A three-way incremental-learning algorithm for radar emitter identification

doi:10.1007/s11704-015-4457-7

Front. Comput. Sci.

2016, Vol. 10

Issue (4) : 673-688 https://doi.org/10.1007/s11704-015-4457-7

RESEARCH ARTICLE

A three-way incremental-learning algorithm for radar emitter identification

Xin XU^1,^*(

),Wei WANG²,Jianhong WANG¹

¹. Science and Technology on Information System Engineering Laboratory, Nanjing Research Institute of Electronic Engineering (NRIEE), Nanjing 210007, China
². State Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing 210093, China

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Abstract

Radar emitter identification has been recognized as an indispensable task for electronic intelligence system. With the increasingly accumulated radar emitter intelligence and information, one key issue is to rebuild the radar emitter classifier efficiently with the newly-arrived information. Although existing incremental learning algorithms are superior in saving significant computational cost by incremental learning on continuously increasing training samples, they are not adaptable enough yet when emitter types, features and samples are increasing dramatically. For instance, the intra-pulse characters of emitter signals could be further extracted and thus expand the feature dimension. The same goes for the radar emitter type dimension when samples from new radar emitter types are gathered. In addition, existing incremental classifiers are still problematic in terms of computational cost, sensitivity to data input order, and difficulty in multiemitter type identification. To address the above problems, we bring forward a three-way incremental learning algorithm (TILA) for radar emitter identification which is adaptable for the increase in emitter features, types and samples.

Keywords radar emitter identification incremental learning classification data mining

Corresponding Author(s): Xin XU

Just Accepted Date: 31 August 2015 Online First Date: 06 April 2016 Issue Date: 06 July 2016

Cite this article:

Xin XU,Wei WANG,Jianhong WANG. A three-way incremental-learning algorithm for radar emitter identification[J]. Front. Comput. Sci., 2016, 10(4): 673-688.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4457-7
https://academic.hep.com.cn/fcs/EN/Y2016/V10/I4/673

1	Zhou Z H, Chen Z Q. Hybrid decision tree. Knowledge-Based Systems, 2002, 15(8): 515–528 https://doi.org/10.1016/S0950-7051(02)00038-2
2	Domingos P, Hulten G. A general framework for mining massive data streams. Journal of Computational and Graphical Statistics, 2003, 12(14): 945–949 https://doi.org/10.1198/1061860032544
3	Masud M M, Chen Q, Khan L, Aggarwal C C, Gao J, Han J, Srivastava A, Oza N C. Classification and adaptive novel class detection of feature-evolving data streams. IEEE Transaction on Knowledge and Data Engineering, 2013, 25(7): 1484–1497 https://doi.org/10.1109/TKDE.2012.109
4	Bordes A, Bottou L. The huller: a simple and efficient online SVM. Lecture Notes in Computer Science, 2005, 3720: 505–512 https://doi.org/10.1007/11564096_48
5	Barak O, Rigotti M. A simple derivation of a bound on the rerceptron margin using singular value decomposition. Neural Computation, 2011, 23(8): 1935–1943 https://doi.org/10.1162/NECO_a_00152
6	Zhang T. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the 21st International Conferenc on Machine Learning. 2004, 919–926 https://doi.org/10.1145/1015330.1015332
7	Shalev-Shwartz S, Singer Y, Srebro N, Cotter A. Pegasos: primal estimated sub-gradient solver for SVM. Mathematical Programming, 2011, 127(1): 3–30 https://doi.org/10.1007/s10107-010-0420-4
8	Li Y, Long P M. The relaxed online maximum margin algorithm. Machine Learning, 2002, 46: 1–3 https://doi.org/10.1023/A:1012435301888
9	Hulten G, Spencer L, Domingos P. Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2001, 97–106 https://doi.org/10.1145/502512.502529
10	Carpenter G A, Grossberg S, Markuzon N, Reynolds J H, Rosen D B. Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 1991, 3(5): 698–713 https://doi.org/10.1109/72.159059
11	Polikar R, Udpa L, Udpa S S, Honavar V. Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 2001, 31(4): 497–508 https://doi.org/10.1109/5326.983933
12	Sheng W, Banta L E. Parameter incremental learning algorithm for neural networks. IEEE Transactions on Neural Networks, 2006, 17(6): 1424–1438 https://doi.org/10.1109/TNN.2006.880581
13	Katakis I, Tsoumakas G, Vlahavas I. Dynamic feature space and incremental feature selection for the classification of textual data streams. Knowledge Discovery from Data Streams, 2006: 107–116
14	Wenerstrom B, Giraud-Carrier C. Temporal data mining in dynamic feature spaces. In: Proceedings of the 13th IEEE International Conference on Data Mining. 2006, 1141–1145 https://doi.org/10.1109/icdm.2006.157
15	Masud M M, Gao J, Khan L, Han J, Thuraisingham B. Integrating novel class detection with classification for concept-drifting data streams. Lecture Notes in Computer Science, 2009, 5782: 79–94 https://doi.org/10.1007/978-3-642-04174-7_6
16	Masud M M, Chen Q, Jing G, Latifur K, Jiawei H, Bhavani T. Classification and novel class detection of data streams in a dynamic feature space. Machine Learning and Knowledge Discovery in Databases, 2010, 6322: 337–352 https://doi.org/10.1007/978-3-642-15883-4_22
17	Spinosa E J, de Leon F, de Carvalho A P, Gama J. Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks. In: Proceedings of the 2008 ACM Symposium on Applied Computing. 2008: 976–980 https://doi.org/10.1145/1363686.1363912
18	Sminu N R, Jemimah S. Feature based data stream classification (FBDC) and novel class detection. International Journal of Engineering Research and Applications (IJERA), 2014, 25(7): 28–32
19	Zhu B, Jin d W. Radar emitter signal recognition based on EMD and neural network. Journal of Computers, 2012, 7(6): 1413–1420
20	Yang Z, Wu Z, Yin Z, Quan T, Sun H. Hybrid radar emitter recognition based on rough k-means classifier and relevance vector machine. Sensors, 2013, 13(1): 848–864 https://doi.org/10.3390/s130100848
21	Liu H J, Liu Z, Jiang W L, Zhou Y Y. Incremental learning approach based on vector neural network for emitter identification. IET Signal Processing, 2010, 4(1): 45–54 https://doi.org/10.1049/iet-spr.2008.0240
22	Kauppia J P, Martikainenb K, Ruotsalainena U. Hierarchical classifica tion of dynamically varying radar pulse repetition interval modulation patterns. Neural Networks, 2010, 23(10): 1226–1237 https://doi.org/10.1016/j.neunet.2010.06.008
23	Xu X, Wang W. An incremental gray relational analysis algorithm for multiclass classification and outlier detection. International Journal of Pattern Recognition and Artificial Intelligence, 2012, 26(6): 1250011 https://doi.org/10.1142/S0218001412500115
24	Montazer G A, Khoshniat H, Fathi V. Improvement of RBF neural networks using Fuzzy-OSD algorithm in an online radar pulse classification system. Applied Soft Computing, 2013, 13(9): 3831–3838 https://doi.org/10.1016/j.asoc.2013.04.021
25	Tang K, Lin M, Minku F L, Yao X. Selective negative correlation learning approach to incremental learning. Neurocomputing, 2009, 72(13–15): 2796–2805 https://doi.org/10.1016/j.neucom.2008.09.022
26	Sudo A, Sato A, Hasegawa O. Associative memory for online learning in noisy environments using self-organizing incremental neural network. IEEE Transactions on Neural Networks, 2009, 20(6): 964–972 https://doi.org/10.1109/TNN.2009.2014374
27	Chao S, Wong F. An incremental decision tree learning methodology regarding attributes in medical data mining. In: Proceedings of IEEE International Conference on Machine Learning and Cybernetics. 2009, 3: 1694–1699
28	Bottou L. Large-scale machine learning with stochastic gradient descent. In: Procceedings of OMPSTAT. 2010, 177–186 https://doi.org/10.1007/978-3-7908-2604-3_16
29	Bottou L, Bousquet O. The tradeoffs of large scale learning. Advances in Neural Information Processing Systems, 2008, 20: 161–168
30	Sculley D. Combined regression and ranking. In: Proceedings of the 16th ACM SIGKDD Conference. 2010, 979–988 https://doi.org/10.1145/1835804.1835928
31	Wu X, Yu K, Ding W, Wang H, Zhu X. Online feature selection with streaming features. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2013, 35(5): 1178–1192 https://doi.org/10.1109/TPAMI.2012.197
32	Ditzler G, Rosen G, Polikar R. Incremental learning of new classes from unbalanced data. In: Proceedings of International Joint Conference on Neural Networks (IJCNN). 2013, 33–42 https://doi.org/10.1109/ijcnn.2013.6706770
33	Filzmoser P. A multivariate outlier detection method. In: Proceedings of the 7th International Conference on Computer Data Analysis and Modeling. 2004, 18–22

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