Abstract
This chapter investigates a classification problem for timely and reliable identification of radar signal emitters by implementing and following a neural network (NN) based approach. A large data set of intercepted generic radar signals, containing records of their pulse train characteristics (such as operational frequencies, modulation types, pulse repetition intervals, scanning period, etc.), is used for this research. Due to the nature of the available signals, the data entries consist of a mixture of continuous, discrete and categorical data, with a considerable number of records containing missing values. To solve the classification problem, two separate approaches are investigated, implemented, tested and validated on a number of case studies. In the first approach, a listwise deletion is used to clean the data of samples containing missing values and then feed-forward neural networks are employed for the classification task. In the second one, a multiple imputation (MI) model-based method for dealing with missing data (by producing confidence intervals for unbiased estimates without loss of statistical power, i.e. by using all the available samples) is investigated. Afterwards, a feedforward backpropagation neural network is trained to solve the signal classification problem. Each of the approaches is tested and validated on a number of case studies and the results are evaluated and critically compared. The rest of the chapter is organised as follows: the next section (Introduction and Background) presents a review of related literature and relevant background knowledge on the investigated topic. In Sect. 2 (Data Analysis), a broader formulation of the problem is provided and a deeper analysis of the available data set is made. Different statistical transformation techniques are discussed and a multiple imputation method for dealing with missing data is introduced in Sect. 3 (Data Pre-Processing). Several NN topologies, training parameters, input and output coding, and data transformation techniques for facilitating the learning process are tested and evaluated on a set of case studies in Sect. 4 (Results and Discussion). Finally, Sect. 5 (Conclusion) summarises the results and provides ideas for further extension of this research.
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References
Richards, M.A.: Fundamentals of Radar Signal Processing. Tata McGraw-Hill Education, New Delhi (2005)
Dickmann, J., Appenrodt N., Brenk, C.: Making Bertha see. IEEE Spectrum, 41–45 (2014)
Mure-Dubois, J., Vincent, F., Bonacci, D.: Sonar and radar SAR processing for parking lot detection. In: Proceedings of the IEEE International Radar Symposium (IRS), pp. 471–476 (2011)
Skolnik, M.I.: Introduction to Radar Systems. McGraw Hill, Boston (2001)
Lazarus, M.: Radar everywhere. IEEE Spectr. 52(2), 52–59 (2015)
Cumming, I.G., Wong, F.H.: Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation. Artech House, Norwood (2005)
Krieger, G., Younis, M., Gebert, N., Huber, S., Bordoni, F., Patyuchenko, A., Moreira, A.: Advanced concepts for high-resolution wide-swath SAR imaging. In: 8th European Conference on Synthetic Aperture Radar (EUSAR), pp. 1–4. VDE (2010)
Zhai, S., Jiang, T.: A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine. Neurocomput. 149(1), 573–584 (2015)
Brunner, D., Lemoine, G., Bruzzone, L.: Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Trans. Geosci. Remote Sens. 48(5), 2403–2420 (2010)
Arcone, S.A., Spikes, V.B., Hamilton, G.S.: Stratigraphic variation within polar firn caused by differential accumulation and ice flow: interpretation of a 400 MHz short-pulse radar profile from West Antarctica. J. Glaciol. 51(174), 407–422 (2005)
Storvold, R., Malnes, E., Larsen, Y., Høgda, K., Hamran, S., Mueller, K., Langley, K.: SAR remote sensing of snow parameters in Norwegian areas—Current status and future perspective. J. Electromagn. Waves Appl. 20(13), 1751–1759 (2006)
Evans, D.L., Alpers, W., Cazenave, A., Elachi, C., Farr, T., Glackin, D., Holt, B., Jones, L., Liu, W.T., McCandless, W.: Seasat—a 25-year legacy of success. Remote Sens. Environ. 94(3), 384–404 (2005)
Secades, C., O’Connor, B., Brown, C., Walpole, M.: Earth observation for biodiversity monitoring: a review of current approaches and future opportunities for tracking progress towards the Aichi Biodiversity Targets, CBD Technical Series 72, (2014)
Persico, R.: Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing. Wiley, New York (2014)
Goodman, D., Piro, S.: GPR Remote Sensing in Archaeology. Springer, New York (2013)
Sambuelli, L., Bohm, G., Capizzi, P., Cardarelli, E., Cosentino, P.: Comparison between GPR measurements and ultrasonic tomography with different inversion algorithms: an application to the base of an ancient Egyptian sculpture. J. Geophys. Eng. 8(3), 106–116 (2011)
Francke, J.: Applications of GPR in mineral resource evaluations. In: Proceedings of the 13th IEEE International Conference on Ground Penetrating Radar (GPR), pp. 1–5 (2010)
Lambot, S., Slob, E.C., van den Bosch, I., Stockbroeckx, B., Vanclooster, M.: Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties. IEEE Trans. Geosci. Remote Sens. 42(11), 2555–2568 (2004)
Soldovieri, F., Prisco, G., Persico, R.: A strategy for the determination of the dielectric permittivity of a lossy soil exploiting GPR surface measurements and a cooperative target. J. Appl. Geophys. 67(4), 288–295 (2009)
Peabody, J.E., Charvat, G.L., Goodwin, J., Tobias, M.: Through-wall imaging radar. Lincoln Lab. J. 19(1), 62–72 (2012)
Pettinelli, E., Di Matteo, A., Mattei, E., Crocco, L., Soldovieri, F., Redman, J.D., Annan, A.P.: GPR response from buried pipes: Measurement on field site and tomographic reconstructions. IEEE Trans. Geosci. Remote Sens. 47(8), 2639–2645 (2009)
Slob, E., Sato, M., Olhoeft, G.: Surface and borehole ground-penetrating-radar developments. Geophysics 75(5), 75A103–175A120 (2010)
IEEE Standard Letter Designations for Radar-Frequency Bands. Standard 521–2002 (2003). doi:10.1109/IEEESTD.2003.94224
Capraro, G.T., Farina, A., Griffiths, H., Wicks, M.C.: Knowledge-based radar signal and data processing: a tutorial review. IEEE Sig. Process. Mag. 23(1), 18–29 (2006)
Galejs, R.J.: Volume surveillance radar frequency selection. In: Proceedings of the IEEE International Radar Conference, pp. 187–192 (2000)
Johnsen, T., Olsen, K.E.: Bi-and multistatic radar. DTIC Document (2006)
Schleher, D.C.: Electronic warfare in the information age. Artech House, Boston, London (1999)
Sciortino, J.C.: Autonomous ESM systems. Naval Eng. J. 109(6), 73–84 (1997)
Wang, Z., Zhang, D., Bi, D., Wang, S.: Multiple-parameter radar signal sorting using support vector clustering and similitude entropy index. Circ. Syst. Sig. Process. 33(6), 1985–1996 (2014)
Granger, E., Rubin, M.A., Grossberg, S., Lavoie, P.: A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. Neural Netw. 14(3), 325–344 (2001)
D’Agostino, S., Foglia, G., Pistoia, D.: Specific emitter identification: Analysis on real radar signal data. In: Proceedings of the IEEE European Radar Conference (EuRAD), pp. 242–245 (2009)
Feng, B., Lin, Y.: Radar signal recognition based on manifold learning method. Int. J. Control Autom. 7(12), 399–440 (2014)
Li, J., Ying, Y.: Radar signal recognition algorithm based on entropy theory. In: Proceedings of the 2nd IEEE International Conference on Systems and Informatics (ICSAI 2014), pp. 718–723 (2014)
Zhang, G., Hu, L., Jin, W.: A novel approach for radar emitter signal recognition. In: Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems, pp. 817–820 (2004)
Lunden, J., Koivunen, V.: Automatic radar waveform recognition. IEEE J. Sel. Top. Sig. Process. 1(1), 124–136 (2007)
Hassan, H.: A new algorithm for radar emitter recognition. In: Proceedings of the 3rd IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 1097–1101 (2003)
Liu, H.-J., Liu, Z., Jiang, W.-L., Zhou, Y.-Y.: Approach based on combination of vector neural networks for emitter identification. IET Sig. Proc. 4(2), 137–148 (2010)
Ting, C., Wei, G., Bing, S.: A new radar emitter recognition method based on pulse sample figure. In: Proceedings of the 8th IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1902–1905 (2011)
Wang, L., Ji, H., Shi, Y.: Feature extraction and optimization of representative-slice in ambiguity function for moving radar emitter recognition. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2246–2249 (2010)
Zeng, Y., Li, O.: A new algorithm for signal emitter recognition. In: Proceedings of the IEEE International Conference on Image Analysis and Signal Processing (IASP), pp. 446–449 (2010)
Pang, J., Lin, Y., Xu, X.: The improved radial source recognition algorithm based on fractal theory and neural network theory. Int. J. Hybrid Inf. Technol. 7(2), 397–402 (2014)
Li, J., Ying, Y.: Radar signal recognition algorithm based on entropy theory. In: Proceedings of the 2nd IEEE International Conference on Systems and Informatics (ICSAI), pp. 718–723 (2014)
Ripley, B.D.: Pattern Recognition and Neural Networks (2008)
Kamgar-Parsi, B., Kamgar-Parsi, B., Sciortino Jr., J.C.: Automatic Data Sorting Using Neural Network Techniques. DTIC Document (1996)
Pape, D.R., Anderson, J.A., Carter, J.A., Wasilousky, P.A.: Advanced signal waveform classifier. In: Proceedings of the 8th Optical Technology for Microwave Applications, pp. 162–169 (1997)
Ince, T.: Polarimetric SAR image classification using a radial basis function neural network. PIERS 41(4), 636–646 (2010)
Vicen-Bueno, R., Carrasco-Álvarez, R., Rosa-Zurera, M., Nieto-Borge, J.C., Jarabo-Amores, M.-P.: Artificial neural network-based clutter reduction systems for ship size estimation in maritime radars. EURASIP J. Adv. Sig. Process. 9(1), 1–15 (2010)
Anjaneyulu, L., Sarma, N., Murthy, N.: Identification of LPI radar signals by higher order spectra and neural network techniques. Int. J. Inf. Commun. Technol. 2(1), 142–155 (2009)
Ibrahim, N., Abdullah, R.R., Saripan, M.: Artificial neural network approach in radar target classification. J. Comput. Sci. 5(1), 23 (2009)
Yin, Z., Yang, W., Yang, Z., Zuo, L., Gao, H.: A study on radar emitter recognition based on SPDS neural network. Inf. Technol. J. 10(4), 883–888 (2011)
Zhang, Z.-C., Guan, X., He, Y.: Study on radar emitter recognition signal based on rough sets and RBF neural network. In: Proceedings of the IEEE International Conference on Machine Learning and Cybernetics, pp. 1225–1230 (2009)
Peipei, D., Hui, L.: The radar target recognition research based on improved neural network algorithm. In: Proceedings of the Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), pp. 1074–1077 (2014)
Chen, S., Cowan, C.F., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2(2), 302–309 (1991)
Shieh, C.-S., Lin, C.-T.: A vector neural network for emitter identification. IEEE Trans. Antennas Propag. 50(8), 1120–1127 (2002)
Lin, C.-M., Chen, Y.-M., Hsueh, C.-S.: A self-organizing interval type-2 fuzzy neural network for radar emitter identification. Int. J. Fuzzy Syst. 16(1), 20 (2014)
Liu, J., Lee, J.P., Li, L., Luo, Z.-Q., Wong, K.M.: Online clustering algorithms for radar emitter classification. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1185–1196 (2005)
Li, L., Ji, H., Wang, L.: Specific radar emitter recognition based on wavelet packet transform and probabilistic SVM. In: Proceedings of the IEEE International Conference on Information and Automation (ICIA), pp. 1308–1313 (2009)
Ting, C., Jingqing, L., Bing, S.: Research on rough set-neural network and its application in radar signal recognition. In: Proceedings of the 8th International Conference on Electronic Measurement and Instruments (ICEMI), pp. 764–768 (2007)
Azimi-Sadjadi, M.R., Yao, D., Huang, Q., Dobeck, G.J.: Underwater target classification using wavelet packets and neural networks. IEEE Trans. Neural Netw. 11(3), 784–794 (2000)
Lee, S.-J., Choi, I.-S., Cho, B., Rothwell, E.J., Temme, A.K.: Performance enhancement of target recognition using feature vector fusion of monostatic and bistatic radar. Prog. Electromagnet. Res. 144, 291–302 (2014)
Baraldi, A.N., Enders, C.K.: An introduction to modern missing data analyses. J. Sch. Psychol. 48(1), 5–37 (2010)
Enders, C.K.: Applied Missing Data Analysis. Guilford Press, New York (2010)
Graham, J.W.: Missing data analysis: making it work in the real world. Annu. Rev. Psychol. 60(1), 549–576 (2009)
Horton, N.J., Lipsitz, S.R.: Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am. Stat. 55(3), 244–254 (2001)
Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley, New York (2002)
Osborne, J.W., Overbay, A.: Best practices in data cleaning. Sage, Thousand Oaks (2012)
Rahm, E., Do, H.H.: Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)
Verboven, S., Branden, K.V., Goos, P.: Sequential imputation for missing values. Comput. Biol. Chem. 31(5), 320–327 (2007)
Morring Jr., F., Perrett, B.: L-band SAR Satellite May Help JAXA’s New Military Job, Aviation Week & Space Technology (2014)
O’Reilly, D., Bowring, N., Harmer, S.: Signal processing techniques for concealed weapon detection by use of neural networks. In: IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI), pp. 1–4 (2012)
Lee, J.-H., Choi, I.-S., Kim, H.-T.: Natural frequency-based neural network approach to radar target recognition. IEEE Trans. Sig. Process. 51(12), 3191–3197 (2003)
Khairnar, D., Merchant, S., Desai, U.: Radar signal detection in non-gaussian noise using RBF neural network. J. Comput. 3(1), 32–39 (2008)
Briones, J., Flores, B., Cruz-Cano, R.: Multi-mode radar target detection and recognition using neural networks. Int. J. Adv. Rob. Syst. 9(177), 1–9 (2012)
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Jordanov, I., Petrov, N. (2016). Intelligent Radar Signal Recognition and Classification. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_5
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