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In applications such as seismic acquisition, the position information of sensor nodes, that are deployed in a linear topology, is desired with sub-meter accuracy in the presence of a limited number of anchor nodes. This can be achieved with antenna arrays via mm-wave cooperative localization, whose performance limits are derived in this letter. The number of anchor nodes is seen to have a stronger impact than the number of antenna elements in the anchor nodes. Succinct closed-form expressions for the position error bound are also obtained for 1-hop and 2-hop cooperative localization, where sub-meter accuracy is perceived over several hundred nodes.

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Oil and gas companies consider transforming conventional cable-based seismic acquisition to wireless acquisition as a promising step for cost and weight reduction in reservoir exploration. Wireless seismic acquisition requires large number of wireless geophone (WG) sensors to be deployed in the field. The locations of the WG sensors must be known when processing the collected data. The application of direction of arrival (DOA) estimation helps in localizing WGs and improves received signal level through beam steering and interference avoidance. Conventional DOA algorithms require high computational complexity which renders them inefficient for real-time response. In this paper, deep neural network (DNN) is proposed for DOA estimation of WGs at wireless gateway node (WGN) under different channel conditions. The estimated angle and corresponding coordinates of WGNs are used in least square estimation (LSE) to estimate the position of the WGs. The simulation results depict reasonable estimation and position accuracy in real-time.

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