Abstract
Normally in tracking applications, the target motion is usually modeled in Cartesian coordinates but, most sensors measure target parameters in polar coordinates. In this paper two contributions are considered in target tracking. One depends on position measurements and another one is on Doppler measurements. The position measurements are measured by taking the range and bearing (angle) of the target depending on the sensor location. Tracking the target Cartesian coordinates by using this range and bearing measurements is a nonlinear state estimation problem. To calculate the position measurements (range and angle), it is preferred to convert them to Cartesian coordinates by considering the linear form values. This is done, to avoid using nonlinear filters. This method is called as converted position measurement Kalman filter (CPMKF). In this paper another contribution is Doppler (range rate) measurement in target tracking systems. In this contribution the nonlinear pseudo states are calculated. This method is called as Converted Doppler measurement Kalman filter (CDMKF). By considering these two methods a parallel filtering structure, called statically fused converted measurement Kalman filter (SF-CMKF) is proposed. The two methods are operated along with each other to construct the new state estimator SF-CMKF by a static estimator to obtain final state estimates.
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References
Y. Bar-shalom, X.R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and software (Wiley, New York, 2001)
Y. Bar-Shalom, P.K. Willett, X. Tian, Tacking and Data Fusion: A Handbook of Algorithms (YBS Publishing, Storrs, CT, 2011)
D.F. Bizup, D.R. Brown, The over-extended Kalman filter—Don’t use it, in Proceedings of the 6th International Conference on Information Fusion, Cairns, Queensland, Australia, July
S.V. Bordonaro, P. Willet, Y. Bar-Shalom, Tracking with converted position and Doppler measurements, in Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, 2011, pp. 81370D-1-4
S.V. Bordonaro, P. Willet, Y. Bar-Shalom, Unbiased tracking with converted measurements, in Proceedings of 2012 IEEE Radar Conference, pp. 741–745
Y.P. Dai, et al., A target tracking algorithm with range rate under the color measurement environment, Proceedings of the 38th SICE Annual Conference, Morioka, Japan, July 1999, pp. 1145–1148
Z. Duan, C. Han, X.R. Li, Sequential nonlinear tracking filter considered with range-rate measurements in spherical coordinates, in Proceedings of the 7th International Conference held on International Conference on Information Fusion, 2004, pp. 599–605
Z. Duan, C. Han, Radar target tracking with range rate measurements in polar coordinates. J. Syst. Simul. 16(12), 2860–2863 (2004). (in Chinese)
Z. Duan, C. Han, X. Li, R Comments on unbiased converted position and Doppler measurements for tracking. IEEE Trans. Aerosp. Electron. Syst. 40(4), 1374–1377 (2004)
Z. Duan, et al., Sequential unscented Kalman filter for radar target tracking with range rate measurements, in Proceedings of the 8th International Conference held on Information Fusion, 2005, pp. 130–137
S.J. Julier, J.K. Uhlmann, A consistent, debiased method for converting between polar and Cartesian coordinate systems, in Proceedings of the 1997 SPIE Conference on Acquisition, Tracking, and Pointing XI, vol. 3086
D. Lerro, Y. Bar-shalon, Unbiased kalman filter using converted measurements versus EKF. IEEE Trans. Aerosp. Electron. Syst. 29(3), 1015–1022 (1993)
W. Mei, Y. Bar-Shalom, Unbiased Kalman filter using converted measurements: revisit, in Proceedings taken from SPIE Conference held on Signal and Data Processing in Small Targets, 2009, pp. 7445–38
G. Zhou et al., Statically fused Converted Position and Doppler measurement Kalman filters. IEEE Trans. Aerosp. Electron. Syst. 50(1), 300–318 (2014)
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Teeparti, S.P., Kota, C.B.R., Putrevu, V.K.C., Sanagapallea, K.R. (2016). Advanced Parallel Structure Kalman Filter for Radar Applications. In: Satapathy, S., Rao, N., Kumar, S., Raj, C., Rao, V., Sarma, G. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 372. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2728-1_21
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DOI: https://doi.org/10.1007/978-81-322-2728-1_21
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