# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 2 Related Works
### Deep-Learning-Based Signal Detection  
Currently, deep learning models for object detection in computer vision can be broadly categorized into two types. The first is a two-stage model based on region recommendation, exemplified by region-based convolutional neural networks (R-CNNs) [21], fast region-based convolutional neural network (Fast-RCNN) [22], faster region-based convolutional neural network (Faster-RCNN) [23], etc. These algorithms follow a two-step process: firstly, they extract candidate regions in the global sliding window and determine whether these regions are foreground or background; secondly, they classify the targets within the region. The second type consists of regression-based one-stage models like Single Shot Multi-Box Detector (SSD) [24], You Only Look Once (YOLO) [25], YOLOv2 [26], and subsequent YOLO versions [27; 28]. The rapid advancement in deep learning has injected new vitality into signal detection and recognition [29; 30; 31]. [PERSON] et al. [32] employ convolutional long short-term deep neural networks (CLDNNs) cascaded with convolutional neural networks (CNNs) and long short-term memory (LSTM) to extract time-domain and frequency-domain features from input signal sequences. which gives better performance than traditional energy detection algorithms. This type of algorithm can directly extract features from preset candidate regions, with fast detection speed but relatively low accuracy. [PERSON] et al. [PERSON] et al. (2018) utilize the Fast-RCNN object detection model and achieve an average signal detection rate of 90% based on broadband signal time-frequency maps. However, due to the two-stage process involved in this type of algorithm, its real-time performance is suboptimal. [PERSON] et al. [PERSON] et al. (2018) divide the entire signal frequency band into broadband segments, obtain time-frequency maps using short-time Fourier transform, and propose a CNN-based broadband signal detection method, successfully detecting and recognizing six types of signals. Additionally, [PERSON] et al. (2018) introduces a deep learning method based on YOLO to automatically and accurately pick out tweet signals from VLF measurements. The aforementioned deep-learning-based methods typically require time-domain IQ sequences or their transformed counterparts, such as time-frequency spectrograms, as inputs to deep neural networks. However, this is difficult to meet under the condition of limited phase information. Most object detectors locate objects through internal pixels in the image, and it is difficult for the receptive field of CNN to cover signals of a long duration. Transformers [PERSON] et al. (2018) have undergone significant development, emerging as a popular research topic. Initially employed in natural language processing (NLP), they have proven their ability to capture local information based on self-attention. The success of the Vision Transformer (ViT) [PERSON] et al. (2018) in image recognition tasks has demonstrated the substantial potential of Transformers in computer vision (CV). With ongoing research, numerous Transformer models tailored for object detection tasks have emerged. For instance, [PERSON] et al. [PERSON] et al. (2018) defined object detection as a set prediction problem and introduced a novel framework named Detection with Transformer (DETR). Subsequently, [PERSON] et al. [PERSON] et al. (2018) proposed Deformable DETR, incorporating locally sparse deformable attention modules to address the challenge of poor performance in detecting small objects. [PERSON] et al. [PERSON] et al. (2018) presented an anchor-decoupled attention mechanism, referred to as anchor DETR. However, Transformer-based models face challenges of slow algorithm convergence and high computational resource consumption. This is primarily attributed to the need for multiple layers of encoders and decoders to capture global attention, introducing a significant number of parameters. Some researchers have alleviated this issue from the perspective of optimizing data parallelism [PERSON] et al.