Antenna Array Processing for Radar Applications with Support Vector Machines

Support Vector Machines (SVM) are a good candidate for the solution of antenna array processing problems such as beamforming, estimation of angle of arrival or Ultra-Wide Band (UWB) electromagnetic design, because these algorithms provide superior performance in generalization ability and computational complexity. In this work we revise some applications of the SVM for antenna array processing and for target detection in UWB sea surface surveillance radar return profiles

The first presented approach is based on the use of linear and nonlinear regressor schemes applied to antenna array processing, in particular to beamforming. The last one is related to the application of nonlinear multiclass Support Vector classification applied to radar object detection. Comparisons with conventional strategies and simulation results are provided to demonstrate the advantages of the Support Vector Machine approaches

Since the 1990s there has been a significant activity in the theoretical development and applications of SVM. The first applications of machine learning have been related to data mining, text categorization, and other classical pattern recognition tasks. Recently, however, SVM have been applied to signal processing3, wireless communication problems, notably spread spectrum receiver design and channel equalization.