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Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.
Electromagnetic Radiation Source Identification (ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics. Discriminatory capability of machine learning methods has been been used for facilitating ERSI.This paper presents a new approach to improve ERSI. by adoption support vector machines, which are proven to be effective tools in pattern classification and regression, on the basis of the spatial distribution of electromagnetic radiation sources. Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model. The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%. The influence of parameters (eg, penalty parameter, reflection and noise from the ambient environment , and the scaling method for the input data) are discussed.The proposed method has good performance in noisy a nd reverberant environment. It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB. proposed method has better accuracy in a noisy environment than artificial neural networks. Give that each Electromagnetic (EM) source has unique spatial characteristics, this method can be used for EM source identification and EM interference diagnostics.