ENHANCED ARTIFICIAL NEURAL NETWORK ALGORITHMS FOR FAST RADAR THREATS IDENTIFICATION

Document Type : Original Article

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Egyptian Armed Forces.

Abstract

In this paper, an enhanced algorithm for radar threat identification and based on the artificial neural network (ANN) is proposed. Four radar parameters are used as the inputs for the suggested ANNs. These parameters are: 1) the radio frequency, 2) the pulse repetition frequency, 3) the pulse width, and 4) the scan rate. A lot of work has been done to select the suitable structure of the ANNs. The chosen ANNs achieve minimum sum square errors and short time training. Also, they provide the highest success rate over all the examined networks. It is found that, one can choose a single hidden layer ANN structure with 12 nodes in the hidden layer or a double hidden layer with six nodes in each hidden layer. These ANN provide 100% success rate. Due to the simplicity of the ANNs structure, it can be used for on-line analysis. To use the developed ANN algorithms for radar threat identification in the on-line analysis, the main requirement is to finish the training phase beforehand.

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