Improvements on deinterleaving of radar pulses in dynamically varying signal environments

Abstract An electronic support system receiver which is a passive receiver picks up an interleaved stream of pulses and extracts their pulse parameters. These parameters are sent to a deinterleaving subsystem which sorts them and forms pulse cells that each are assumed to belong to a specific emitter. In this paper, we develop a method for this task of deinterleaving of radar pulse sequences. For this aim, a novel pulse amplitude tracking algorithm is proposed for dynamically varying signal environments wherein radar parameters can change abruptly. This method particularly works for air-to-air engagements where pulse amplitude distortion due to channel effects can be considered negligible. Simulation results show that the proposed algorithm incorporated with a clustering algorithm improves deinterleaving of radar emitters that have agile pulse parameters such as airborne radars.

[1]  John B. Moore,et al.  Deinterleaving pulse trains using discrete-time stochastic dynamic-linear models , 1994, IEEE Trans. Signal Process..

[2]  Farid Alilat,et al.  Implementation of fuzzy-ART on FPGA: Classification of the Algiers's Bay , 2012, 2012 Colloquium in Information Science and Technology.

[3]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[4]  Esa Alhoniemi,et al.  Self-organizing map in Matlab: the SOM Toolbox , 1999 .

[5]  S. Grossberg How does a brain build a cognitive code , 1980 .

[6]  K. S. Venkatesh,et al.  Emotion recognition from geometric facial features using self-organizing map , 2014, Pattern Recognit..

[7]  Biing-Hwang Juang,et al.  Nonlinear Compensation Using the Gauss–Newton Method for Noise-Robust Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  Robert J. Brunner,et al.  SOMz: photometric redshift PDFs with self organizing maps and random atlas , 2013, ArXiv.

[9]  John B. Moore,et al.  The limits of extended Kalman filtering for pulse train deinterleaving , 1998, IEEE Trans. Signal Process..

[10]  Adam Gacek Preprocessing and analysis of ECG signals - A self-organizing maps approach , 2011, Expert Syst. Appl..

[11]  Valentina Radić,et al.  Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra , 2016 .

[12]  P. Kitanidis,et al.  Maximum likelihood parameter estimation of hydrologic spatial processes by the Gauss-Newton method , 1985 .

[13]  David L Adamy,et al.  Ew 101: A First Course in Electronic Warfare , 2001 .

[14]  Hakan Tora,et al.  Performance evaluation of self organizing neural networks for clustering in ESM systems , 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).

[15]  John B. Moore,et al.  On the estimation of interleaved pulse train phases , 2000, IEEE Trans. Signal Process..

[16]  Carlos A. B. Mello,et al.  Segmentation of connected handwritten digits using Self-Organizing Maps , 2013, Expert Syst. Appl..

[17]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[18]  Ali Kara,et al.  Design of a scenario-based synthetic mixed pulse generator , 2013, 2013 21st Signal Processing and Communications Applications Conference (SIU).

[19]  Vikram Krishnamurthy,et al.  An interval-amplitude algorithm for deinterleaving stochastic pulse train sources , 1998, IEEE Trans. Signal Process..

[20]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[21]  Gülsen Aydin Keskin,et al.  The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection , 2010, Expert Syst. Appl..

[22]  Sungshin Kim,et al.  A passport recognition and face verification using enhanced fuzzy ART based RBF network and PCA algorithm , 2008, Neurocomputing.

[23]  S. N. Abdullah,et al.  Deinterleaving of radar signals and PRF identification algorithms , 2007 .

[24]  Masaaki Kobayashi,et al.  Improved algorithm for estimating pulse repetition intervals , 2000, IEEE Trans. Aerosp. Electron. Syst..

[25]  Yvon Savaria,et al.  A comparison of self-organizing neural networks for fast clustering of radar pulses , 1998, Signal Process..

[26]  Carlos R. Minussi,et al.  Electric load forecasting using a fuzzy ART&ARTMAP neural network , 2005, Appl. Soft Comput..

[27]  Siamak Khorram,et al.  An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery , 2009, Remote. Sens..

[28]  Young-Seuk Park,et al.  Self-Organizing Map , 2008 .

[29]  B. M. Popovic,et al.  Improved algorithm for the deinterleaving of radar pulses , 1992 .

[30]  C. L. Davies,et al.  Automatic processing for ESM , 1982 .

[31]  H. K. Mardia New techniques for the deinterleaving of repetitive sequences , 1989 .

[32]  P. S. Ray A novel pulse TOA analysis technique for radar identification , 1998 .

[33]  T. Kohonen SELF-ORGANIZING MAPS: OPHMIZATION APPROACHES , 1991 .

[34]  Chee Peng Lim,et al.  A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction , 2016, Expert Syst. Appl..

[35]  Dino Isa,et al.  Using the self organizing map for clustering of text documents , 2009, Expert Syst. Appl..

[36]  Chuan-Yu Chang,et al.  A robust DWT-based copyright verification scheme with Fuzzy ART , 2009, J. Syst. Softw..

[37]  John B. Moore,et al.  Spectrum estimation of interleaved pulse trains , 1999, IEEE Trans. Signal Process..

[38]  Richard G. Wiley,et al.  ELINT: The Interception and Analysis of Radar Signals , 2006 .

[39]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..