航空磁探中磁异常信号检测器性能仿真分析
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摘要
航空磁异常探测中目标信号受到飞行器机动平台的干扰和强背景噪声的影响,使得传统的基于正交基函数分解的检测器虚警率过高不能满足实际应用需求。针对航空磁探中的弱信噪比检测问题,提出了一种基于小波分解变换的能量积累检测算法用于磁异常信号检测以提高检测性能。首先对含噪的航空磁异常信号在输入端进行小波去噪预处理,对提纯后的航空磁异常信号进行流式Mallat小波分解,对分解在各个尺度上比例成分不同的粗分辨逼近能量和代表信号边缘的细节信息能量使用多尺度能量积累器对小波信号能量进行积累,并将积累器得到的总能量信号送入判决器进行判决。采用复杂磁环境下的实测噪声数据对积累器进行性能分析,并同传统的OBF检测器以及航空动目标OBF检测器进行比较。结果表明,小波域多尺度能量积累检测器可以在弱信噪比条件下准确地检测目标。
The target signal in airborne magnetic anomaly detection is severely affected by the interference of the aircraft maneuvering platform and strong background noise. It leads to the large false alarm rate of the traditional detector based on the decomposition of orthogonal basis functions( OBF). Thus the detector cannot be applicable to practical requirement. As to the problem that the low signal-to-noise ratio( SNR) detection for airborne magnetic anomaly,an energy accumulation detection algorithm based on wavelet decomposition transform is proposed for the airborne magnetic anomaly signal detection to improve the detection capability. Firstly,the airborne magnetic anomaly signal corrupted by the noise is processed by the wavelet transform denoising at the input end. The purified airborne magnetic anomaly signal is decomposed by flow-type Mallat wavelet decomposition. Then the energy of coarse-resolution approximation and the energy of detailed information are accumulated at each scale by the multi-scale energy cumulant. The detailed information presents the edge of the signal. The total energy signal from the wavelet domain is sent to the decision device for signal detection. The performance of the cumulant is analyzed by using the measured noise data in the complex magnetic environment and compared with the OBF detector and airborne moving target OBF detector. The results show that the multi-scale energy cumulant in wavelet domains can detect targets accurately under low SNR conditions.
The target signal in airborne magnetic anomaly detection is severely affected by the interference of the aircraft maneuvering platform and strong background noise. It leads to the large false alarm rate of the traditional detector based on the decomposition of orthogonal basis functions( OBF). Thus the detector cannot be applicable to practical requirement. As to the problem that the low signal-to-noise ratio( SNR) detection for airborne magnetic anomaly,an energy accumulation detection algorithm based on wavelet decomposition transform is proposed for the airborne magnetic anomaly signal detection to improve the detection capability. Firstly,the airborne magnetic anomaly signal corrupted by the noise is processed by the wavelet transform denoising at the input end. The purified airborne magnetic anomaly signal is decomposed by flow-type Mallat wavelet decomposition. Then the energy of coarse-resolution approximation and the energy of detailed information are accumulated at each scale by the multi-scale energy cumulant. The detailed information presents the edge of the signal. The total energy signal from the wavelet domain is sent to the decision device for signal detection. The performance of the cumulant is analyzed by using the measured noise data in the complex magnetic environment and compared with the OBF detector and airborne moving target OBF detector. The results show that the multi-scale energy cumulant in wavelet domains can detect targets accurately under low SNR conditions.
引文
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[2] F Robert,et al. Magnetic signature attenuation of an unmanned aircraft system for aeromagnetic survey[J]. IEEE/ASME Transactions on Mechatronics,2014,19(4):1436-1446.
[3] Z Liu,H Pang,M Pan. Calibration and compensation of geomagnetic vector measurement system and improvement of magnetic anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing Letters,2016,13(3),447-451.
[4] Dames P M,Schwager M,Schwager D. Active magnetic anomaly detection using multiple micro aerial vehicles[J]. IEEE Robotics and Automation Letters,2016,1(1),153-160.
[5] Y Liu,Y Zhang,H Yi. The new magnetic survey method for underwater pipeline detection[J]. Applied Mechanics and Materials,2013,239(2):338-343.
[6] L Song,et al. Transient electromagnetic scattering of a metallic object buried in underwater sediments[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(2):1091-1102.
[7] A Sheinker,L Frumkis,B Ginzburg. Magnetic anomaly detection using a three-axis magnetometer[J]. IEEE Transactions on Magnetics,2009,45(1):160-167.
[8] A Sheinker,N Salomonskil,B Ginzburg. Magnetic anomaly detection using entropy filter[J]. Measurement Science and Technology,2008,19(5):1-5.
[9] B Ginzburg,L Frumkisb,B Z Kaplan. An efficient method for processing scalar magnetic gradiometer signals[J]. Sensors and Actuators,2004,114(1):73-79.
[10] B Ginzburg,L Frumkis,B Z Kaplan. Processing of magnetic scalar gradiometer signals using orthonormalized functions[J]. Sensors and Actuators,2002,102(1):67-75.
[11] L Frumkisa,B Ginzburg,N Salomonski. Optimization of scalar magnetic gradiometer signal processing[J]. Sensors and Actuators,2005,121(1):88-94.
[12] A Sheinker,A Shkalim,N Salomonski. Processing of a scalar magnetometer signal contaminated by 1/fαnoise[J]. Sensors and Actuators,2007,138(1):105-111.
[13]李婧,田铮. SAR图像多尺度配准的小波域等周割方法[J].计算机仿真,2011,28(01):246-249.
[14]刘斌,高强.基于双正交小波变换的矩不变量[J].电子学报,2017,45(4):826-831.
[15]田永刚,董毅.小波多尺度分割算法在细胞图像上的应用[J].计算机仿真,2011,28(11):255-257.
[16]王唯嘉,肖明清,张磊,陈茂才.多小波包框架下EMD-CIIT去噪方法研究[J].计算机仿真,2015,32(12):386-391.
[17]周小勇,叶银忠.基于小波多分辨率系数模值的并发故障检测方法[J].华东理工大学学报(自然科学版),2006,32(7):832-835.
[18]任亚飞,柯熙政.多尺度小波分解融合在微机电陀螺数据处理中的应用[J].应用科学学报,2010,28(4):394-398.
[19]李合生,等.基于多尺度小波能量积累的雷达回波信号检测方法[J].系统工程与电子技术,2000,22(11):46-49.
[20]李合生,毛剑琴,张富堂.小波能量积累器在信号处理中的应用[J].强激光与离子束,2000,12(5):610-614.