Abstract
Direction of arrival (DOA) based localization is widely applied in interferer localization. However, conventional terrestrial DOA estimation suffers from multi-path effects and shadow fading, while the UAV-based measurement on the shortwave and the very high frequency (VHF) band is subject to stringent constraints on the weight, size and power consumption of the payloads. This paper proposes to place an directional antenna at a pan-and-tilt positioner to measure and collect RSS values for DOA estimation. Nevertheless, to address such a three-dimensional (3-D) DOA estimation, conventional estimation methods based on solving a least square minimization problem suffer high computational complexity. As a countermeasure, this paper proposes an image processing-based DOA estimation. Benefiting from image denoising and a proposed k-means clustering-based image segmentation, the proposed approach can outperform various baseline schemes, in terms of estimation accuracy, especially in the low signal-to-noise regions. Compared to the conventional estimation method, the proposed approach can significantly decrease the average running time.
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