فهرست مطالب javad ghalandari

Due to developments have been occurred in ECCM techniques of radars, pulse separation methods in ESM systems rely on intera-pulse modulation instead of analyzing pulse descriptive words in traditional methods. Extraction of pulse modulation is a suitable method but in the case of multi-mode radars, the number of targets is overestimated by changing the intra-pulse modulation. 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 at the post processing stage. This method involves providing an appropriate framework for examining the separated pulse string by defining and selecting similarity criteria from the extended PDW. In this method, at first the distinguishing features of each radar are extracted and the similarity criteria of each feature are calculated to check the similarity between the two pulse strings. Input data contains information separated from real radars received by ESM system. Due to the uncertainties of each criteria, similarity scores are computed through the fuzzy roles and normalization and training dataset is formed. The data table is then used to train a perceptron neural network. The trained network can detect multimode radars automatically. To test the network, a section of the data table is applied to the network and trained network succeed in 100% of test data to distinguish multi-mode radars from the distinct radars.