Seismic signal recognition using improved BP neural network and combined feature extraction method

Abstract

Seismic signal is generally employed in moving target monitoring due to its robust characteristic. A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network. For analyzing the seismic signal of the moving objects, the seismic signal of person and vehicle was acquisitioned from the seismic sensor, and then feature vectors were extracted with combined methods after filter processing. Finally, these features were put into the improved BP neural network designed for effective signal classification. Compared with previous ways, it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results. It also shows the effectiveness of the improved BP neural network.

Keywords

seismic signal / feature extraction / BP neural network / signal identification

References

[1] RichardsP G, WaldhauserF, SchaffD, KimW Y. The applicability of modern methods of earthquake location [J]. Pure Appl Geophys, 2006, 163(2/3): 351-372

[2] GibbonsS J, RingdalF. Seismic monitoring of the north Korea nuclear test site using a multichannel correlation detector [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1897-1909

[3] RichardsP G, KimW Y. Seismic signature [J]. Nat Phys, 2007, 3(1): 4-6

[4] Van HerwijnenA, SchweizeraJ. Monitoring avalanche activity using a seismic sensor [J]. Cold Regions Science and Technology, 2011, 69(2/3): 165-176

[5] TianY-x, QiH-rong. Target detection and classification using seismic signal processing in unattended ground sensor systems [C]. International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2002. Orlando, US, 20024172-4176

[6] NieW-r, ZhuJ-nan. Eigenvector analysis in time and frequency of seismic signals of moving target [J]. Journal of Nanjing University of Science and Technology, 2002, 26(5): 478-485

[7] LiuY, ZhaoH-m, ZhangY, DuanY-jie. Research on technology of seismic signal acquisition and analysis [J]. Water Resources and Power, 2010, 28(5): 130-131

[8] LiuG-chang. Time-frequency analysis of seismic data using local attributes [J]. Geophysics, 2011, 76(6): 23-34

[9] TianY-xinTarget detection and classification using seismic signal processing in unattended ground sensor system [D], 2001, Knoxville, The University of Tennessee

[10] YangL-j, HaoWei. Multi-sensor target recognition method based on FMM artificial neural network and DS evidence reasoning [J]. Journal of Detection & Control, 2006, 28(1): 40-43

[11] AhnJ J, SonI H, OhK J, KimT Y, SongG M. Lag-l forecasting and machine-learning algorithms [J]. Expert Systems, 2011, 28(3): 269-282

[12] WuQ, LiuW-y, YangY-han. Time series online prediction algorithm based on least squares support vector machine [J]. Journal of Central South University of Technology, 2007, 14(3): 442-446

[13] BishopC MPattern recognition and machine learning (Information Science and Statistic) [EB/OL], 2006

[14] OquendoM A, Baca-GarciaE, Artés-RodríguezA, Perez-CruzF, GalfalvyH C, Blasco-FontecillaH, MadiganD, DuanN. Machine learning and data mining: strategies for hypothesis generation [J]. Molecular Psychiatry, 2012, 17: 956-959

[15] KohonenT. An introduction to neural computing [J]. Neural Networks, 1988, 11(1): 3-16

[16] LiY-w, PengJ-h, LiangG-a, LiW, ZhangS-min. TI Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology [J]. Journal of Central South University of Technology, 2011, 18: 1441-1447

[17] GraupeD, ClineW. Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes [J]. IEEE Trans Syst Man Cybern, 1975252-259

[18] SangA, LiS Q. A predictability analysis of network traffic [J]. Computer Networks, 2002, 39(4): 329-345

[19] IzeboudjenN, BouridaneA, FarahA, BessalahH. Application of design reuse to artificial neural networks: Case study of the back propagation algorithm [J]. Neural Computing & Applications, 2012, 21(7): 1531-1544

[20] KulkarniP. Introduction to reinforcement and systemic machine learning [M]. Reinforcement and Systemic Machine Learning for Decision Making, 2012, Hoboken, Wiley-IEEE Press eBook Chapters: 1-21

[21] ZhangZ-m, LiK-j, LiG-t, ShangY-ling. Feature extraction of acoustic/seismic signal of target [J]. Journal of Beijing Institute of Technology: English Edition, 2000, 9(3): 312-317

[22] GeaM, WangbH, SchisslercA, RamanidR. Measuring pillar width in trona mines using a body wave based in-seam seismic technique [J]. International Journal of Rock Mechanics and Mining Sciences, 2012, 5: 10-17

[23] YuanZ, LiT. Absorption and attenuation of stratum and recording performance of seismic instrument [J]. Geophysical Prospecting for Petroleum, 2004, 43(5): 450-452

[24] ElgortK, KomaromyS, MaddenJ, TaylorA. US army employment of unattended ground sensors [C]. Proceedings of the 2005 System and Information Engineering Design Symposium. Charlottesville, US, 2005263-269

[25] MansourA, JuttenC. What should we say about the kurtosis?. [J]. IEEE Signal Processing Letters, 1999, 6: 321-322

[26] KirkR E. STATISTICS-Measures of dispersion, skewness and kurtosis [M]. Chennai: Tamil Nadu Textbook Corporation, 200589-115

[27] ConradsenI, BeniczkyS, HoppeK, WolfP, SorensenH. Automated algorithm for generalized tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate [J]. IEEE Transactions on Biomedical Engineering, 2012, 59(2): 579-585

[28] LauY-k, ChanC-ki. Speech recognition based on zero crossing rate and energy [J]. Acoustics, Speech and Signal Processing, 1985, 33: 320-323

[29] SaeedS, AliM. Privacy-preserving back-propagation and extreme learning machine algorithms [J]. Data & Knowledge Engineering, 2012, 79/80: 40-61

[30] QueR-y, ZhuRong. Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and bp neural network identification [J]. Sensors, 2012, 12(8): 10920-10929

[31] HeY-jieThe feasibility analysis of military vehicles accident prediction based BP neural network [D], 2011, Jinan, Shandong University

[32] ChengYueStudy on improved algorithm and application of BP neural network [D], 2011, Chongqing, Chongqing University

[33] JiangJ-s, JiHua. Model of online grain moisture test system based on improved BP neural network [C]. 2009 Second International Conference on Intelligent Computation Technology and Automation. Changsha, China, 200979-82

[34] TanJ, ChenX-s, DuM, ZhuKai. A novel internet traffic identification approach using wavelet packet decomposition and neural network [J]. Journal of Central South University, 2012, 19(8): 2218-2230

[35] JingG-l, DuW-t, GuoY-ying. Studies on prediction of separation percent in electrodialysis process via BP neural networks and improved BP algorithms [J]. Desalination, 2012, 291: 78-93

[36] BlancoA, DelgadoM, PegalajarM C. A genetic algorithm to obtain the optimal recurrent neural network [J]. International Journal of Approximate Reasoning, 2000, 23: 67-83

[37] CaoC, PengZ-q, LiuQ-s, LiBo. Personnel and vehicle signal sensing based on seismic detection [C]. ICICIP 2012. Dalian, China, 2012482-487