# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 1 Introduction  
In recent years, with advancements in wireless communication technology, satellite communication has gained widespread application in both military and civilian sectors due to its abundant spectrum resources, wide coverage, and freedom from geographical constraints [1, 2]. However, the continuous development of communication frequency bands, the emergence of new modulation techniques, and the proliferation of unidentified systems and proprietary protocols have resulted in an increasing number of signals. Furthermore, complex terrestrial and celestial electromagnetic environments present various challenges such as frequency and time-selective fading, dynamic noise, and interference [3, 4]. These challenging channel conditions pose severe difficulties for the detection [5] and analysis of satellite telemetry, tracking, and command (TT&C) signals under non-cooperative conditions. Under the premise of receiving complete signals, signal restoration can be achieved through techniques such as signal adaptive reconstruction [6], time-domain equalization [7], etc., enabling high-precision detection. However, in practical reception, the equipment side necessitates swift signal transmission for real-time analysis. Due to limitations innon-cooperative information or constraints on the data volume of intermediate frequency signals, real-time processing often only obtains the spectral amplitude of the signal, with missing phase information. Under the condition of utilizing solely spectral amplitude information, signal processing and restoration methods based on time-domain sequences are almost infeasible. Existing solutions addressing this challenge remain limited, as traditional time-domain processing methods exhibit significant limitations under severe restrictions on phase information. For classical detectors based on deep learning, the framework adjustment from general target detection to signal detection primarily involves the adaptation of prior anchors' sizes to accommodate signals through statistical or clustering methods. However, the aspect ratio variations in signals in spectrograms are substantial, making it challenging for even well-designed prior anchors to match all signals. When predicting the signal positions for candidate regions, the model generates multiple prediction boxes, which inevitably raises the question of how to eliminate redundant predictions. Most approaches rely on post-processing steps, such as non-maximum suppression (NMS) [8] or its related improved algorithms [9], to filter and retain the most probable box among multiple potential predictions for the same target by setting a threshold. However, there is significant variation in the aspect ratios of signals in the spectrogram, making it challenging for well-designed prior anchors to match different signals. Furthermore, incorporating the NMS data post-processing step complicates the detector and hinders the optimization and fine-tuning of the entire model. Even a well-performing trained model faces hardware compatibility issues with the NMS algorithm during practical deployment, resulting in poor transferability and usability. The above defects result in a decrease in the performance of classical detectors when used for signal detection. Therefore, this paper aims to address the task of satellite TT&C signal detection under constrained conditions by proposing a TATR-based intelligent satellite signal detection algorithm. In the scenario where only the signal spectrum amplitude information is available, we first convert the 1D signal amplitude sequence into a 2D spectrogram image to facilitate the correlation of signal frequency and spatial features. Based on Triplet Attention [10] and the ResNet50 network, we designed the ResTA backbone for extracting spectrum spatial features. To enhance the interaction capability of spectral context, we introduce the Transformer network to associate the local frequency information of signal spectra.