Short-Range Multitarget Motion Parameter Estimation Method Based on Hough Transform
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摘要
This study proposes a novel short-range multitarget motion parameter estimation method based on Hough transform. The proposed method can be used for multitarget detection, motion parameter estimation, and data association. In our proposed method, the measured radial distance and Doppler frequency versus time data is mapped to the motion parameter space by Hough transform. The motion parameter space data is binarized to determine the number of targets. The unsupervised nearest neighbor clustering technique is used to determine the search space of targets. The maximum value in each search space is estimated as the motion parameter of the corresponding target. Simulation results show that the proposed method has higher parameter estimation accuracy than that of conventional methods.
This study proposes a novel short-range multitarget motion parameter estimation method based on Hough transform. The proposed method can be used for multitarget detection, motion parameter estimation, and data association. In our proposed method, the measured radial distance and Doppler frequency versus time data is mapped to the motion parameter space by Hough transform. The motion parameter space data is binarized to determine the number of targets. The unsupervised nearest neighbor clustering technique is used to determine the search space of targets. The maximum value in each search space is estimated as the motion parameter of the corresponding target. Simulation results show that the proposed method has higher parameter estimation accuracy than that of conventional methods.
This study proposes a novel short-range multitarget motion parameter estimation method based on Hough transform. The proposed method can be used for multitarget detection, motion parameter estimation, and data association. In our proposed method, the measured radial distance and Doppler frequency versus time data is mapped to the motion parameter space by Hough transform. The motion parameter space data is binarized to determine the number of targets. The unsupervised nearest neighbor clustering technique is used to determine the search space of targets. The maximum value in each search space is estimated as the motion parameter of the corresponding target. Simulation results show that the proposed method has higher parameter estimation accuracy than that of conventional methods.
引文
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[2]G.Dominguez,“PLA Rocket Force becoming‘more versatile’,says report”,available at https://janes.ihs.com/Janes/Display/jdw66268-jdw-2017,2018-3-5.
[3]F.Gao,F.Ma,X.L.Luo,et al.,“Hierarchical featurebased detection method for SAR targets under complex environment”,Chinese Journal of Electronics,Vol.26,No.3,pp.647-653,2017.
[4]H.Y.Zhu,K.Guo and S.Chen,“Fusion of Gaussian mixture models for maneuvering target tracking in the presence of unknown cross-correlation”,Chinese Journal of Electronics,Vol.25,No.2,pp.270-276,2016.
[5]D.K.Zheng,S.Y.Wang and X.Qin,“A dynamic programming track-before-detect algorithm based on local linearization for non-Gaussian clutter background”,Chinese Journal of Electronics,Vol.25,No.3,pp.583-590,2016.
[6]A.Sinha,Z.Ding,T.Kirubarajan,et al.,“Track quality based multitarget tracking approach for global nearest-neighbor association”,IEEE Transactions on Aerospace and Electronic Systems,Vol.48,No.2,pp.1179-1191,2012.
[7]G.S.Yang,C.Z.Hou and L.H.Dou,“Study of two-gate tracking algorithm for JPDA”,Chinese Journal of Electronics,Vol.12,No.3,pp.381-384,2003.
[8]P.Lv,G.H.Wei and W.Cui,“Impact of acceleration on miss distance parameter estimation”,Journal of Guidance Control&Dynamics,Vol.41,No.5,pp.1174-1181,2018.
[9]P.Lv,G.H.Wei,S.Lu,et al.,“A modified multiple hypothesis tracking algorithm with application to range and Doppler measurements”,IET International Radar Conference 2013,Xi’an,China,pp.1-5,2013.
[10]J.Wang and S.L.Wu,“Investigation of multiple target location in vector miss distance measurement system”,2004 7th International Conference on Signal Processing Proceedings,Beijing,China,pp.2013-2016,2004.
[11]S.J.Hou and S.L.Wu,“Multi-targets miss distance measurement based on a sequence of image processing techniques”,2006 8th International Conference on Signal Processing,Beijing,China,2006.
[12]D.W.Feng,S.L.Wu,and G.H.Wei,“Multi-targets miss distance measurement using sequential range-Doppler image”,2012 11th International Conference on Signal Processing,Beijing,China,pp.1839-1842,2012.
[13]R.Deming,J.Schindler and L.Perlovsky,“Multitarget/multi-sensor tracking using only range and Doppler measurements”,IEEE Transactions on Aerospace and Electronic Systems,Vol.45,No.2,pp.593-611,2009.
[14]L.Wang,R.Tao,S.Y.Zhou,et al.,“Detection and imaging of slowly moving target of airborne SAR based on the GMCWD-Hough transform”,Chinese Journal of Electronics,Vol.13,No.3,pp.536-539,2004.
[15]J.Dennis,H.D.Tran and H.Li,“Generalized Hough transform for speech pattern classification”,IEEE/ACMTransactions on Audio,Speech,and Language Processing,Vol.23,No.11,pp.1963-1972,2015.
[16]M.Quintana,J.Torres and J.M.Menéndez,“A simplified computer vision system for road surface inspection and maintenance”,IEEE Transactions on Intelligent Transportation Systems,Vol.17,No.3,pp.608-619,2016.
[17]H.Y.Shi and N.Zhang,“Moving targets indication method in single SAR imagery based on sparse representation and road information”,Acta Electronica Sinica,Vol.43,No.3,pp.431-439,2015.(in Chinese)
[18]H.M.Liu,W.J.Xiong,W.Zhao,et al.,“Reflection symmetry axis detection based on improved mean-standard deviation curve descriptor”,Acta Electronica Sinica,Vol.45,No.3,pp.1701-1706,2017.(in Chinese)
[19]R.K.Satzoda,S.Suchitra and T.Srikanthan,“Parallelizing the Hough transform computation”,IEEE Signal Processing Letters,Vol.15,pp.297-300,2008.
[20]H.T.Ngo,R.N.Rakvic,R.P.Broussard,et al.,“Resourceaware architecture design and implementation of Hough transform for a real-time iris boundary detection system”,IEEE Transactions on Consumer Electronics,Vol.60,No.3,pp.485-492,2014.
[21]C.Cariou and K.Chehdi,“Unsupervised nearest neighbors clustering with application to hyperspectral images”,IEEEJournal of Selected Topics in Signal Processing,Vol.9,No.6,pp.1105-1116,2015.
[22]I.J.Cox,“A review of statistical data association techniques for motion correspondence”,International Journal of Computer Vision,Vol.10,No.1,pp.53-66,1993.
[23]S.Oh,S.Russell and S.Sastry,“Markov chain Monte Carlo data association for multi-target tracking”,IEEETransactions on Automatic Control,Vol.54,No.3,pp.481-497,2009.