Direction Finding of Distributed Man-Made Noise Sources Using Artificial Neural Networks

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  • ニューラルネットワークを用いた人工雑音源の方位測定法
  • ニューラル ネットワーク オ モチイタ ジンコウ ザツオンゲン ノ ホウイ ソ

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Abstract

With an increased utilization of electronic systems, we are disturbed by the noises from other systems. In the array-antenna systems, the recived signal is composed of any sources including undesirable noise sources. Finding out their direction of arrival (DOA) is important in order to eliminate those sources, and DOA is a very important problem for EMC. Many techniques have so far been proposed, but DOA has not been well established. For example, most of these techniques are based on the assumption that the noise sources are a point source. Such an assumption is not appropriate in real situations, because the sources are generally distributed in the space and also their shapes are not always Gaussian. This paper presents a new method for finding the DOA of distributed man-made noises using artifitial neural networks. In particular, we use the Hopfield network. This is due to the feature of this network that we can expect rapid convergence of network energy to a minimum point. The sources are characterized by arbitrary parameters (e. g., amplitude, frequency, incident angle, and distribution). Simulation results are presented and discussed for the case of a single source characterized by two parameters, namely, the incident angle and the distribution of source. Additionally, we also present the results for the case of two sources, and we study the influence of S/N ratio on our method.

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