Incremental learning approach based on vector neural network for emitter identification

To deal with the problem of emitter identification (EID) caused by the measurement uncertainty of emitter feature parameters and to realise the automatic updating of the emitter database, which is usually used as emitter templates in identification processing, a vector neural network based incremental learning (VNNIL) approach for EID is proposed. This method combines the vector neural networks (VNNs) and the ensemble-based incremental learning (Learn++) algorithm. The VNN is adopted to construct a weak classifier and the Learn++ is used to generate ensembles of the weak classifiers. Considering that the VNN can realise the non-linear mapping between the interval-value input data and the interval-value output emitter types, and that the Learn++ can update the emitter database automatically, the VNNIL treats the two mentioned problems above as a single one and realises EID and parameters updating at the same time. A number of simulations are presented to demonstrate the identification and updating capability of the VNNIL algorithm. As shown in the simulation results, the VNNIL algorithm not only possesses a better learning and identification capability, but also achieves a better noise adaptability.