Radar Emitter Signal Analysis with Estimation of Distribution Algorithms

This paper proposes a novel approach (short for iEDA/TFAD) based on estimation of distribution algorithms and time-frequency atom decomposition for analyzing radar emitter signals. In iEDA/TFAD, an improved estimation of distribution algorithm combining Gaussian and Cauchy probability models is presented to implement time-frequency atom decomposition to analyze several typical radar emitter signals by extracting their features and recognizing them. The introduction of iEDA can greatly reduce the computational complexity of TFAD. Experimental results show that EDA/TFAD can efficiently recognize several radar emitter signals at a high correct rate.

[1]  John Platt,et al.  Large Margin DAG's for Multiclass Classification , 1999 .

[2]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

[3]  T L Carroll,et al.  A nonlinear dynamics method for signal identification. , 2007, Chaos.

[4]  Gexiang Zhang.  Support Vector Machines with Huffman Tree Architecture for Multiclass Classification , 2005, CIARP.

[5]  Alexander Mendiburu,et al.  A Review on Parallel Estimation of Distribution Algorithms , 2010, Parallel and Distributed Computational Intelligence.

[6]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[7]  Eduardo A. B. da Silva,et al.  Optimum Rate-Distortion Dictionary Selection for Compression of Atomic Decompositions of Electric Disturbance Signals , 2007, IEEE Signal Processing Letters.

[8]  Gexiang Zhang,et al.  Quantum-inspired evolutionary algorithms: a survey and empirical study , 2011, J. Heuristics.

[9]  Ge-Xiang Zhang,et al.  Time-Frequency Atom Decomposition with Quantum-Inspired Evolutionary Algorithms , 2010, Circuits Syst. Signal Process..

[10]  Roberto Cristi,et al.  Extraction of polyphase radar modulation parameters using a wigner-ville distribution - radon transform , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Xiangxiang Zeng,et al.  A Novel Membrane Algorithm Based on Differential Evolution for Numerical Optimization , 2011, Int. J. Unconv. Comput..

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[14]  Zhang Gexiang.  A Novel Approach of Feature Extraction for Advanced Radar Emitter Signals Using Time-Frequency Atom Decomposition , 2010 .

[15]  Stephen Grossberg,et al.  A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters , 2001, Neural Networks.

[16]  Jesus Grajal,et al.  Atomic decomposition-based radar complex signal interception , 2003 .

[17]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[18]  V. Koivunen,et al.  Automatic Radar Waveform Recognition , 2007, IEEE Journal of Selected Topics in Signal Processing.