## 1 Introduction  
A radar is a robust sensor used in environmental sensing applications, such as autonomous driving and remote sensing, which enables target detection even under adverse weather conditions [1, 2]. Frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar systems offer a higher resolution and have lower manufacturing costs than conventional radars [3, 4, 5]. However, the mass deployment of FMCW radar systems in congested areas has led to escalating concerns regarding interference between the radar systems [6]. The limited spectrum poses serious challenges for reliable target detection in interference environments. Figure 1 illustrates the scenario in which mutual interference occurs. Mutual interference is incoherent when the parameters of the FMCW radar systems are different, which is the most common scenario. Incoherent interference degrades the target detection accuracy and poses an accident risk. Therefore, mitigating the interference in radar images is necessary for reliable target detection. Various signal processing approaches have been  
developed to suppress incoherent interference, including traditional signal processing approaches and deep learning (DL)-based approaches. These approaches work by detecting, separating, and filtering interference signals in various domains [6]. An adaptive noise canceller (ANC) has been used to reduce interference in the frequency domain through the least mean squares algorithm [7]. In [8], wavelet denoising (WD) was applied to extract the interference signal. A constant false alarm rate detector (CFAR)-based framework was proposed in [9] to mitigate interference for FMCW radars. In the framework, interference was detected and eliminated in the time-frequency domain. [PERSON] et al. [10] presented a Matrix Pencil (MP)-based interference suppression method, exploiting the fact that the desired beat signals were the sum of exponential sinusoidal components and the interferences were the sum of chirp-like waveforms. In [11], the received radar signal was partitioned into sub-bands, and then the interference was mitigated in each sub-band. In addition, several works have investigated signal decomposition-based approaches. In [12], the interference signal was decomposed into a set of components via empirical mode decomposition (EMD), and the interference-dominated components were determined by the consecutive mean squares error (CMSE) algorithm. Variational mode decomposition (VMD) was also used to decompose the received radar signal and to separate the target signals from the interference [13]. Subsequently, an interference suppression method based on outlier detection and VMD was proposed [14]. In [15], a sparsity-based technique was presented for radar interference suppression, and the alternating direction method of the multiplier-based technique was applied to restore the radar echoes. An interference mitigation optimization problem was designed in [16] to extract the target echoes with a row-sparse constraint by taking advantage of the sparsity of the interference signal in the time domain and the row sparsity of the useful echo signal in the frequency domain. In [17], a bi-level-based signal optimization model was derived by using L1-norm penalized least squares to reduce interference. Recently, the effectiveness of DL-based interference suppression methods has been demonstrated. Recurrent neural networks (RNNs) have been used to deal with interference in the time domain [18; 19]. In [20], the interference was mitigated by using a generative adversarial network (GAN). Another type of strategy was to convert the interfered signal into the time-frequency domain and perform two-dimensional processing. In [21], a complex-valued fully convolutional network (CV-FCN)-based architecture effectively suppressed the interference by exploiting prior features as a regularization term.