Ra-SPD: Radar Signal Interference Mitigation Using Spectral-Spatial Decomposition

With the continuous evolution of radar RF sensing technology in the Internet of Things (IoT) field, the deployment of co-frequency communication devices within smart home and healthcare environments is becoming increasingly prevalent. Concurrently, the proliferation of RF signals has rendered the challenge of spectrum resource allocation increasingly prominent, exacerbating the issue of severe mutual interference among these co-frequency devices. This severe interference exhibits intricate attributes, characterized by prolonged duration, a broad frequency range, and high level power intensity. When experiencing interference, the target’s echo is notably veiled, rendering recovery through standard technologies exceedingly challenging. From our perspective, in the presence of severe interference, the emphasis ought to be on elegantly reconstructing the target’s echo located in the period of interference, rather than merely eliminating the negative impact on interferences from the raw radar signals. Based on this thought, we have explored the feasibility of a deep network model for interference mitigation of radar signals in this paper, and introduced an interference mitigation method, namely Ra-SPD. The Ra-SPD is crafted through a dual design methodology, integrating the mask-guided spectral decomposition mechanism alongside the region-aware spatial decomposition scheme. The primary objective of the initial design is to enhance the capabilities of the standard deep model, enabling it to distinguish the duration of interference present in radar signals, particularly when the semantic alignment between the restored radar signal and the corresponding ground truth is at risk of being disrupted. Meanwhile, the improvement of local information related to the restored radar signal is accomplished using the spatial decomposition scheme, aimed at increasing the robustness of low-intensity signal segments against severe interference. Experimental results demonstrate superior performance of Ra-SPD at 15%, 40%, and 80% three different signal-to-interference ratio conditions, achieving PSNR: 35.73/32.99/30.28 dB; SSIM: 0.96/0.95/0.92; FID: 23.41/30.97/43.92; and MAE: 0.014/0.024/0.036 across conditions. Our method consistently outperforms six benchmark algorithms in all objective metrics and subjective evaluations, exhibiting 7.6%, 1.5%, 5.5%, and 17.2% average improvements in four metrics relative to the suboptimal method, highlighting significant advantages in interference mitigation and signal quality preservation.