## 4 Analysis of the Simulation and Experimental Results  
### Experimental Parameter Setting and Dataset Description  
In the process of the simulation experiment, the compound jamming signal and the time-frequency image dataset were generated on the Matlab R2019b platform. The MAML algorithm was implemented using PyTorch 1.13.1 on the PyCharm 2020.1\(\times\) 64 platform. In the process of short-time Fourier transform toward one-dimensional time-domain sequence, we set the time-domain segment length to be 512 sampling points and the overlap length between adjacent Windows to be 440 sampling points.  
For the eight kinds of single jamming used for meta-training nodes, we generated 100 sets of signals under each JNR at 2 dB intervals as the meta-training datasets.In order to better conform to the real communication environment, the settings for generating single tone jamming are shown in Table 1.  
For four kinds of compound jamming signals to be classified, we generated 200 groups of signals under each JNR at 2 dB intervals as the test set. Then, we extracted 32 sets of data from each class as the support set for the test set, which is used to fine-tune the network parameters to achieve the four-way 32-shot classification task. After that, we extracted 100 groups of data from each class under each JNR as the query set of the test set (the query set and the support set are not repeated) for testing the classification performance. Each group of the query set contains 21 time-frequency images generated by adding noise to the same jamming signal, which were used to simulate the compound jamming signal received  
Figure 9: Diagram of multi-node collaborative compound jamming recognition algorithm based on MAML and time–frequency analysis.  
by multiple nodes. The parameter setting of the compound jamming signal is shown in Table 2. \(W_{r}\) represents the power ratio of two single jamming signals in the composite jamming signal; the value range is from 0.8 to 1.2. The purpose is to avoid a single jamming signal component significantly suppressing another. \(F_{b}\) represents the band relationship between two single jamming signals in a composite jamming signal, and its value is \(\varnothing\). This is also for avoiding a single jamming signal component significantly suppressing another. Finally, we set the learning rate of the inner loop to 0.05, the learning rate of the outer loop to 0.001, and the training epoch to 10,000.  
In the MAML training process, the amount of samples of each type is very limited. In order to make the network suitable for various types of tasks, more types of tasks would be required for the training process. However, since the category of single jamming signals used for training in this article is also very limited, expanding the dataset is a necessary operation. The Omniglot dataset is a classic few-short learning dataset, which includes 1623 handwritten characters in 50 different languages, with 20 samples for each character [21]. As shown in Figure 10, character recognition is also realized through the characteristics of lines, which is similar to the time-frequency diagram of compound jamming. Therefore, extending the dataset in this article with the Omniglot dataset is valid.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Type** & **Bandwidth Range** & **Parameters Setting** \\ \hline Sampling Frequency \(f_{s}\) & — & 50 MHz \\ Single Tone Jamming & — & Random Carrier Frequency \\ Multi-Tone Jamming & — & Random Tone Amounts: 3–15 \\ Partial Band Noise Jamming & 0.03\(\sim\)0.8 & — \\ Pulse Jamming & 0.04\(\sim\)0.98 & Duration Time: 20 μs\(\sim\)120 μs \\ Comb Spectrum Noise & — & Random Tone Amounts: 3–15 \\ Linear Frequency & 0.04\(\sim\)0.8 & — \\ Modulation Jamming & 0.02\(\sim\)0.2 & — \\ Sinusoidal Frequency & 0.04\(\sim\)0.8 & — \\ Modulation Jamming & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting of eight single jamming signals.  
Figure 10: Comparison of the compacted jamming signal with the Omniglot dataset.