Waveform recognition in pulse compression radar systems

被引：7

作者：

Lundén, J ^{[1
]}

Terho, L ^{[1
]}

Koivunen, V ^{[1
]}

机构：

[1] Helsinki Univ Technol, Signal Proc Lab, SMARAD CoE, FIN-02015 Helsinki, Finland

来源：

2005 IEEE Workshop on Machine Learning for Signal Processing (MLSP) | 2005年

关键词：

MODULATION RECOGNITION; NEURAL-NETWORK; CLASSIFICATION; ALGORITHM; SIGNALS;

D O I：

10.1109/MLSP.2005.1532912

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a system for recognizing pulse compression radar waveforms is introduced. The waveforms considered in this study are the linear frequency modulation (LFM), Costas codes, binary phase codes, and the Frank, P1, P2, P3, and P4 codes. A feature vector based on instantaneous signal properties, second- and higher-order statistics, and time-frequency distributions is computed from the received signals., Cyclic correlations are used in symbol rate estimation. Information theoretic measure is used to remove redundant components from the feature vector. The discrimination capability of the features is evaluated using an ensemble averaging early-stop committee of multilayer perceptrons. Bayesian MLP classifier is considered as well. In simulation the classifier attains over 97 % overall correct classification rate in signal-to-noise ratio (SNR) of 6 dB.

引用

页码：271 / 276

页数：6

共 18 条

[1] [Anonymous], 1996, LECT NOTES STAT
[2] Bishop CM, 1995, Neural Networks for Pattern Recognition
[3] Bridle J. S., 1990, Neurocomputing, Algorithms, Architectures and Applications. Proceedings of the NATO Advanced Research Workshop, P227
[4] Asymptotic analysis of blind cyclic correlation-based symbol-rate estimators
Ciblat, P
Loubaton, P
Serpedin, E
Giannakis, GB
[J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2002, 48 (07) : 1922 - 1934
[5] Hampshire J. B., 1990, P 1990 CONN MOD SUMM, P159
[6] An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers
Husmeier, D
Penny, WD
Roberts, SJ
[J]. NEURAL NETWORKS, 1999, 12 (4-5) : 677 - 705
[7] Input feature selection by mutual information based on Parzen window
Kwak, N
Choi, CH
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2002, 24 (12) : 1667 - 1671
[8] Bayesian approach for neural networks - review and case studies
Lampinen, J
Vehtari, A
[J]. NEURAL NETWORKS, 2001, 14 (03) : 257 - 274
[9] Lewis B.L., 1986, ASPECTS RADAR SIGNAL
[10] Atomic decomposition-based radar complex signal interception
Lopez-Risueño, G
Grajal, J
Yeste-Ojeda, O
[J]. IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION, 2003, 150 (04) : 323 - 331

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