背景变化鲁棒的微弱瞬态信号检测

doi:10.19665/j.issn1001-2400.2019.04.022

摘要/Abstract

摘要：

在微弱瞬态信号检测中,由于信号完全淹没在背景和噪声中,因此当背景信号发生变化时,传统方法的检测性能会急剧降低。针对上述问题,提出一种基于核函数的对连续背景变化鲁棒的微弱瞬态信号检测方法。利用核函数将信号映射到一个高维空间来解决在原始空间中线性不可分的问题。根据信号和噪声特性对核函数加以约束,使检测方法在能检测出瞬态信号的同时还具有对连续背景变化鲁棒特性。将该方法应用于基于高帧频图像序列的微弱运动点目标检测中,实验证明该方法无论是在稳定背景情况下,还是背景连续变换情况下, 相比传统方法都有更好的检测结果,在背景连续变换的情况下优势更加明显。

关键词: 瞬态信号, 核函数, 背景变化鲁棒, 运动目标检测

Abstract:

In the detection of a weak transient signal, the signal is completely submerged in the noise, so the detection performance of traditional methods will be dramatically deteriorated when the background changes. To solve this problem, a new weak transient signal based on the kernel function is proposed, which is robust to the continuous background change. The signal is mapped to a higher dimensional feature space by use of the kernel function to solve the problem that there is linear inseparability in the original space. The kernel function is constrained according to characteristics of the signal and noise, so that the detection method is robust to the continuous background change while detecting transient signals effectively. The proposed method is applied to weak moving point target detection based on the high frame rate image sequence. The experiment shows that the proposed method can obtain better detection results in both simulation data and real world data, and that the proposed method is more superior when the background is changing continuously.

Key words: transient signal, kernel function, robustness of background change, moving target detection

中图分类号:

TN911.72

吴勇,郑伟,牛文龙,杨震. 背景变化鲁棒的微弱瞬态信号检测[J]. 西安电子科技大学学报, 2019, 46(4): 159-166.

WU Yong,ZHENG Wei,NIU Wenlong,YANG Zhen. Robust detection of weak transient signals with changing background[J]. Journal of Xidian University, 2019, 46(4): 159-166.

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参考文献 30

[1]	PAPADOPOULOS C K, IOANNIDIS G C, PSOMOPOULOS C S . Detection of Transient Signals Based on the Tricepstrum[J]. Digital Signal Processing: A Review Journal, 2018,78:232-249.
[2]	NIU W, ZHENG W, YANG Z , et al. Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR[J]. IEEE Geoscience and Remote Sensing Letters, 2018,15(2):217-221.
[3]	张维强, 宋国乡 . 基于一种新的阈值函数的小波域信号去噪[J]. 西安电子科技大学学报, 2004,31(2):296-302.
	ZHANGWeiqiang, SONG Guoxiang . Signal De-noising in Wavelet Domain Based on a New Kind of Thresholding Function[J]. Journal of Xidian University, 2004,31(2):296-302.
[4]	HOLOBAR A, ZAZULA D . Multichannel Blind Source Separation Using Convolution Kernel Compensation[J]. IEEE Transactions on Signal Processing, 2007,55(9):4487-4496.
[5]	ASCHWANDEN M J, DENNIS B R, BENZ A O. Logistic Avalanche Processes, Elementary Time Structures , Frequency Distributions in Solar Flares[J]. The Astrophysical Journal, 1998,497(1):972-993.
[6]	ZHANG C, LI Z, HU C , et al. An Optimized Ensemble Local Mean Decomposition Method for Fault Detection of Mechanical Components[J]. Measurement Science and Technology, 2017,28(3):035102.
[7]	ACHARYA U R, KOH J E W, HAGIWARA Y , et al . Automated Diagnosis of Focal Liver Lesions Using Bidirectional Empirical Mode Decomposition Features[J]. Computers in Biology and Medicine, 2018,94:11-18.
[8]	GHAEL S P, SAYEED A M, BARANIUK R G. Improved Wavelet Denoising via Empirical Wiener Filtering [C]//Proceedings of SPIE: 3169. Bellingham: SPIE, 1997: 389-399.
[9]	LI J, CHEN X, HE Z . Adaptive Stochastic Resonance Method for Impact Signal Detection Based on Sliding Window[J]. Mechanical Systems and Signal Processing, 2013,36(2):240-255.
[10]	YUVARAJ R, RAJENDRA ACHARYA U, HAGIWARA Y . A Novel Parkinson’s Disease Diagnosis Index Using Higher-order Spectra Features in EEG Signals[J]. Neural Computing and Applications, 2018,30(4):1225-1235.
[11]	WANG D, ZHANG N, LI Z , et al. Leveraging High Order Cumulants for Spectrum Sensing and Power Recognition in Cognitive Radio Networks[J]. IEEE Transactions on Wireless Communications, 2018,17(2):1298-1310.
[12]	PALAHINA E, PALAHIN V. Signal Detection in Additive-multiplicative Non-Gaussian Noise Using Higher Order Statistics [C]// Proceedings of the 2016 26th International Conference Radioelektronika. Piscataway: IEEE, 2016: 262-267.
[13]	HUANG D, YANG J, ZHOU D , et al. Recovering an Unknown Signal Completely Submerged in Strong Noise by a New Stochastic Resonance Method[J]. Communications in Nonlinear Science and Numerical Simulation, 2019,66:156-166.
[14]	CHENG F Q, SHI Y W. Random Phase Weak Sine Signal Detection Based on Autocorrelation and Chaos Theory[J]. Applied Mechanics and Materials, 2013, 397-400:2129-2133.
[15]	MCDONNELL M, NIGEL G S , CHARLES E M P, et al. Stochastic Resonance[M]. Cambridge, UK: Cambridge University Press, 2008.
[16]	LU S, HE Q, DAI D , et al. Periodic Fault Signal Enhancement in Rotating Machine Vibrations via Stochastic Resonance[J]. Journal of Vibration and Control, 2016,22(20):4227-4246.
[17]	MBA C U, MARCHESIELLO S, FASANA A. et al. Fault Detection in Gears Using Stochastic Resonance [C]//Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Cham: Springer, 2018: 55-70.
[18]	LI J, CHEN X, HE Z . Adaptive Stochastic Resonance Method for Impact Signal Detection Based on Sliding Window[J]. Mechanical Systems and Signal Processing, 2013,36(2):240-255.
[19]	JIAO S B, KOU J, ZHANG Q. Weak Impact Signal Detection Based on Adaptive Stochastic Resonance with Knowledge-based Particle Swarm Optimization [C]//Proceedings of the 2016 International Conference on Natural Computation. Washington: IEEE Computer Society, 2016: 308-313.
[20]	LI Y, YANG B J, BADAL J , et al. Chaotic System Detection of Weak Seismic Signals[J]. Geophysical Journal International, 2009,178(3):1493-1522.
[21]	HOU J, YAN X P, LI P , et al. Weak Wide-band Signal Detection Method Based on Small-scale Periodic State of Duffing Oscillator[J]. Chinese Physics B, 2018,27(3):030702.
[22]	姚慧, 孙颖, 张雪英 . 情感语音的非线性动力学特征[J]. 西安电子科技大学学报, 2016,43(5):167-172.
	YAO Hui, SUN Ying, ZHANG Xueying . Research on Nonlinear Dynamics Features of Emotional Speech[J]. Journal of Xidian University, 2016,43(5):167-172.
[23]	NIU W, WU Y, ZHENG W. et al. High Frame-Rate Based Moving Point Target Detection [C]//Proceedings of the 2018 International Geoscience and Remote Sensing Symposium. Piscataway: IEEE, 2018: 3639-3642.
[24]	SCHöLKOPF B. Statistical Learning and Kernel Methods [C]// Data Fusion and Perception. Vienna: Springer, 2001: 111-120.
[25]	赵峰, 张军英, 刘敬 . 核最优变换与聚类中心的算法[J]. 西安电子科技大学学报, 2009,36(1):126-133.
	ZHAO Feng, ZHANG Junying, LIU Jing . KernelOptimal Transformation and Cluster Centers Algorithm[J]. Journal of Xidian University, 2009,36(1):126-133.
[26]	CAMPBELL C . An Introduction to Kernel Methods[J]. IEEE Transactions on Neural Networks, 2008,12(2):181.
[27]	XU Y, LUO M, LI T , et al. ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold[J]. Sensors, 2017,17(12):2754.
[28]	JIAO S B, KOU J, ZHANG Q . Weak Impact Signal Detection Based on Adaptive Stochastic Resonance with Knowledge-based Particle Swarm Optimization [C]//Proceedings of the 2016 International Conference on Natural Computation. Washington: IEEE Computer Society, 2016: 308-313.
[29]	FARAJZADEH M, MAHMOODI A, ARVAN M R. Detection of Small Target Based on Morphological Filters [C]//Proceedings of the 2012 20th Iranian Conference on Electrical Engineering. Washington: IEEE Computer Society, 2012: 1097-1101.
[30]	LUO Q . Early Warning Radar Clutter Suppression and Dim Moving Target Detection Algorithm[J]. Journal of Beijing University of Technology, 2012,38(3):369-373.

方法	虚警率/%	探测率/%
高阶统计量	1.85	69.72
小波	17.3	49.54
Top-Hat	3.15	18.35
背景减法	8.24	43.12
文中方法	0.2	79.82