# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results
### Evolution and Indicator  
To objectively assess the detection performance of the proposed method, we evaluate the algorithm's effectiveness from common evaluation metrics in the field of object  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{TT\&C Signal Simulation Parameters} \\ \hline Signal modulation type & BPSK, QPSK, PCM-BPSK-PM, PCM-QPSK-PM \\ Signal center frequency (MHz) & 2200–2240 \\ Bandwidth (MHz) & 3–30 \\ Channel & Gaussian Noise and Rayleigh Fading \\ SNR (dB) & \(-\)15–15 \\ Number of satellite telemetry signals & 0–1 \\ \hline \multicolumn{3}{c}{Background Signal Simulation Parameters} \\ \hline Spectrum size & \multicolumn{2}{c}{3500 \(\times\) 2625} \\ Frequency range (MHz) & 2100–2400 \\ Background environmental signal & 3G/4G signal, etc. \\ \hline
3G signal type & WCDMA & CDMA2000 & TD-SCDMA \\ Chip rate (Mchip/s) & 3.8 & 3.68 & 1.28 \\ Frame length (ms) & 10 & 25 & 10 \\ Time slot & 15 & 15 & 15 \\ Data modulation & QPSK & QPSK & QPSK \\ Channel width (MHz) & 5 & 5 & 1.6 \\ \hline \multicolumn{3}{c}{4G signal} & TD-LTE \\ Carrier bandwidth (MHz) & 20 & \\ Subcarrier bandwidth (MHz) & 15 & \\ Frame length (ms) & 10 & \\ Time slot configuration & 10:2:2 & \\ Data modulation & OFDM & \\ \hline \multicolumn{3}{c}{Neural Network Hyperparameters} \\ \hline Training epoch & 200 \\ Batch size & 200 \\ Initial learning rate & \(1\times 10^{-4}\) \\ Learning rate adjustment strategy & StepLR \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset and experimental parameters.  
detection, including average recall (AR), mean average precision (mAP), frames per second (FPS), and the estimation errors of signal parameters, denoted as \(M_{s}\).  
Table 2 presents a confusion matrix, elucidating the concepts of true positives (TPs), true negatives (TNs), false negatives (FNs), and false positives (FPs).  
Average recall [50], employed to assess the network's miss detection rate, is the probability of correctly detecting a target (TP) among all prospective targets to be detected (TP and FN). The calculation formula is given by  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{14}\]  
Precision [50] is the probability that the correctly detected target (TP) accounts for all detected targets (TP and FP). The calculation formula is  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{15}\]  
Average precision [51], the area under the precision-recall (PR) curve, as shown in (16), is calculated from a curve plotted by precision and recall. Recall serves as the horizontal axis, while precision serves as the vertical axis. A larger area under the PR curve, denoted as a higher AP, indicates higher precision and recall, hence a better overall detection performance of the model.  
\[\mathrm{AP}=\int_{0}^{1}\mathrm{P_{d}(r)d}\mathrm{r} \tag{16}\]  
where \(\mathrm{P_{d}(r)}\) represents the PR curve of the detection results.  
FPS is used to assess the speed of object detection, representing the number of images that can be processed in one second.  
The estimation error in signal parameters \(M_{s}\) is the average normalized estimation error percentage for parameters such as the center frequency \(f_{s}\) and bandwidth \(B_{s}\) of the detected satellite signal. The calculation formula is  
\[M_{s}=\frac{1}{k}\sum_{i}^{k}\left|\frac{\overline{\varepsilon_{i}}- \varepsilon_{i}}{\varepsilon_{i}}\right| \tag{17}\]  
where \(\varepsilon_{i}\) is the estimated value of each signal parameter, \(\varepsilon_{i}\) is the true value, and \(k\) is the number of parameters to be estimated.