# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 3 Proposed DOA Estimation Method
### DOA Estimation Using TSNetIQ  
CNN is a deep learning model that is specifically designed to process data with grid-like structures. It was widely applied to feature extraction from data such as images and audio. CNN extracts local features through convolutional layers and uses parameter sharing and local connection mechanisms to significantly reduce model parameters, thereby improving computational efficiency and generalization ability. The neural network in this study is designed with CNN as the main body, proposing an angle regression model, named TSNetIQ, which integrates the CNN and Transformer mechanisms. The goal is to extract spatiotemporal features from multi-channel \(IQ\) signals and achieve high-precision direction estimation. The overall architecture consists of five 2D convolutional layers, an SE attention module, positional encoding, a Transformer encoder, and a fully connected output layer. Unlike traditional CNN architectures, the model avoids excessive use of pooling layers, except for the pooling layer in the SE module and the average pooling layer at the tail of the model, as pooling layers would degrade the performance of our model. A three-layer 2D convolutional structure was also tested, but it exhibited an insufficient feature extraction capability, leading to inferior performance compared to the five-layer 2D convolutional structure. Taking the case of 4 array elements \(M\), \(f_{s}=4200\) Hz and \(T=3\) s as an example, the specific structure of the TSNetIQ network is as follows. As shown in Figure 2, the overall architecture consists of five 2D convolutional layers, an SE attention module, positional encoding, a Transformer encoder, and a fully connected output layer. The front end of the network is composed of five 2D convolutional layers, which extract the local spatial features of the signal layer by layer. The first layer uses a convolution kernel with a size of (8,7) to enhance joint time-frequency perception, while the remaining four convolutional layers adopt progressively smaller kernel sizes ((1,5), (1,3), (1,3), (1,1)), combined with a 1 \(\times\) 2 stride to control the down-sampling rate. Each convolutional layer is followed by an activation layer and a normalization layer, where the normalization layer uses the BatchNorm2d function, and the activation layer uses the ReLU function to accelerate convergence and improve nonlinear expression capability, which is described as follows. \[ReLU(x)=max(0,x) \tag{9}\]  
After the fifth convolutional layer, an SE attention module is introduced to enhance the sensitivity of the TSNetIQ model to channel features. This module generates attention weights for each channel through global average pooling and a two-layer fully connected network, achieving the adaptive recalibration of features, reinforcing effective features, and suppressing redundant information. In the context of deep learning for DOA, modeling the relationships between channels is considered crucial. To capture sequence dependencies in the high-level features extracted by CNN, a Transformer structure is introduced after the convolutional layers, which consists of a position encoding layer and two Transformer encoder layers. First, the output of the convolutional layers is reshaped, and learnable positional encoding vectors are added to explicitly incorporate the relative ordering of temporal features and facilitate the modeling of sequential dependencies across time steps. The feature sequence is then input into two Transformer encoder layers; this consists of 8 attention heads, 256 embedding dimensions and 512 hidden layer sizes to effectively capture long-range dependencies, integrate global contextual features across the entire sequence, and learn continuous feature representations for precise value estimation. The output of the Transformer encoder, which captures global temporal dependencies across the input sequence, is aggregated through a mean pooling operation to obtain a fixed-dimensional representation.