# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 1 Introduction
With high computational capacity and advanced modeling capabilities, deep learning technology has shown significant research potential in the field of sound source localization [20]. Its advantage lies in that it can automatically extract effective information from a large amount of raw data without relying on artificial design features. Meanwhile, under strong noise and reverberation conditions, deep learning models can achieve a robust estimation of sound source location through data-driven methods, which is superior to traditional methods [21]. The main methods include classification models, Transformer and hybrid architectures, and end-to-end raw signal modeling. Currently, most deep learning-based DOA estimation methods commonly use classification models for angle prediction [22]. For example, some studies have successfully achieved effective prediction of the true angle of arrival using deep neural networks (DNN) trained with the lower triangular element of the covariance matrix as an input feature [23]. With the widespread application of convolutional neural networks (CNN), researchers can further learn spectral features through CNN to improve model performance and training efficiency [24]. Subsequently, various CNN-based depth frameworks have been proposed, such as DeepMUSIC [25], which divides DOA space into multiple sub-regions and estimates them separately. In parallel, regression-based networks have gained traction for their continuous angle estimation, eliminating quantization errors inherent in classification approaches. Techniques like multi-layer perceptron (MLP) regression heads and end-to-end convolutional regression architectures demonstrate sub-degree precision in 2D azimuth-elevation estimation [26], proving especially valuable for full-spatial field analysis. To improve the estimation accuracy, some researchers propose to extract and splice the real part, imaginary part and phase information of covariance matrix of multi-channel signals as inputs, respectively, to enhance the DOA estimation ability under low-SNR conditions [27]. With the widespread adoption of Transformer networks, researchers have also attempted to apply them to the DOA field [28], contributing to the achievement of super-resolution DOA estimation [29], namely Transformer and hybrid architectures, where self-attention mechanisms capture long-range dependencies across sensor arrays, enabling joint spatial-spectral feature learning. This has contributed to the achievement of super-resolution DOA estimation, with recent variants like cross-attention Transformers achieving real-time performance while maintaining millimeter-level accuracy in near-field scenarios [30]. However, the above methods inevitably rely on the traditional DOA processing flow in the data preprocessing stage, which not only increases the calculation overhead, but also may lead to the loss of some feature information, thus affecting model performance. In this regard, some studies have proposed to directly input the original in-phase (I) and quadrature (Q) signals into the neural network for modeling to reduce the preprocessing cost, namely end-to-end raw signal modeling, and improve the estimation accuracy and achieved good results [31]. Notably, these end-to-end approaches have catalyzed advancements in lightweight 2D DOA systems for UAVs. Mature frameworks now integrate time-domain convolutional networks (TCNs) with spatial covariance features, enabling real-time azimuth and elevation tracking on embedded platforms [32]. Such systems compensate for UAV ego-noise through adversarial training and leverage multi-array spatial diversity to resolve front-back ambiguities--critical for navigation in complex urban environments [33]. Based on end-to-end raw signal modeling, this paper proposes a new neural network model named TSNetIQ.