Enhancing Direction of Arrival Trajectory Estimation with Data-Driven Approaches

Abstract

Localization and tracking play a critical role in various fields, ranging from wireless communications and robotics to surveillance and autonomous vehicles. In the context of signal processing, localization typically involves measuring signals received from multiple sensors to estimate the source’s position. Tracking, on the other hand, extends localization to a temporal context. In this thesis, we explore the data-driven approaches to enhance the direction of arrival (DOA) trajectory estimates. DOA estimation methods involve processing multi-channel sensor array data to determine the directions from which signals originate. Classical methods have two key limitations: first, they rely on analytical properties of observed signals which do not generalize well to non-ideal conditions; second they employ block-level processing, assuming static DOA within each block. In this thesis, the first challenge is resolved with a data-driven approachs, while the second is addressed by operating in DOA-trajectory space instead of DOA space. So, instead of estimating individual DOA, the approach focuses on estimating DOA trajectories. We proposed two data-driven approaches: one grid-based and the other grid-free. In the grid-based approach, we develop a fully convolutional neural network (FCNN) inspired by the computer vison techniques of image translation. The FCNN processes the 2D low resolution spectrum as input and outputs a refined 2D spectrum. The input spectrum typically has broad peaks, while the transformed spectrum has sharp peaks, which allows precise identification of DOA trajectory parameters. In another study, we develop a data-driven approach that is both grid-free and re-useable. This addresses two issues: first, the limitation of parameter estimation on predefined grids, which affects resolution; and second, the problem of estimating all sources at once, which can make traditional methods dependent on the number of sources. A deep complex network is proposed that directly processes the complex sensor array data, with outputs comprising of complex signal amplitude (per snapshot) and trajectory parameters for a single source. We obtain a residual by removing contribution of the identified source from the input data and this residual is again fed back into the network to identify the next source making it re-useble and independent of the number of sources to be estimated. These proposed methods are rigorously evaluated through extensive experiments and analysis, demonstrating their advantages over existing approaches. These findings have the potential to impact a variety of applications, with room for further improvement.