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Energetic azimuth–range characteristics of passive interference (provided by the state enterprise “Iskra”)

Method of forming classified training sample in case of spacial signal processing under influence of combined interference

Dmytro M. Piza, T. I. Bugrova, V. N. Lavrentiev, Dmytro S. Semenov


Under the conditions of the combined interference, the operation efficiency of the radar equipment is substantially deteriorated. This is due to the decorrelation of the signals of the point source of active interference acting on the radar by passive interference. In this article methods of formation of the classified training sample, generated only by active interference, are considered for adaptation of the weight coefficients of spatial filters under conditions of combined interference presence. An effective method of forming the classified training sample generated by active masking interference was developed for spatial processing of radar signals under conditions of simultaneous exposure to passive interference. The developed method of forming the training sample is based on estimating the width of the normalized autocorrelation function in each element of the distance resolution. The current analysis of the combined interference components in each resolution element improves the quality of the interference component classification and, as a result, minimizes the effect of passive interference on the adaptation process of the spatial filter. The theoretical and practical aspects of the formation of the classified training sample are considered. The functional scheme of the classifier for the combined interference components is developed. The efficiency of the proposed method is compared with known correlation methods. The current analysis of the combined interference components in each element of the range resolution improves the quality of interference classification, which is important in the context of complex hydrometeorological conditions.


radar signal processing; adaptive spatial filtering; combined interference; classified training sample; normalized autocorrelation function; functional scheme

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