基于主成分分析的测量雷达效能评估方法
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摘要
针对导弹试验,雷达在跟踪测量目标时,需评估雷达测量效能问题,以便提供更准确可靠的测量目标航迹数据。结合试验和工程实际提出一种基于主成分分析的雷达效能分析方法。通过算例试验数据分析,得出了影响雷达效能的因素中目标的过载、目标的姿态角和目标的温度对其影响较大,当目标匀速运动、目标机动性小和目标姿态稳定时,雷达跟踪较为稳定,雷达测量效能发挥较好。算例数据分析为雷达的使用提供了定量分析,可在靶场推广应用。
In ballistic missile flight test, when radar is tracking measurement goals, radar measuring effectiveness needs to be evaluated, in order to provide a more accurate and reliable measurement tracking data. A method of radar effectiveness analysis based on principal component analysis is proposed based on experiment and engineering practice. Through the analysis of example test data, it is concluded that the influence of target overload, target attitude angle and target temperature on radar efficiency is greater. When the target has uniform motion, the target mobility is small, and the target attitude is stable, the radar tracking is more stable and the radar measurement efficiency is better. The data analysis of the example provides quantitative analysis for the use of radar, and can be popularized in shooting range.
In ballistic missile flight test, when radar is tracking measurement goals, radar measuring effectiveness needs to be evaluated, in order to provide a more accurate and reliable measurement tracking data. A method of radar effectiveness analysis based on principal component analysis is proposed based on experiment and engineering practice. Through the analysis of example test data, it is concluded that the influence of target overload, target attitude angle and target temperature on radar efficiency is greater. When the target has uniform motion, the target mobility is small, and the target attitude is stable, the radar tracking is more stable and the radar measurement efficiency is better. The data analysis of the example provides quantitative analysis for the use of radar, and can be popularized in shooting range.
引文
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[10] NAIDU V P S. Hybrid DDCT-PCA based multi sensor image fusion[J]. Journal of Optics, 2014, 43(1): 48-61.
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[12] KAO Y H, ROY B J V. Learning a factor model via regularized PCA[J]. Machine Learning, 2013, 91(3): 279-303.
[2] 胡来招.无源定位[M].北京:国防工业出版社,2004.HU Laizhao. Passive location[M]. Beijing: National Defense Industry Press, 2004.
[3] 周颖.电子战条件下导弹防御相控阵雷达仿真与评估研究[D].长沙:国防科技大学,2005.ZHOU Ying. Simulation and evaluation of missile defense phased array radar under EW condition[D]. Changsha: National University of Defense Technology, 2005.
[4] 陈永光,李修和,沈阳.组网雷达作战能力分析与评估[M].北京:国防工业出版社,2006.CHEN Yongguang, LI Xiuhe, SHEN Yang. Analysis and evaluation of operational capability of network radar[M]. Beijing: National Defense Industry Press, 2006.
[5] Christian Kühnert Thomas Bernard. Ereignisdetektion in trinkwassernetzen mittels PCA und DPCA[J]. Technisches Messen, 2016,83(2): 96-101.
[6] SLá O H, KLASOVá V. Slovak creativity index-A PCA based approach[J]. European Spatial Research and Policy, 2016, 23(1): 47-64.
[7] TAOUALI J I, ELAISSI O. A new online fault detection method based on PCA technique[J]. IMA Journal of Mathematical Control and Information, 2014, 31(4): 487-499.
[8] CHOU T J T, CHOU P Y. Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression[J]. International Journal of Electronics: Theoretical & Experimental, 2015,102(10/12): 1831-1851.
[9] XU X Z, XIE L, WANG S Q. Multimode process monitoring with PCA mixture model[J]. Computers and Electrical Engineering, 2014, 40(7): 2101-2112.
[10] NAIDU V P S. Hybrid DDCT-PCA based multi sensor image fusion[J]. Journal of Optics, 2014, 43(1): 48-61.
[11] XIANG J, ZHU H L. Efficient sparse PCA-based method for motion recognition[J]. Journal of Information and Computational Science, 2014, 11(17): 6419-6426.
[12] KAO Y H, ROY B J V. Learning a factor model via regularized PCA[J]. Machine Learning, 2013, 91(3): 279-303.