DeepTDOA: Direction Finding using Deep Learning on Distributed TDOA-based Systems in the Presence of Time Offset

With the advancing sensor (receiver-processor) technology, Time Difference of Arrival (TDOA) based systems have become attractive for estimating the direction of a transient or pulsed signal. Compared to traditional phase or amplitude comparison-based direction finding (DF) systems, distributed TDOA systems have several advantages. These include the absence of uncertainty problems, direction of arrival (DOA) estimation independent of sensor and signal characteristics, mobility, and the dynamic array manifold structure. On the other hand, in distributed TDOA systems, a synchronization problem arises among clocks since each node has its own local clock. This article introduces a deep neural network (DNN) structure capable of estimating the direction of a signal in the presence of time offset. Experimental (simulation) results demonstrate that the proposed method outperforms classical approaches.