JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2020 Vol.21 No.12 P.1804-1814

http://doi.org/10.1631/FITEE.1900679

Target tracking methods based on a signal-to-noise ratio model

Author(s): Dai Liu, Yong-bo Zhao, Zi-qiao Yuan, Jie-tao Li, Guo-ji Chen
Affiliation(s): National Lab of Radar Signal Processing, Xidian University, Xi�an 710071, China; more
Corresponding email(s): ybzhao@xidian.edu.cn
Key Words: Signal-to-noise ratio (SNR) model, Target tracking, Angle error, Range error, Nonlinear filter

Share this article to： More <<< Previous Article \|

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.

基于信噪比模型的目标跟踪算法

刘代^1,2，赵永波¹，袁子乔²，李杰涛²，陈国际²
¹西安电子科技大学雷达信号处理国家重点实验室，中国西安市，710071
²西安电子工程研究所，中国西安市，710100

摘要：传统目标跟踪算法中测角误差和测距误差取经验值，量测噪声为常数。本文分析测角误差和测距误差的影响因素，发现它们都与目标信噪比相关。于是建立雷达信噪比模型，将信噪比信息应用到目标跟踪算法。结合先进的非线性滤波算法，提出利用信噪比的扩展卡尔曼滤波（SNR-EKF）算法和利用姿态角的不敏卡尔曼滤波（SNR-UKF）算法。SNR-EKF和SNR-UKF相比位置精度差距不大，但在计算耗时上SNR-EKF算法较优，速度精度上SNR-UKF占优。仿真结果表明，利用信噪比的目标跟踪算法相比传统的EKF、UKF算法目标跟踪性能得到很大提高，体现在跟踪精度显著提高，收敛速度显著加快。

关键词：信噪比模型；目标跟踪；角度误差；距离误差；非线性滤波

Reference

[1]Alberhseim WJ, 1981. A closed-form approximation to Robertson’s detection characteristics. Proc IEEE, 69(7):839.

[2]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.

[3]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.

[4]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.

[5]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.

[6]Das A, Rao BD, 2012. SNR and noise variance estimation for MIMO systems. IEEE Trans Signal Process, 60(8):3929- 3941.

[7]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.

[8]Ehrman LM, Lanterman AD, 2008. Extended Kalman filter for estimating aircraft orientation from velocity measurements. IET Radar Sonar Nav, 2(1):12-16.

[9]Ehrman LM, Mahapatra PR, 2009. Impact of noncoherent pulse integration on RCS-assisted tracking. IEEE Trans Aerosp Electron Syst, 45(4):1573-1579.

[10]Gokce M, Kuzuoglu M, 2015. Unscented Kalman filter-aided Gaussian sum filter. IET Radar Sonar Nav, 9(5):589-599.

[11]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.

[12]Liu CY, Shui PL, Li S, 2011. Unscented extended Kalman filter for target tracking. J Syst Eng Electron, 22(2):188-192.

[13]Liu D, Zhao YB, Xu BQ, 2019. Tracking algorithms aided by the pose of target. IEEE Access, 7:9627-9633.

[14]Menegaz HMT, Ishihara JY, Kussaba HTM, 2019. Unscented Kalman filters for Riemannian state-space systems. IEEE Trans Autom Contr, 64(4):1487-1502.

[15]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.

[16]Musicki D, Song TL, 2013. Track initialization: prior target velocity and acceleration moments. IEEE Trans Aerosp Electron Syst, 49(1):665-670.

[17]Rashedi M, Liu JF, Huang B, 2018. Triggered communication in distributed adaptive high-gain EKF. IEEE Trans Ind Inform, 14(1):58-68.

[18]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.

[19]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).

[20]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.

[21]Tufts DW, Cann AJ, 1983. On Albersheim’s detection equation. IEEE Trans Aerosp Electron Syst, AES-19(4):643-646.

[22]Villano M, 2014. SNR and noise variance estimation in polarimetric SAR data. IEEE Geosci Remote Sens Lett, 11(1):278-282.

[23]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.

[24]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.

[25]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.

[26]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.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Similar articles

- Go to

基于信噪比模型的目标跟踪算法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference