Abstract
In traditional target tracking methods, the angle error and range error are often measured by the empirical value, while observation noise is a constant. In this paper, the angle error and range error are analyzed. They are influenced by the signal-to-noise ratio (SNR). Therefore, a model related to SNR has been established, in which the SNR information is applied for target tracking. Combined with an advanced nonlinear filter method, the extended Kalman filter method based on the SNR model (SNR-EKF) and the unscented Kalman filter method based on the SNR model (SNR-UKF) are proposed. There is little difference between the SNR-EKF and SNR-UKF methods in position precision, but the SNR-EKF method has advantages in computation time and the SNR-UKF method has advantages in velocity precision. Simulation results show that target tracking methods based on the SNR model can greatly improve the tracking performance compared with traditional tracking methods. The target tracking accuracy and convergence speed of the proposed methods have significant improvements.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alberhseim WJ, 1981. A closed-form approximation to Robertson’s detection characteristics. Proc IEEE, 69(7):839. https://doi.org/10.1109/PROC.198L12082
Barczyk M, Bonnabel S, Deschaud JE, et al., 2015. Invariant EKF design for scan matching-aided localization. IEEE Trans Contr Syst Technol, 23(6):2440–2448. https://doi.org/10.1109/TCST.2015.2413933
Brekke E, Hallingstad O, Glattetre J, 2010. Tracking small targets in heavy-tailed clutter using amplitude information. IEEE J Ocean Eng, 35(2):314–329. https://doi.org/10.1109/JOE.2010.2044670
Brekke E, Hallingstad O, Glattetre J, 2011. The modified Riccati equation for amplitude-aided target tracking in heavy-tailed clutter. IEEE Trans Aerosp Electron Syst, 47(4):2874–2886. https://doi.org/10.1109/TAES.2011.6034670
Daniyan A, Lambotharan S, Deligiannis A, et al., 2018. Bayesian multiple extended target tracking using labeled random finite sets and splines. IEEE Trans Signal Process, 66(22):6076–6091. https://doi.org/10.1109/TSP.2018.2873537
Das A, Rao BD, 2012. SNR and noise variance estimation for MIMO systems. IEEE Trans Signal Process, 60(8):3929–3941. https://doi.org/10.1109/TSP.2012.2194707
Du L, Liu H, Bao Z, et al., 2007. Radar automatic target recognition using complex high-resolution range profiles. IET Radar Sonar Nav, 1(1):18–26. https://doi.org/10.1049/iet-rsn:20050119
Ehrman LM, Lanterman AD, 2008. Extended Kalman filter for estimating aircraft orientation from velocity measurements. IET Radar Sonar Nav, 2(1):12–16. https://doi.org/10.1049/iet-rsn:20070025
Ehrman LM, Mahapatra PR, 2009. Impact of noncoherent pulse integration on RCS-assisted tracking. IEEE Trans Aerosp Electron Syst, 45(4):1573–1579. https://doi.org/10.1109/TAES.2009.5310319
Gokce M, Kuzuoglu M, 2015. Unscented Kalman filter-aided Gaussian sum filter. IET Radar Sonar Nav, 9(5):589–599. https://doi.org/10.1049/iet-rsn.2014.0088
Hong L, Wu S, Layne JR, 2004. Invariant-based probabilistic target tracking and identification with GMTI/HRR measurements. IEE Proc Radar Sonar Nav, 151(5):280–290. https://doi.org/10.1049/ip-rsn:20040858
Liu CY, Shui PL, Li S, 2011. Unscented extended Kalman filter for target tracking. J Syst Eng Electron, 22(2):188–192. https://doi.org/10.3969/j.issn.1004-4132.2011.02.002
Liu D, Zhao YB, Xu BQ, 2019. Tracking algorithms aided by the pose of target. IEEE Access, 7:9627–9633. https://doi.org/10.1109/ACCESS.2019.2890981
Menegaz HMT, Ishihara JY, Kussaba HTM, 2019. Unscented Kalman filters for Riemannian state-space systems. IEEE Trans Autom Contr, 64(4):1487–1502. https://doi.org/10.1109/TAC.2018.2846684
Mertens M, Ulmke M, Koch W, 2016. Ground target tracking with RCS estimation based on signal strength measurements. IEEE Trans Aerosp Electron Syst, 52(1):205–220. https://doi.org/10.1109/TAES.2015.140866
Musicki D, Song TL, 2013. Track initialization: prior target velocity and acceleration moments. IEEE Trans Aerosp Electron Syst, 49(1):665–670. https://doi.org/10.1109/TAES.2013.6404131
Rashedi M, Liu JF, Huang B, 2018. Triggered communication in distributed adaptive high-gain EKF. IEEE Trans Ind Inform, 14(1):58–68. https://doi.org/10.1109/TII.2017.2715340
Ruan Y, Hong L, 2006. Feature-aided tracking with GMTI and HRR measurements via mixture density estimation. IEE Proc Contr Theory Appl, 153(3):342–356. https://doi.org/10.1049/ip-cta:20045099
Skolnik M, 1962. Introduction to Radar System. Zuo QS, Xu GL, Ma L, et al., translators, 2010. Publishing House of Electronics Industry, Beijing, China (in Chinese).
Tang X, Tharmarasa R, McDonald M, et al., 2017. Multiple detection-aided low-observable track initialization using ML-PDA. IEEE Trans Aerosp Electron Syst, 53(2): 722–735. https://doi.org/10.1109/TAES.2017.2664598
Tufts DW, Cann AJ, 1983. On Albersheim’s detection equation. IEEE Trans Aerosp Electron Syst, AES-19(4):643–646. https://doi.org/10.1109/TAES.1983.309356
Villano M, 2014. SNR and noise variance estimation in polarimetric SAR data. IEEE Geosci Remote Sens Lett, 11(1):278–282. https://doi.org/10.1109/LGRS.2013.2255860
Xi YH, Zhang XD, Li ZW, et al., 2018. Double-ended travelling-wave fault location based on residual analysis using an adaptive EKF. IET Signal Process, 12(8):1000–1008. https://doi.org/10.1049/iet-spr.2017.0486
Zhang XY, Huang JL, Wang GH, et al., 2019. Hypersonic target tracking with high dynamic biases. IEEE Trans Aerosp Electron Syst, 55(1):506–510. https://doi.org/10.1109/TAES.2018.2884187
Zhang Y, Mu HL, Jiang YC, et al., 2019. Moving target tracking based on improved GMPHD filter in circular SAR system. IEEE Geosci Remote Sens Lett, 16(4):559–563. https://doi.org/10.1109/LGRS.2018.2878467
Zhou GJ, Pelletier M, Kirubarajan T, et al., 2014. Statically fused converted position and Doppler measurement Kalman filters. IEEE Trans Aerosp Electron Syst, 50(1): 300–318. https://doi.org/10.1109/TAES.2013.120256
Author information
Authors and Affiliations
Contributions
Dai LIU designed the research. Dai LIU and Yong-bo ZHAO processed the data. Dai LIU drafted the manuscript. Zi-qiao YUAN, Jie-tao LI, and Guo-ji CHEN helped organize the manuscript. Dai LIU and Yong-bo ZHAO revised and finalized the paper.
Corresponding author
Additional information
Compliance with ethics guidelines
Dai LIU, Yong-bo ZHAO, Zi-qiao YUAN, Jie-tao LI, and Guo-ji CHEN declare that they have no conflict of interest.
Project supported by the National Natural Science Foundation of China (No. 61671357)
Rights and permissions
About this article
Cite this article
Liu, D., Zhao, Yb., Yuan, Zq. et al. Target tracking methods based on a signal-to-noise ratio model. Front Inform Technol Electron Eng 21, 1804–1814 (2020). https://doi.org/10.1631/FITEE.1900679
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1900679