State estimation in range coordinate using range-only measurements

doi:10.23919/JSEE.2022.000050

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 497-510.doi: 10.23919/JSEE.2022.000050

• ELECTRONICS TECHNOLOGY • Next Articles

State estimation in range coordinate using range-only measurements

Keyi LI(), Zhengkun GUO(), Gongjian ZHOU*()

¹ School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Received:2021-03-06 Accepted:2022-01-14 Online:2022-06-18 Published:2022-06-24
Contact: Gongjian ZHOU E-mail:likeyi@hit.edu.cn;guozkbetter@163.com;zhougj@hit.edu.cn
About author:|LI Keyi was born in 1991. He received his B.E. degree in communication engineering and M.E. and Ph.D. degrees in information and communication engineering from Harbin Institute of Technology, Harbin, China, in 2014, 2016, and 2020, respectively. He is currently a lecturer in the School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China. From November 2020, he held a Postdoctoral Fellowship in the Department of Aerospace Engineering, Harbin Institute of Technology. From September 2018 to September 2019, supported by China Scholarship Council, he was a visiting Ph.D. student in McMaster University, Hamilton, ON, Canada. His research interests include estimation, target tracking and information fusion. E-mail: likeyi@hit.edu.cn||GUO Zhengkun was born in 1985. He received his B.E. degree from the School of Electronic Information, Wuhan University, Wuhan, China, in 2008, his M.E. degree from Shanghai Academy of Spaceflight Technology, Shanghai, China, in 2011 and his Ph.D. degree from the School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China. He is currently working as an assistant engineer in Shanghai Academy of Spaceflight Technology. From March 2016 to March 2017, supported by China Scholarship Council, he was a visiting Ph.D. student in McMaster University, Hamilton, ON, Canada. His research interests include signal processing, estimation, tracking and information fusion. E-mail: guozkbetter@163.com||ZHOU Gongjian was born in 1979. He received his B.E., M.E., and Ph.D. degrees in information and communication engineering from Harbin Institute of Technology, Harbin, China, in 2000, 2002, and 2008, respectively. From February 2009 to March 2011, he held a Postdoctoral Fellowship in the Department of Aerospace Engineering, Harbin Institute of Technology. From April 2011 to May 2012, he was a visiting professor with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada. He is currently a professor with the Department of Electronic Engineering, Harbin Institute of Technology. He is also a Longjiang Young Scholar. His research interests include estimation, tracking, detection, information fusion and signal processing. E-mail: zhougj@hit.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61671181; 62101162)

Abstract

Abstract:

In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.

Key words: range-only measurement, state estimation, filter initialization, target tracking, unscented Kalman filter (UKF)

Keyi LI, Zhengkun GUO, Gongjian ZHOU. State estimation in range coordinate using range-only measurements[J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 497-510.

Figures/Tables 12

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References 39

1	BRANKO R, SANJEEV A, JAMES M Target motion analysis using range-only measurements: algorithms, performance and application to ISAR data. Signal Processing, 2002, 82 (2): 273- 296. doi: 10.1016/S0165-1684(01)00187-6
2	ZHOU G J, ZHOU J F, FU T J, et al Multisensor-multitarget tracking based on hierarchical association only using range-Doppler measurements. Proc. of the IET International Radar Conference, 2013, 327- 340.
3	TOBIAS M, LANTERMAN A D A probability hypothesis density-based multitarget tracker using multiple bistatic range and velocity measurements. Proc. of the 36th Southeastern Symposium on System Theory, 2004, 205- 209.
4	JAUFFRET C, PEREZ A, PILLON D. Observability: range-only versus bearings-only target motion analysis when the observer maneuvers smoothly. IEEE Trans. on Aerospace and Electronic Systems, 2017, 53(6): 2814–2832.
5	PILLON D, PEREZ-PIGNOL A, JAUFFRET C Observability: range-only vs. bearings-only target motion analysis for a leg-by-leg observers trajectory. IEEE Trans. on Aerospace and Electronic Systems, 2016, 52 (4): 1667- 1678. doi: 10.1109/TAES.2016.150016
6	SONG T L. Observability of target tracking with range-only measurements. IEEE Journal of Oceanic Engineering, 1999, 24(3): 383–387.
7	COLEMAN D, BOPARDIKAR S D, TAN X, Observability-aware target tracking with range only measurement. Proc. of the American Control Conference, 2021: 1–8.
8	CLARK J M, KOUNTOURIOTIS P A, VINTER R B. A Gaussian mixture filter for range-only tracking. IEEE Trans. on Automatic Control, 2011, 56(3): 602–613.
9	MANOLAKIS D E. Efficient solution and performance analysis of 3-D position estimation by trilateration. IEEE Trans. on Aerospace and Electronic Systems, 1996, 32(4): 1239–1248.
10	DEMING R, SCHINDLER J, PERLOVSKY L. Multi-target/multi-sensor tracking using only range and Doppler measurements. IEEE Trans. on Aerospace and Electronic Systems, 2009, 45(2): 593–611.
11	MASHHADANI W A, DANOON L, BROWN A. Modeling range-only multistatic radar target detection with interval analysis in 3D. Proc. of the European Radar Conference, 2016: 173–176.
12	PETSIOS M N, ALIVIZATOS E G, UZUNOGLU N K. Manoeuvring target tracking using multiple bistatic range and range-rate measurements. Signal Processing, 2007, 87(4): 665–686.
13	YANG X S, ZHANG W A, LIU A D, et al. Linear fusion estimation for range-only target tracking with nonlinear transformation. IEEE Trans. on Industrial Informatics, 2020, 16(10): 6403–6412.
14	ZHAO Y S, HU D X, ZHAO Y J, et al. Moving target localization for multistatic passive radar using delay, Doppler and Doppler rate measurements. Journal of Systems Engineering and Electronics, 2020, 31(5): 939–949.
15	PACHOLSKA M, DUMBGEN F, SCHOLEFIELD A. Relax and recover: guaranteed range-only continuous localization. IEEE Robotics and Automation Letter, 2020, 5(2): 2248–2255.
16	SADEGHI M, BEHNIA F, AMIRI R. Optimal sensor placement for 2-D range-only target localization in constrained sensor geometry. IEEE Trans. on Signal Processing, 2020, 68: 2316–2327.
17	DASH D, JAYARAMAN V. A probabilistic model for sensor fusion using range-only measurements in multistatic radar. IEEE Sensors Letter, 2020, 4(6): 7500604.
18	MA H, ANTONIOU M, STOVE A G, et al. Target kinematic state estimation with passive multistatic radar. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57(4): 2121–2134.
19	BARRICK D. History, present status, and future directions of HF surface-wave radars in the U S. Proc. of the International Conference on Radar, 2003, 652- 655.
20	GRIFFITHS H. Multistatic, MIMO and networked radar: the future of radar sensors Proc. of the 7th European Radar Conference, 2010, 81- 84.
21	ZHOU G J, GUO Z K, LI K Y, et al. Motion modeling and state estimation in range-Doppler plane. Aerospace Science and Technology, 2021, 115: 106792.
22	LI K Y, GUO Z K, ZHOU G J. Nearly constant acceleration model for state estimation in the range-Doppler plane. IET Radar, Sonar & Navigation, 2021, 15(12): 1687–1701.
23	JULIER S J, UHLMANN J K, DURRANT-WHYTE H F. A new method for nonlinear transformation of means and covariances in filters and estimates. IEEE Trans. on Automatic Control, 2000, 45(3): 477–482.
24	JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3): 401–422.
25	WAN E A, VAN D M R. The unscented Kalman filter for nonlinear estimation Proc. of IEEE Symposium on Adaptive Systems for Signal Processing, Communication and Control, 2000, 153- 158.
26	LIU C Y, SHUI P L, WEI G, et al Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking. Journal of Systems Engineering and Electronics, 2014, 25 (3): 380- 385. doi: 10.1109/JSEE.2014.00043
27	DENG F, CHEN J, CHEN C Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects. Journal of Systems Engineering and Electronics, 2013, 24 (4): 655- 665. doi: 10.1109/JSEE.2013.00076
28	GUO Z K, ZHOU G J. State estimation from range-only measurements Proc. of the 23rd International Conference on Information Fusion, 2020, 367- 372.
29	ZHOU G J, PELLETIER M G, KIRUBARAJAN T, et al. Statically fused converted position and Doppler measurement Kalman filters. IEEE Trans. on Aerospace and Electronic Systems, 2014, 50(1): 300–318.
30	ZHOU G J, WU L G, XIE J H, et al. Constant turn model for statically fused converted measurement Kalman filters. Signal Processing, 2015, 108: 400–411.
31	ARASARATNAM I, HAYKIN S. Cubature Kalman filter. IEEE Trans. on Automatic Control, 2009, 54(6): 1254–1269.
32	SHI J, QI G Q, LI Y Y, et al. Stochastic convergence analysis of cubature Kalman filter with intermittent observations. Journal of Systems Engineering and Electronics, 2018, 29(4): 823–833.
33	ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 2002, 50(2): 174–188.
34	DAN C, DOUCET A. A survey of convergence results on particle filtering methods for practitioners. IEEE Trans. on Signal Processing, 2002, 50(3): 736–746.
35	ZHANG Y, WANG S F, LI J C. Improved particle filtering techniques based on generalized interactive genetic algorithm. Journal of Systems Engineering and Electronics, 2016, 27(1): 242–250.
36	ZUO J Y, ZHONG X P. Particle filter for nonlinear systems with multi-sensor asynchronous random delays. Journal of Systems Engineering and Electronics, 2017, 28(6): 1064–1071.
37	TICHAVSKY P, MURAVCHIK C H, NEHORAI A. Posterior Cramer-Rao bounds for discrete-time nonlinear filter. IEEE Trans. on Signal Processing, 1998, 46(5): 1386–1396.
38	LI X R, JILKOV V P. Survey of maneuvering target tracking Part I: dynamic models. IEEE Trans. on Aerospace and Electronic Systems, 2003, 39 (4): 1333- 1364. doi: 10.1109/TAES.2003.1261132
39	BAR-SHALOM Y, LI X R, KIRUBARAJAN T. Estimation with applications to tracking and navigation: theory, algorithms, and software. New York: Wiley, 2001.

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State estimation in range coordinate using range-only measurements

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