# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 6 Future Work  
Although the proposed TSNetIQ model demonstrates a superior performance in single-source DOA estimation under anechoic conditions, several important aspects remain to be addressed in future research. First, real-world acoustic environments often involve multiple simultaneous sound sources, leading to signal superposition and mutual interference. Therefore, extending the current framework to support multi-source localization will be a valuable and necessary enhancement. To achieve this, the model could be adapted to output multiple angle predictions, potentially through sequence modeling or attention-based multi-output regression mechanisms. Second, while our experiments are conducted in idealized anechoic chambers, actual environments frequently contain reverberation caused by reflections from surrounding surfaces. This reverberation can significantly distort the spatial features embedded in the raw \(IQ\) signals. Future work will incorporate reverberant RIR modeling into the simulation process using tools such as the image-source method in pyroomacoustics Python package, thereby enhancing the realism and robustness of the training data.  
The current results were achieved under relatively large-scale datasets. However, in practical applications, data efficiency is often a critical constraint. It is of interest to explore how the estimation accuracy and angular resolution of the model can be further improved without expanding the scale of the dataset. This could be approached by incorporating self-supervised learning, data augmentation, or knowledge distillation techniques, aiming to maximize information extraction from limited training data.  
Increasing the angular resolution beyond the current 0.1\({}^{\circ}\) while maintaining computational feasibility remains an open challenge. Future efforts may consider multi-resolution regression strategies or hierarchical DOA prediction frameworks to achieve finer-grained direction estimation under the same data constraints. In addition, future work will con  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **M** & **4** & **6** & **8** & **10** & **12** \\ \hline TSNetIQ & 0.85 & 0.53 & 0.27 & 0.18 & 0.14 \\ ResNetIQ & 1.13 & 0.78 & 0.5 & 0.43 & 0.43 \\ CNN & 2.76 & 1.82 & 1.1 & 0.81 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Standard deviation of RMSE over 10 predictions under varying numbers of elements.  
sider the deployment of the proposed model on edge devices. In real-world applications, inference latency and power consumption are critical factors affecting feasibility. Therefore, we plan to adopt techniques such as model pruning, quantization, and a lightweight architecture design to reduce model complexity and improve runtime efficiency, aiming to enable real-time DOA estimation on embedded systems or mobile platforms.  
The current study focuses solely on azimuthal DOA estimation within a single plane. To enhance the system's spatial awareness, future efforts will extend the TSNetIQ framework to support 2D DOA estimation, i.e., simultaneously predicting both the azimuth and elevation angles of the sound source. This extension is particularly valuable for three-dimensional sound source localization in scenarios such as UAV tracking and aerial situational awareness.