Electronic warfare (EW) receivers are passive receivers which receive emissions from other platforms, and do certain analysis on these emissions. Some EW receivers receive radar pulses, measure the parameter of each p...
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Electronic warfare (EW) receivers are passive receivers which receive emissions from other platforms, and do certain analysis on these emissions. Some EW receivers receive radar pulses, measure the parameter of each pulse received and group the pulses that belongs to the same emitter together to determine the radar parameters for each emitter. These parameters are then compared with values stored for known radar types, to identify the emitter type. Two parts are focused, emitters deinterleaving and prf-type identification. The deinterleaving is done through parameters clustering. Two parameters are selected for clustering direction of arrival and radio frequency. A self-organising neural network called Fuzzy ART is proposed for clustering. This algorithm has a very good clustering quality and can run in real-time applications. The prf-type identification is done through time-of-arrival (TOA) analysis. Three previously presented algorithms are combined in new scheme to do the TOA analysis (or prf-type identification). These algorithms are difference TOA histogram, TOA folding histogram and sequence search algorithm. The complete proposed system has been tested using three different tests. These tests are simple PRI test, jittered PRI test and staggered PRI test. The proposed system identifies up to 90 simple emitters, 20 jittered emitters and 20 staggered emitters. In all tests, the data were simulated and generated using software.
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