Radar target classification using Doppler signatures of human locomotion models

被引：36
作者：
Dept. of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel [1 ]
机构：
[1] Dept. of Electrical and Computer Engineering, Ben-Gurion University of the Negev
来源：
IEEE Trans. Aerosp. Electron. Syst. | 2007年 / 4卷 / 1510-1522期
关键词：
Doppler radar;
D O I：
10.1109/TAES.2007.4441755
中图分类号：
学科分类号：
摘要：
The problem of target classification for ground surveillance Doppler radars is addressed. Two sources of knowledge are presented and incorporated within the classification algorithms: 1) statistical knowledge on radar target echo features, and 2) physical knowledge, represented via the locomotion models for different targets. The statistical knowledge is represented by distribution models whose parameters are estimated using a collected database. The physical knowledge is represented by target locomotion and radar measurements models. Various concepts to incorporate these sources of knowledge are presented. These concepts are tested using real data of radar echo records, which include three target classes: one person, two persons and vehicle. A combined approach, which implements both statistical and physical prior knowledge provides the best classification performance, and it achieves a classification rate of 99% in the three-class problem in high signal-to-noise conditions. © 2007 IEEE.
引用
收藏
页码：1510 / 1522
页数：12
相关论文
共 38 条
  • [1] Skolnik M., Introduction to Radar Systems, (2000)
  • [2] Jahangir M., Ponting K., O'Loghlen J.W., A robust Doppler classification technique based on hidden Markov models, IEE Proceedings, Radar, Sonar and Navigation, pp. 162-166, (2002)
  • [3] Jahangir M., Ponting K., O'Loghlen J.W., Application of HMM to the classification of ground moving targets, Proceedings of IGARSS 2000, (2000)
  • [4] Stove A.G., Sykes S.R., A Doppler-based automatic target classifier for a battlefield surveillance radar, Proceedings of RADAR 2002, pp. 419-423, (2002)
  • [5] Bhau B., Automatic target recognition: State of the art survey, IEEE Transactions on Aerospace and Electronic Systems, 22, pp. 364-377, (1986)
  • [6] Copsey K., Webb A., Bayesian gamma mixture model approach to radar target recognition, IEEE Transactions on Aerospace and Electronic Systems, 39, 4, pp. 1201-1217, (2003)
  • [7] Zyweck A., Bogner R.E., Radar target classification of commercial aircraft, IEEE Transactions on Aerospace and Electronic Systems, 32, 2, pp. 598-606, (1996)
  • [8] Bachman C.M., Charles M., Et al., Classification of simulated radar imagery using lateral inhibition neural network, Proceedings of IEEE International Joint Conference on Neural Networks, (1992)
  • [9] Leslie N., A comparison of 1-D and 2-D algorithms for radar target classification, Proceedings of the IEEE International Conference on Systems Engineering, (1991)
  • [10] Rihaczek A.W., Hershkowitz S.J., Theory and Practice of Radar Target Identification, (2000)
1234