# Multi-Source HRRP Target Fusion Recognition Based on Time-Step Correlation
## 3 Experimental Results and Analysis
### Recognition Performance  
In this section, the recognition performance of each model is tested on ship HRRP data sets of different frequency bands. For the convenience of recording, different frequency bands are numbered, as shown in Table 2. The set of A1-A5 is denoted as A, and the set of B1-B5 is denoted as B. The recognition effects of various models are tested on the HRRP datasets in different frequency bands. At the same time, the HRRPs in different frequency bands under the same bandwidth are fused by TCFF method, and compared with the fusion methods of the element addition and element contacting, respectively. The experimental results are shown in Figure 8a-g. As shown in Figure 8, the A curve indicates the recognition results using only A1-A5 single data set without fusion processing, where the abscissa 50 MHz corresponds to A1, 100 MHz corresponds to A2, and so on; the marking of the B curve is similar to A. The 'element addition', 'element contacting' and 'TCEF' represent the results of fusion recognition using two datasets. For the 'element addition' curve abscissa, 50 MHz corresponds to the feature fusion of the A1 and B1 datasets, 100 MHz corresponds to the feature fusion of A2 and B2, 150 MHz, 200 MHz, 250 MHz corresponding data sets and so on. The annotation of the curve of the element contacting method and the TCFF method is similar to the element addition method. Figure 8 records the recognition accuracy of each model in different frequency bands, and the recognition results of the HRRP data feature fusion in different frequency bands with the same bandwidth. It can be seen from the figure that under the condition of a certain signal-to-clutter ratio, in the frequency band of 3-300 MHz bandwidth, the influence of small changes in carrier frequency on recognition performance can be ignored when the bandwidth is the same. Therefore, under the condition of ignoring the influence of carrier frequency, when the bandwidth increases, with the improvement of radar range resolution, the number of scattering points falling into the same range unit decreases, the description of target details by the high resolution range profile is more detailed, and the recognition accuracy is also improved, especially in the range of 50-200 MHz. When the bandwidth increases to 200 MHz, the recognition accuracy is basically stable at an optimal value, and the increase in bandwidth has little effect on the recognition accuracy. On the other hand, the increase in bandwidth leads to the decrease in the length of the range cell, which enhances the detail description of the target HRRP, and it is also more likely to cause the phenomenon of moving across the range cell and destroy the stability of the range profile, which may lead to the decrease in recognition performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Operational Frequency Band (GHz)** & **Bandwidth (MHz)** \\ \hline A1 & 2.85–2.90 & 50 \\ A2 & 2.85–2.95 & 100 \\ A3 & 2.85–3.00 & 150 \\ A4 & 2.85–3.05 & 200 \\ A5 & 2.85–3.10 & 250 \\ B1 & 3.10–3.15 & 50 \\ B2 & 3.05–3.15 & 100 \\ B3 & 3.00–3.15 & 150 \\ B4 & 2.95–3.15 & 200 \\ B5 & 2.90–3.15 & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Corresponding numbers of ship target HRRP data sets in different frequency bands. Figure 8: The recognition accuracy of different model in different frequency bands and the recognition accuracy after feature fusion. (**a-g**) indicates the LSTM, RNN, GRU, Transformer–Encoder, SRU, SRU–Transformer, R–DeLighT model. It can also be found from the figure that by adding an input channel to increase the amount of target information, the feature fusion method of element addition and element contacting can achieve better recognition performance than a single input channel on RNN, LSTM, GRU, SRU and Transformer feature extractors.