactive and complementary characteristics between different models to overcome the problems of low precision and filter divergence. First, investigate the function of conversion measurement Kalman filter (CMKF), debiased conversion measurement Kalman filter (DCMKF), and IUCMKF on double-model and multiple-model. Secondly, compare and analyze the performance of the three algorithms (CMKF-IMM, DCMKF-IMM and IUCMKF-IMM). Finally, identify the effect and impact of the combination of the four different models including CA, Singer, CS, and Jerk on the accuracy of target tracking. The results of numerical simulation show the choice of the number and type of models should be weighed according to the actual simulation environment. Even though more choices of models can improve the tracking accuracy of the target, but that also greatly increases the complexity of the algorithm and the error consistency of the algorithm also cannot be guaranteed to some extent. Therefore, compared with CMKF-IMM and DCMKF-IMM, the new algorithm can attain a more accurate state of the target being tracked and the covariance estimation. It also has more potential in improving tracking accuracy.
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References
Han, B., Xin, J., Xin, J., et al.: A study on maneuvering obstacle motion state estimation for intelligent vehicle using adaptive Kalman filter based on current statistical model. Math. Prob. Eng. 2015(4), 1–14 (2015)
Siyang, L., et al.: Spreading code design for downlink space-time-frequency spreading CDMA. IEEE Trans. Veh. Technol. 57(5), 2933–2946 (2008)
Yankai, C., Xiaogeng, L., Zhigang, W., et al.: Adaptive interacting multiple model algorithm for high maneuvering target tracking. Comput. Eng. Appl. 50(8), 198–201 (2014)
You, K., Xie, L.: Kalman filtering with scheduled measurements. IEEE Trans. Signal Process. 61(6), 1520–1530 (2013)
Wang, M., Wang, H.: An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration. Chin. J. Aeronaut. 30(1), 426–433 (2017)
Niedfeldt, P., Beard, R.: Convergence and complexity analysis of recursive-RANSAC: a new multiple target tracking algorithm jerk model for tracking highly maneuvering targets. IEEE Trans. Autom. Control 61(2), 456–461 (2016)
Shunyi, Z., et al.: Fusion Kalman/UFIR filter for state estimation with uncertain parameters and noise statistics. IEEE Trans. Ind. Electron. 64(4), 3075–3083 (2017)
Lan, J., Li, X.R.: Multiple conversions of measurements for nonlinear estimation. IEEE Trans. Signal Process. 65(18), 4956–4970 (2017)
Barrau, A., Bonnabel, S.: The invariant extended Kalman filter as a stable observer. IEEE Trans. Autom. Control 62(4), 1797–1812 (2017)
Sustika, Rika, Suryana, J.: Nonlinear-filtering with interacting multiple-model algorithm for coastal radar target tracking system. Appl. Mech. Mater. 13(1), 211–220 (2015)
Cunhu, J., et al.: DCMKF-IMM for maneuvering target tracking algorithm realized with hardware. Electro-Opt. Control 20(4), 51–55 (2013)
Yunhe, C., et al.: Tracking methods of high speed strong maneuvering targets in near space. In: International Conference on Signal Processing, pp. 1885–1889 (2015)
Da, L., Xiangyu, Z., Ruifang, L.: Iterative unbiased converted measurement Kalman filter for target tracking. In: 10th International Symposium on Computational Intelligence and Design, ISCID 2017, vol. 1, pp. 342–345. IEEE Computer Society, Hangzhou, China (2017)
Lerro, D., Bar-Shalom, Y.: Tracking with Consistent Converted Measurements Versus EKF. IEEE Trans. Aerosp. Electron. Syst. 29(3), 1015–1022 (1993)
Bar-Shalom, Y., Xiaoquan, S., et al.: Unbiased converted measurements for tracking. IEEE Trans. Aerosp. Electron. Syst. 34(3), 1023–1028 (1998)
Bar-Shalom, Y., Kirubarajan, T.: Estimation with application to tracking and navigation. Eaepe 39(5), 25–29 (2001)
Bell, B., et al.: The iterated Kalman filter update as a Gauss-Newton method. IEEE Trans. Autom. Control 38(2), 294–297 (1993)
Guo, F., Sun, Z., Huangfu, K.: A modified covariance extended Kalman filtering algorithm in passive location. In: Proceedings of the IEEE International Conference on Robotics, Intelligent System and Signal Processing, pp. 307–311 (2013)
Johnston, L., Krishnamurthy, V.: Derivation of a sawtooth iterated extended Kalman smoother via the AECM algorithm. IEEE Trans. Signal Proces. 49(9), 1899–1909 (2001)
Wu, P., Li, X.: Iterated square root unscented Kalman filter for maneuvering target tracking using TDOA measurements. Int. J. Control Autom. Syst. 11(4), 761–767 (2013)
Liang, X., et al.: Analysis of performance of jerk models in tracking high speed maneuvering vehicles. J. Spacecr. TT C Technol. 32(3), 262–267 (2013)
Xiaobing, L., et al.: A α-jerk model for tracking maneuvering targets. Signal Process. 23(4), 481–484 (2007)
Acknowledgment
This work is partly funded by the National Natural Science Foundation of China (Grant No. 51475347).
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Li, D., Zou, X., Lou, P., Li, R., Wei, Q. (2018). Iterative Unbiased Conversion Measurement Kalman Filter with Interactive Multi-model Algorithm for Target Tracking. In: Li, L., Hasegawa, K., Tanaka, S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2018. Communications in Computer and Information Science, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-13-2853-4_30
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DOI: https://doi.org/10.1007/978-981-13-2853-4_30
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