基于正弦高斯混合模型的磁目标通用快速检测
|
摘要
在诸如潜艇等磁目标的探测问题中,被测磁目标信号可能表现出参数待定的磁异信号或极低频磁信号的特征。提出了一种可同时检测参数待定的微弱磁异信号和微弱极低频磁信号的通用快速检测方法。通过抓住磁异信号和极低频磁信号在统计上共同具有的分段类正弦特征,建立了正弦高斯混合模型,实现了对参数待定的磁异信号和极低频磁信号的通用建模。基于正弦高斯混合模型和序贯检测理论构建了一种累积和检测器,实现了对微弱磁异信号和微弱极低频磁信号的通用快速检测,给出了未知模型参数的确定方法,分析了所构建检测器的序贯检测性能。并进一步研究了所构建检测器对不同的微弱磁异信号和微弱极低频磁信号的检测性能。在以地磁为背景的磁目标探测系统实验中,验证了所构建检测器对微弱磁异信号和微弱极低频磁信号的通用性和快速性。实验表明,所构建检测器的信噪比可低至-8 dB,计算量相比于传统检测方法降低4个量级。
The signal characteristic of a magnetic object, such as a submarine, is indeterminate in the magnetic object detection, which may appear as a magnetic anomaly signal or an extremely low frequency magnetic signal with undetermined coefficients. This paper proposes a universal fast detection method for both the weak magnetic anomaly signal and the weak extremely low frequency magnetic signal with undetermined coefficients. A sine-Gauss mixture model is built based on the piecewise sinusoidal statistical characteristics of the magnetic anomaly signal and extremely low frequency magnetic signal. This universal model can represent both the magnetic anomaly signal and extremely low frequency magnetic signal with undetermined coefficients. A detector is developed based on the sine-Gauss mixture model and the sequential detection theory, which realizes the universal fast detection for both types of signals mentioned above. The unknown parameters in the detector are identified, and the sequential detection performance of the detector is studied. In addition, the detection performance for different weak magnetic anomaly signals and weak extremely low frequency magnetic signals is analyzed. The universality and rapidity of the designed detector for both the weak magnetic anomaly signal and the weak extremely low frequency magnetic signal are verified based on experiment system with geomagnetism. The experimental results indicate that the signal-to-noise rate can be-8 dB, the amount of calculation is reduced by 4 orders compared with the traditional detection methods.
The signal characteristic of a magnetic object, such as a submarine, is indeterminate in the magnetic object detection, which may appear as a magnetic anomaly signal or an extremely low frequency magnetic signal with undetermined coefficients. This paper proposes a universal fast detection method for both the weak magnetic anomaly signal and the weak extremely low frequency magnetic signal with undetermined coefficients. A sine-Gauss mixture model is built based on the piecewise sinusoidal statistical characteristics of the magnetic anomaly signal and extremely low frequency magnetic signal. This universal model can represent both the magnetic anomaly signal and extremely low frequency magnetic signal with undetermined coefficients. A detector is developed based on the sine-Gauss mixture model and the sequential detection theory, which realizes the universal fast detection for both types of signals mentioned above. The unknown parameters in the detector are identified, and the sequential detection performance of the detector is studied. In addition, the detection performance for different weak magnetic anomaly signals and weak extremely low frequency magnetic signals is analyzed. The universality and rapidity of the designed detector for both the weak magnetic anomaly signal and the weak extremely low frequency magnetic signal are verified based on experiment system with geomagnetism. The experimental results indicate that the signal-to-noise rate can be-8 dB, the amount of calculation is reduced by 4 orders compared with the traditional detection methods.
引文
[1] ZHOU J, CHEN J, SHAN Z. Spatial signature analysis of submarine magnetic anomaly at low altitude[J]. IEEE Transactions on Magnetics, 2017(99):1-1.
[2] 银鸿, 文轩, 杨生胜, 等. 基于磁异常检测的磁性运动目标识别方法研究[J]. 仪器仪表学报, 2018, 39(3):258- 264.YIN H, WEN X, YANG SH SH, et al. Research on the moving ferromagnetic object recognition method based on magnetic anomaly detection[J]. Chinese Journal of Scientific Instrument, 2018, 39(3):258- 264.
[3] HOLMES J. Exploitation of a ship′s magnetic field signatures[J]. Synthesis Lectures on Computational Electromagnetics, 2006(1):1- 68.
[4] BIRSAN M. Measurement of the extremely low frequency (ELF) magnetic field emission from a ship[J]. Measurement Science & Technology, 2011, 22(8): 085709.
[5] SHEINKER A, GINZBURG B, SALOMONSKI N, et al. Magnetic anomaly detection using high-order crossing method[J]. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(4):1095-1103.
[6] GINZBURG B, FRUMKIS L, KAPLAN B Z. Processing of magnetic scalar gradiometer signals using orthonormalized functions[J]. Sensors & Actuators A Physical, 2002, 102(1- 2):67-75.
[7] 郭静波, 谭博, 蔡雄. 基于反相双峰指数模型的微弱瞬态极低频信号的估计与检测[J]. 仪器仪表学报, 2015, 36(8):1682-1691.GUO J B, TAN B, CAI X. Estimation and detection of the weak transient ELF signal based on the phase inverting double-peak exponential model[J]. Chinese Journal of Scientific Instrument, 2015, 36(8): 1682-1691.
[8] PIAO G Y, GUO J B, HU T H. A novel real-time detection of orthogonal transient weak ELF magnetic signals[C].Sensors Applications Symposium, 2017.
[9] HOLMES, J. Reduction of a ship′s magnetic field signatures[J]. Synthesis Lectures on Computational Electromagnetics, 2008, 1(3):1- 68.
[10] GINZBURG B, FRUMKIS L, KAPLAN B Z. An efficient method for processing scalar magnetic gradiometer signals[J]. Sensors & Actuators A Physical, 2004, 114(1):73-79.
[11] ABRAHAMSSON R, KAY S M, STOICA P. Estimation of the parameters of a bilinear model with applications to submarine detection and system identification[J]. Digital Signal Processing, 2007, 17(4):756-773.
[12] SHEINKER A, SALOMONSKI N, GINZBURG B, et al. Magnetkic anomaly detection using entropy filter[J]. Measurement Science & Technology, 2008, 19(4): 045205.
[13] 李国正, 张波. 基于Duffing振子检测频率未知微弱信号的新方法[J]. 仪器仪表学报, 2017, 38(1): 181-189.LI G ZH, ZHANG B. Novel method for detecting weak signal with unknown frequency based on duffing oscillator[J]. Chinese Journal of Scientific Instrument, 2017, 38(1):181-189.
[14] 张刚, 张义俊, 张天骐. α噪声下自适应非线性耦合双稳随机共振弱信号检测[J]. 电子测量与仪器学报, 2018, 32(5):142-150.ZHANG G, ZHANG Y J, ZHANG T Q. Adaptive coupled bistable stochastic resonance weak signal detection under α noise[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(5): 142-150.
[15] XING H Y, ZHANG Q, LU CH X. Adaptive stochastic resonance method for weak signal detection based on particle swarm optimization[J]. Instrumentation, 2015, 2(2):3-10.
[16] 王冀超, 杨正华, 岳亮, 等. Lyapunov指数算法在微弱信号检测中的应用[J]. 电子测量技术, 2017, 40(8):164-168.WANG Y CH, YANG ZH H, YUE L, et al. Application of Lyapunov exponent algorithm in the weak signal detection[J]. Electronic Measurement Technology, 2017, 40(8):164-168.
[17] 张伟达, 陈良, 梅芳, 等. 频谱监测中的多频随机共振检测[J]. 国外电子测量技术, 2017, 36(9):9-12.ZHANG W D, CHEN L, MEI F, et al. Multi-frequency stochastic resonance detection in spectrum monitoring[J]. Foreign Electronic Measurement Technology, 2017, 36(9):9-12.
[18] PAGE E S. Continuous inspection schemes[J]. Biometrika, 1954, 41(1/2):100-115.
[19] LORDEN G. Procedures for reacting to a change in distribution[J]. Annals of Mathematical Statistics, 1971, 42(6):1897-1908.
[20] VINCENTPOOR H H, OLYMPIA H. Quickest Detection[M]. Cambridge:Cambridge University Press, 2009.
[21] WALD A. Sequential Analysis[M]. Newyork, Wiley: 1947.
[2] 银鸿, 文轩, 杨生胜, 等. 基于磁异常检测的磁性运动目标识别方法研究[J]. 仪器仪表学报, 2018, 39(3):258- 264.YIN H, WEN X, YANG SH SH, et al. Research on the moving ferromagnetic object recognition method based on magnetic anomaly detection[J]. Chinese Journal of Scientific Instrument, 2018, 39(3):258- 264.
[3] HOLMES J. Exploitation of a ship′s magnetic field signatures[J]. Synthesis Lectures on Computational Electromagnetics, 2006(1):1- 68.
[4] BIRSAN M. Measurement of the extremely low frequency (ELF) magnetic field emission from a ship[J]. Measurement Science & Technology, 2011, 22(8): 085709.
[5] SHEINKER A, GINZBURG B, SALOMONSKI N, et al. Magnetic anomaly detection using high-order crossing method[J]. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(4):1095-1103.
[6] GINZBURG B, FRUMKIS L, KAPLAN B Z. Processing of magnetic scalar gradiometer signals using orthonormalized functions[J]. Sensors & Actuators A Physical, 2002, 102(1- 2):67-75.
[7] 郭静波, 谭博, 蔡雄. 基于反相双峰指数模型的微弱瞬态极低频信号的估计与检测[J]. 仪器仪表学报, 2015, 36(8):1682-1691.GUO J B, TAN B, CAI X. Estimation and detection of the weak transient ELF signal based on the phase inverting double-peak exponential model[J]. Chinese Journal of Scientific Instrument, 2015, 36(8): 1682-1691.
[8] PIAO G Y, GUO J B, HU T H. A novel real-time detection of orthogonal transient weak ELF magnetic signals[C].Sensors Applications Symposium, 2017.
[9] HOLMES, J. Reduction of a ship′s magnetic field signatures[J]. Synthesis Lectures on Computational Electromagnetics, 2008, 1(3):1- 68.
[10] GINZBURG B, FRUMKIS L, KAPLAN B Z. An efficient method for processing scalar magnetic gradiometer signals[J]. Sensors & Actuators A Physical, 2004, 114(1):73-79.
[11] ABRAHAMSSON R, KAY S M, STOICA P. Estimation of the parameters of a bilinear model with applications to submarine detection and system identification[J]. Digital Signal Processing, 2007, 17(4):756-773.
[12] SHEINKER A, SALOMONSKI N, GINZBURG B, et al. Magnetkic anomaly detection using entropy filter[J]. Measurement Science & Technology, 2008, 19(4): 045205.
[13] 李国正, 张波. 基于Duffing振子检测频率未知微弱信号的新方法[J]. 仪器仪表学报, 2017, 38(1): 181-189.LI G ZH, ZHANG B. Novel method for detecting weak signal with unknown frequency based on duffing oscillator[J]. Chinese Journal of Scientific Instrument, 2017, 38(1):181-189.
[14] 张刚, 张义俊, 张天骐. α噪声下自适应非线性耦合双稳随机共振弱信号检测[J]. 电子测量与仪器学报, 2018, 32(5):142-150.ZHANG G, ZHANG Y J, ZHANG T Q. Adaptive coupled bistable stochastic resonance weak signal detection under α noise[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(5): 142-150.
[15] XING H Y, ZHANG Q, LU CH X. Adaptive stochastic resonance method for weak signal detection based on particle swarm optimization[J]. Instrumentation, 2015, 2(2):3-10.
[16] 王冀超, 杨正华, 岳亮, 等. Lyapunov指数算法在微弱信号检测中的应用[J]. 电子测量技术, 2017, 40(8):164-168.WANG Y CH, YANG ZH H, YUE L, et al. Application of Lyapunov exponent algorithm in the weak signal detection[J]. Electronic Measurement Technology, 2017, 40(8):164-168.
[17] 张伟达, 陈良, 梅芳, 等. 频谱监测中的多频随机共振检测[J]. 国外电子测量技术, 2017, 36(9):9-12.ZHANG W D, CHEN L, MEI F, et al. Multi-frequency stochastic resonance detection in spectrum monitoring[J]. Foreign Electronic Measurement Technology, 2017, 36(9):9-12.
[18] PAGE E S. Continuous inspection schemes[J]. Biometrika, 1954, 41(1/2):100-115.
[19] LORDEN G. Procedures for reacting to a change in distribution[J]. Annals of Mathematical Statistics, 1971, 42(6):1897-1908.
[20] VINCENTPOOR H H, OLYMPIA H. Quickest Detection[M]. Cambridge:Cambridge University Press, 2009.
[21] WALD A. Sequential Analysis[M]. Newyork, Wiley: 1947.