ANNDP-TBD Algorithm For Passive Coherent Location
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- 英文论文题名：ANNDP-TBD Algorithm For Passive Coherent Location
- 论文作者：Peinan Zhang ; Jian Zheng ; Jinxing Pan ; Songtao Feng ; Yunfei Guo
- 英文论文作者：Peinan Zhang ; Jian Zheng ; Jinxing Pan ; Songtao Feng ; Yunfei Guo ; Automation School ; Hangzhou Dianzi University ; The 28th Research Institute of China Electronics Group Corporation
- 年：2017
- 作者机构：Automation School, Hangzhou Dianzi University;The 28th Research Institute of China Electronics Group Corporation;
- 英文论文关键词：low observable targets ; track-before-detect ; dynamic programming ; neural network
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TN911.7
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：525k
- 原文格式：D
- 会议级别：全国
摘要
In order to address the problem of detecting and tracking low signal-to-noise-ratio(SNR) targets in bistatic passive coherent location(PCL) system, an improved dynamic programming track-before-detect algorithm is proposed. First, the polar measurements are converted to Cartesian measurements. Second, the Cartesian measurements are clustered with the neural network, and the amplitudes of the valid measurements are scaled to improve the performance. Finally, the DP-TBD algorithm is used for amplitude accumulation and state backtracking. Simulation results verify the effectiveness of the proposed method.
In order to address the problem of detecting and tracking low signal-to-noise-ratio(SNR) targets in bistatic passive coherent location(PCL) system, an improved dynamic programming track-before-detect algorithm is proposed. First, the polar measurements are converted to Cartesian measurements. Second, the Cartesian measurements are clustered with the neural network, and the amplitudes of the valid measurements are scaled to improve the performance. Finally, the DP-TBD algorithm is used for amplitude accumulation and state backtracking. Simulation results verify the effectiveness of the proposed method.
In order to address the problem of detecting and tracking low signal-to-noise-ratio(SNR) targets in bistatic passive coherent location(PCL) system, an improved dynamic programming track-before-detect algorithm is proposed. First, the polar measurements are converted to Cartesian measurements. Second, the Cartesian measurements are clustered with the neural network, and the amplitudes of the valid measurements are scaled to improve the performance. Finally, the DP-TBD algorithm is used for amplitude accumulation and state backtracking. Simulation results verify the effectiveness of the proposed method.
引文
[1]Liu X,Chen X,Zeng T,and Zheng L,New analytical approach to detection threshold of a dynamic programming track-before-detect algorithm,IET Radar Sonar Navigation,7(7):773-779,2013.
[2]Barniv Y,Dynamic programming solution for detecting dim moving targets,IEEE Transactions on Aerospace and Electronic Systems,21(1):144-156,1985.
[3]Arnold J,Shaw S W,and Pasternack H,Efficient target tracking using dynamic programming,IEEE Transactions on Aerospace and Electronic Systems,29(1):44-56,1993.
[4]Tonissen S M,Evans R J,Performance of dynamic programming techniques for track-before-detect,IEEE Transactions on Aerospace and Electronic Systems,32(4):1440-1451,1996.
[5]Grossi E,Lops M,and Venturino L,A novel dynamic programming algorithm for track-before-detect in radar systems,IEEE Transactions on Signal Processing,61(10):2608-2619,2013.
[6]Grossi E,Lops M,and Venturino L,Track-before-detect for multiframe detection with censored observations,IEEE Transactions on Aerospace and Electronic Systems,50(3):2032-2046,2014.
[7]Zheng D,Wang S,and Qin X,A dynamic programming track-before-detect algorithm based on local linearization for non-gaussian clutter background,Chinese Journal of Electronics,25(3):583-590,2016.
[8]Ince T,Kiranyaz S,Eren L,Askar M and Gabbouj M,Real-time motor fault detection by 1-D convolutional neural networks,IEEE Transactions on Industrial Electronics,63(11):7067-7075,2016.
[9]Cheng G,Zhou P,and Han J,Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images,IEEE Transactions on Geoscience and Remote Sensing,54(12):7405-7415,2016.
[10]Punithakumar K,Kirubarajan T,Hernandez M,Multisensor deployment using PCRLBS,incorporating sensor deployment and motion uncertainties,IEEE Transactions on Aerospace and Electronic Systems,42(4):1474-1485,2006.
[11]Schoenecker S,Willett P,Bar-Shalom Y,Extreme-value analysis for ML-PMHT,Part 1:threshold determination,IEEE Transactions on Aerospace and Electronic Systems,50(4):2500-2514,2014.
[2]Barniv Y,Dynamic programming solution for detecting dim moving targets,IEEE Transactions on Aerospace and Electronic Systems,21(1):144-156,1985.
[3]Arnold J,Shaw S W,and Pasternack H,Efficient target tracking using dynamic programming,IEEE Transactions on Aerospace and Electronic Systems,29(1):44-56,1993.
[4]Tonissen S M,Evans R J,Performance of dynamic programming techniques for track-before-detect,IEEE Transactions on Aerospace and Electronic Systems,32(4):1440-1451,1996.
[5]Grossi E,Lops M,and Venturino L,A novel dynamic programming algorithm for track-before-detect in radar systems,IEEE Transactions on Signal Processing,61(10):2608-2619,2013.
[6]Grossi E,Lops M,and Venturino L,Track-before-detect for multiframe detection with censored observations,IEEE Transactions on Aerospace and Electronic Systems,50(3):2032-2046,2014.
[7]Zheng D,Wang S,and Qin X,A dynamic programming track-before-detect algorithm based on local linearization for non-gaussian clutter background,Chinese Journal of Electronics,25(3):583-590,2016.
[8]Ince T,Kiranyaz S,Eren L,Askar M and Gabbouj M,Real-time motor fault detection by 1-D convolutional neural networks,IEEE Transactions on Industrial Electronics,63(11):7067-7075,2016.
[9]Cheng G,Zhou P,and Han J,Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images,IEEE Transactions on Geoscience and Remote Sensing,54(12):7405-7415,2016.
[10]Punithakumar K,Kirubarajan T,Hernandez M,Multisensor deployment using PCRLBS,incorporating sensor deployment and motion uncertainties,IEEE Transactions on Aerospace and Electronic Systems,42(4):1474-1485,2006.
[11]Schoenecker S,Willett P,Bar-Shalom Y,Extreme-value analysis for ML-PMHT,Part 1:threshold determination,IEEE Transactions on Aerospace and Electronic Systems,50(4):2500-2514,2014.