Multi-mode radars discrimination based on Similarity criteria in interleaved pulses

Due to developments of radars in changing parameters, pulse separation methods in ELINT systems have relied on intra-pulse modulation instead of analyzing common pulse description words. Number of radars in this method, may be estimated incorrectly because a modern radar with the ability in changing intra-pulse modulation type may be detected as multiple radars.The purpose of this paper is to detect multi-mode radars with various types of internal modulation in a dense radar environment. The proposed’solution is to add multi-mode radars detection to the existing pulse separation methods. In this method, by extracting the distinguishing features of each radar, similarity criteria of each feature are calculated to examine the similarity between the two pulse streams. Input data are features that  extract of  real data of a ELINT system with traditional parameters and developed parameters related to the pulse shape provided by a simulator. The dataset is then used to train a LVQ neural network to discriminate between different and multi-mode radars. After training the network, in the new operating conditions, a multi-mode radar’s pulse streams is classified as a single radar.The simulation results show a higher accuracy for classification with the similarity criteria of developed features than the criteria extracted of  classical data at different SNRs. Also, the increase in classification accuracy in single-layer perceptron and multi-layer neural network at SNR equal to 0.5 dB has been shown. In similar articles, only multi-mode radars with the ability to change frequency and PRI have been investigated, but with the proposed method, radars with the ability to change several parameters along with the type of intra-pulse modulation can be identyfied.The innovation of the article is the development of distinguishing features and the idea of ​​similarity criteria for the detection of multi-mode radars.