## 3 Experiments
### Measured Experiments  
Measured experiments are conducted to validate the effectiveness of the proposed method in real-world scenarios. The experimental setup is based on a closed road, as illustrated in Figure 13. One TI AWR1243 radar is selected as the victim radar, while the TI AWR1243 and TI AWR1642 radars are selected as the interfering radars, which are placed at about 1 m and 2 m away from the victim radar, respectively. The former is used for target detection, while the latter two radars are used for transmitting interference signals. A corner reflector serves as a target at a distance of approximately 10 m from the victim radar. The victim radar also receives the signals reflected from the two interfering radar boards, therefore the two interfering radar boards behave as two stationary targets. The specific radar parameter settings are presented in Table 4. For the victim radar, the initial frequency is set at 77.0 GHz. Each frame comprises 128 chirps, and each chirp contains 1024 sampling points. Figure 12: Comparison of RD maps for different interference suppression methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **WD** & **ANC** & **BiGRU** & \
\begin{tabular}{c} **ONNX** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Deployed** \\ **with** \\ **TensorRT** \\ \end{tabular} \\ \hline PSINR (dB) & 10.53 & 10.73 & 17.435 & 20.248 & 20.247 \\ SINR (dB) & \(-\)0.88 & \(-\)8.60 & 7.614 & 10.716 & 10.715 \\ Time (ms) & 6.58 & 9.32 & 20.15 & 14.82 & 4.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of quantization results and inference times for different interference suppression methods in the simulation experiments. The time-domain waveforms of the interfered signal and the interference-free signal are shown in Figure 14, where the chirp-like interferences can be observed. There are two interference scenarios where the target signal is contaminated by one interference and two interferences, respectively. The strong interferences increase the noise floor in the range profiles and mask the weak targets, as illustrated in Figure 14c,d. The range profiles after applying different interference suppression methods are shown in Figure 14e,f. The ANC method uses the negative frequency component of the IF signal as the reference channel and utilizes a least mean squares algorithm to solve for the optimum filter solution. Therefore, the ANC method preserves the amplitude of the target signal. The amplitude of the target signal restored by the ANC method is higher than the signals of the WD method, the BiGRU-based method, and the proposed method. However, there is residual interference in the range profiles of the ANC method in Figure 14e,f. In scenario 2 with two interferences, the ANC method slightly reduces interferences. The WD method and the BiGRU-based method suppress the interference but cause power loss of the target signal. In addition, the performance of the WD method degrade when the percentage of samples contaminated by the interference increases, as illustrated in Figure 14f. In contrast, the proposed distilled lightweight RIMformer effectively mitigates interferences and recovers the target signal in both scenarios 1 and 2. The quantitative results of interference suppression are listed in the Table 5. The proposed distilled lightweight RIMformer has better PSINR than the other three methods. The lightweight improvement weakens the performance to some extent, but this trade-off in performance and complexity is acceptable. Despite the differences in radar parameters, the network trained solely with simulated data still demonstrates effective interference suppression performance in measured radar signals.