Satellite Cluster Consepts: A system evaluation with emphasis on deinterleaving and emitter recognition

Abstract

In a dense and complex emitter environment, a high pulse arrival rate and a large number of interleaved radar pulse sequences is expected, from both agile and stable emitters. This thesis evaluates the combination of interval-only algorithms with different monopulse parameters, in comparison to a neural network to do accurate emitter classification. This thesis has evaluated a selection of TOA deinterleaving algorithms with the intent to clearly discriminate between pulses emitted from agile emitters. The first section presents the different techniques, with emphasis on pinpointing the different algorithmic structures. The second section presents a neural network combinational recognition system, with a main focus on the fuzzy ARTMAP neural network, where also some practical implementations has been presented. The final section gives a partial system evaluation based on some statistical means, seeking to get an estimate on the information flow from the ESM receiver as a function of both the density and the expected parametric values, i.e. PW since this is proportional to the amount of processed pulses.