认知结构模型机动目标跟踪算法
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摘要
针对复杂战场中环境特性复杂以及目标机动性能提升所带来的跟踪难题,提出一种基于人类认知机制的机动目标自适应跟踪算法。算法将人类"记忆"机制引入机动模型构建,利用神经网络对目标特征参数进行离线学习并存储,指导机动模型参数实时调整,使模型对运动状态的描述更加合理。为进一步提高跟踪性能,基于人类认知"感知-行动"循环理论,将雷达接收端经数据处理后的目标状态估计信息反馈至雷达发射端,以最小感知信息熵为代价函数,从波形库中自适应选择最佳波形来匹配目标。仿真对比实验表明,该算法对环境及目标的感知更加准确,融入波形选择的自适应目标跟踪算法要明显优于传统采用固定波形的跟踪算法。
Target tracking becomes hard due to the complicated battlefield environment and the improved target's maneuver. A maneuvering target tracking algorithm based on human cognition mechanism is put forward. The human"memory"mechanism is introduced into the construction of the maneuvering model. In order to make the description of the target motion reasonable,the neural network can obtain and store the information about the characteristics of the target through the off-line learning. The stored information can be used to guide the adjustment of the model parameters.Furthermore,based on the theory of the human cognitive perception-action cycle,the target state estimated information is fed back to the radar transmitter. With the minimum perceptual information entropy as the cost function,the optimal waveform is selected from the waveform library to match the target. The simulation results show that the algorithm is more accurate for the environment and target perception,and the adaptive target tracking algorithm with waveform selection is better than that with fixed waveform.
Target tracking becomes hard due to the complicated battlefield environment and the improved target's maneuver. A maneuvering target tracking algorithm based on human cognition mechanism is put forward. The human"memory"mechanism is introduced into the construction of the maneuvering model. In order to make the description of the target motion reasonable,the neural network can obtain and store the information about the characteristics of the target through the off-line learning. The stored information can be used to guide the adjustment of the model parameters.Furthermore,based on the theory of the human cognitive perception-action cycle,the target state estimated information is fed back to the radar transmitter. With the minimum perceptual information entropy as the cost function,the optimal waveform is selected from the waveform library to match the target. The simulation results show that the algorithm is more accurate for the environment and target perception,and the adaptive target tracking algorithm with waveform selection is better than that with fixed waveform.
引文
[1] Li X R,Jilkov V P. Survey of maneuvering target tracking-Part I:dynamic models[J]. IEEE Transactions on Aerospace and electronic system,2004,39(4):1333-1364.
[2] Li X R,Jilkov V P. Survey of maneuvering target tracking-Part V:multiple-model method[J]. IEEE Transactions on Aerospace and electronic system,2005,41(4):1255-1321.
[3] Sun W,Yang Y. Adaptive maneuvering frequency method of current statistical model[J]. IEEE/CAA Journal of Automatica Sinica,2017,4(1):154-160.
[4] Yang Y,Fan X,Zhuo Z,et al. AFAKF for manoeuvring target tracking based on current statistical model[J]. Iet Science Measurement&Technology,2016,10(6):637-643.
[5]吴旭姿,刘峥,刘韵佛.杂波环境下发射-接收联合优化的自适应滤波方法[J].电子与信息学报,2013,35(11):2657-2663.[Wu Xu-zi,Liu Zheng,Liu Yun-fo. Adaptive filter based on the co-optimized transmit-receiver in clutter[J]. Journal of Electronics&Information Technology,2013,35(11):2657-2663.]
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[8] Kershaw D J,Evans R J. Optimal waveform selection for tracking systems[J]. IEEE Transactions on Information Theory,1994,40(5):1536-1550.
[9]檀甲甲,张建秋.跟踪机动目标的雷达波形选择新方法[J].系统工程与电子技术,2011,33(3):515-522.[Tan Jiajia,Zhang Jian-qiu. New waveform selection approach to tracking maneuver targets[J]. Systems Engineering and Electronics,2011,33(3):515-522.]
[10] Yongqiang G,Yuming W,Hui L. Construction of waveform library in cognitive radar[J]. Polish Maritime Research,2017,24(94):22-29
[11]靳标,纠博,苏涛,等.针对机动目标跟踪的雷达发射波形选择[J].电子与信息学报,2014,36(8):1912-1918.[Jin Biao,Jiu Bo, Su Tao, et al. Radar transmitted waveform selection for maneuvering target tracking[J]. Journal of Electronics&Information Technology,2014,36(8):1912-1918.]
[12] Wu X,Chen J,Lu K. Investigation of system structure and information processing mechanism for cognitive sky-wave overthe-horizon radar[J]. Journal of Systems Engineering and Electronics,2016,27(4):797-806.
[13] Wang B,Sun J,Zhang X,et al. A waveform-agile un-scented Kalman filter for radar target tracking[C]. IEEE Image and Signal Processing, BioMedical Engineering and Informatics(CISP-BMEI)conference,Datong,China,Oct. 15-17,2016.
[14]王树亮,毕大平,阮怀林.认知雷达波形自适应数据关联跟踪算法[J].宇航学报,2017,38(12):1331-1338.[Wang shuliang,Bi Da-ping,Ruan Huai-lin. Waveform self-adaption data association algorithm for cognitive radar tracking[J]. Journal of Astronautics,2017,38(12):1331-1338.]
[15] Haykin S,Zia A,Arasaratnam I,et al. Cognitive tracking radar[C]. IEEE Radar Conference,Washington DC,USA,May 10-14,2010.
[16] Fatemi M,Haykin S. Cognitive control:theory and application[J]. IEEE Access,2014,2:698-710.
[17]周宏仁,敬忠良,王培德.机动目标跟踪[M].北京:国防工业出版社,1991:15-16,134-142.
[18]张卓然,叶广强,赵晓林.基于改进当前统计模型的自适应无源跟踪算法[J].计算机工程与应用,2017,53(3):124-130.[Zhang Zhuo-ran, Ye Guang-qiang, Zhao Xiao-lin.Adaptive algorithm based on modified current statistical model for passive tracking[J]. Computer Engineering and Applications,2017,53(3):124-130.]
[19] Wang M,Wang H. An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration[J].Chinese Journal of Aeronautics,2017,30(1):426-433.
[2] Li X R,Jilkov V P. Survey of maneuvering target tracking-Part V:multiple-model method[J]. IEEE Transactions on Aerospace and electronic system,2005,41(4):1255-1321.
[3] Sun W,Yang Y. Adaptive maneuvering frequency method of current statistical model[J]. IEEE/CAA Journal of Automatica Sinica,2017,4(1):154-160.
[4] Yang Y,Fan X,Zhuo Z,et al. AFAKF for manoeuvring target tracking based on current statistical model[J]. Iet Science Measurement&Technology,2016,10(6):637-643.
[5]吴旭姿,刘峥,刘韵佛.杂波环境下发射-接收联合优化的自适应滤波方法[J].电子与信息学报,2013,35(11):2657-2663.[Wu Xu-zi,Liu Zheng,Liu Yun-fo. Adaptive filter based on the co-optimized transmit-receiver in clutter[J]. Journal of Electronics&Information Technology,2013,35(11):2657-2663.]
[6] Haykin S. Cognitive radar:a way of the future[J]. IEEE Signal Processing Magazine,2006,23:30-40.
[7]黎湘,范梅梅.认知雷达及其关键技术研究进展[J].电子学报,2012,40(9):1863-1870.[Li Xiang,Fan Mei-mei.Research advance on cognitive radar and its key technology[J].Acta Electronica Sinica,2012,40(9):1863-1870.]
[8] Kershaw D J,Evans R J. Optimal waveform selection for tracking systems[J]. IEEE Transactions on Information Theory,1994,40(5):1536-1550.
[9]檀甲甲,张建秋.跟踪机动目标的雷达波形选择新方法[J].系统工程与电子技术,2011,33(3):515-522.[Tan Jiajia,Zhang Jian-qiu. New waveform selection approach to tracking maneuver targets[J]. Systems Engineering and Electronics,2011,33(3):515-522.]
[10] Yongqiang G,Yuming W,Hui L. Construction of waveform library in cognitive radar[J]. Polish Maritime Research,2017,24(94):22-29
[11]靳标,纠博,苏涛,等.针对机动目标跟踪的雷达发射波形选择[J].电子与信息学报,2014,36(8):1912-1918.[Jin Biao,Jiu Bo, Su Tao, et al. Radar transmitted waveform selection for maneuvering target tracking[J]. Journal of Electronics&Information Technology,2014,36(8):1912-1918.]
[12] Wu X,Chen J,Lu K. Investigation of system structure and information processing mechanism for cognitive sky-wave overthe-horizon radar[J]. Journal of Systems Engineering and Electronics,2016,27(4):797-806.
[13] Wang B,Sun J,Zhang X,et al. A waveform-agile un-scented Kalman filter for radar target tracking[C]. IEEE Image and Signal Processing, BioMedical Engineering and Informatics(CISP-BMEI)conference,Datong,China,Oct. 15-17,2016.
[14]王树亮,毕大平,阮怀林.认知雷达波形自适应数据关联跟踪算法[J].宇航学报,2017,38(12):1331-1338.[Wang shuliang,Bi Da-ping,Ruan Huai-lin. Waveform self-adaption data association algorithm for cognitive radar tracking[J]. Journal of Astronautics,2017,38(12):1331-1338.]
[15] Haykin S,Zia A,Arasaratnam I,et al. Cognitive tracking radar[C]. IEEE Radar Conference,Washington DC,USA,May 10-14,2010.
[16] Fatemi M,Haykin S. Cognitive control:theory and application[J]. IEEE Access,2014,2:698-710.
[17]周宏仁,敬忠良,王培德.机动目标跟踪[M].北京:国防工业出版社,1991:15-16,134-142.
[18]张卓然,叶广强,赵晓林.基于改进当前统计模型的自适应无源跟踪算法[J].计算机工程与应用,2017,53(3):124-130.[Zhang Zhuo-ran, Ye Guang-qiang, Zhao Xiao-lin.Adaptive algorithm based on modified current statistical model for passive tracking[J]. Computer Engineering and Applications,2017,53(3):124-130.]
[19] Wang M,Wang H. An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration[J].Chinese Journal of Aeronautics,2017,30(1):426-433.