## 2 System Model  
### The Baseline Model  
In an FMCW mmWave radar system, target detection is based on the time delay between the transmitted and received echo signal. During a chirp cycle, the received echo signal \(s_{r}(t)\) is mixed with the transmitted signal \(s_{\mathrm{T}}(t)\) and low-pass filtered (LPF) to produce an intermediate frequency (IF) signal. The interfered IF signal \(s_{\mathrm{IF}}(t)\) with interference \(s_{\mathrm{int}}(t)\) and noise \(s_{n}(t)\) can be expressed as follows:  
\[s_{\mathrm{IF}}(t)=\mathrm{LPF}\{[s_{r}(t)+s_{\mathrm{int}}\left(t\right)] \cdot s_{\mathrm{T}}^{*}(t)\}+s_{\mathrm{n}}(t), \tag{1}\]  
where the superscript \(*\) denotes the complex conjugate of the signal [9]. As shown in Figure 2, interferences introduce significant fluctuations in the time domain and have a broad influence across the entire bandwidth. Consequently, the interference elevates the noise floor and obscures peaks in the range profile, which degrade the accuracy of target detection. In [25], we introduced a transformer-based framework named RIMformer to mitigate interferences in FMCW mmWave radar systems. The proposed RIMformer is designed with an end-to-end architecture, which can deal with the interfered radar signals in the time domain. As depicted in Figure 3, the network adopts a typical encoder-decoder structure and comprises the following five main parts: input segmentation; channel expansion; feature extraction; signal reconstruction; and the merging of reconstructed signal frames. The proposed RIMformer block acts as an encoder and decoder at different stages. The encoder extracts features from the intermediate frequency signals during the feature extraction phase. The decoder reconstructs the target signal using key feature maps in the signal reconstruction phase. A distinctive aspect of the RIMformer block structure is its use of dual-path self-attention calculations combined with convolutions for local feature extraction, which improves the performance of the network in terms of capturing the features of the beneficial components of the interfered signals at different scales. Figure 2: The IF signal with interferences in the time domain and resulted range profile. The structure of the RIMformer block is shown in Figure 4. The IF signal of the radar system is first transformed by a sliding window algorithm into a structured format consisting of the frame number, the frame length, and the channel. Each frame represents a localized portion of the signal and preserves temporal continuity through the overlap. This operation transforms the 1D signal into structured 2D data, allowing for the localized analysis of the adjacent frame. After the sliding window operation, the reassembled data block is then expanded along the channel dimension using a 1D convolution layer. This increases the feature richness by projecting each frame into a higher-dimensional feature space. These segmented signals are analyzed along the frame number and length, with attention mechanisms computed within each frame (intra-frame) and across the frames (inter-frame). Intra-frame attention captures short-term signal correlations, whereas inter-frame attention reflects long-term dependencies across the sequence. The normalized attention results are combined with the original input via residual connections, forming the final output of the attention layer. The next layer is a fully connected feedforward network (FFN), including a hidden layer and an activation function for non-linear transformation of the features at each position. The output from the FFN is subjected to layer normalization and residual connections to ensure data stability. The introduction of the convolutional block enables the use of CNNs for local feature extraction. Each block comprises two convolutional layers, a linear layer, and several activation functions. The gated linear unit (GLU) [37] is positioned between the two convolutional layers.