## 1 Introduction  
As a form of information transmission, human activity recognition (HAR) has significant application value in security, counter-terrorism, disaster rescue, smart homes, intelligent driving, and traffic safety [1]. Traditional HAR technologies mainly rely on optical sensors and wearable devices [2]. Optical sensors, such as cameras, can provide intuitive visual information but are limited in low-light or occluded environments, and pose significant privacy concerns. Wearable devices, while capable of providing continuous physiological data monitoring, are highly intrusive and require the user to wear them constantly, which can affect user comfort and acceptance. In contrast, radar-based HAR can demonstrate unique advantages. IR-UWB radar is an ultra-wideband radar that uses extremely short pulses to transmit data. When operating in the low-frequency band, the radar waves it emits can penetrate walls and other obstacles under non-contact conditions, achieving non-line-of-sight behavior recognition. This effectively addresses the challenge of HAR in complex occlusion environments where cameras are not viable, while also protecting personal privacy [3]. Due to its extremely narrow pulse characteristics, IR-UWB radar has a very high range resolution, sufficient to distinguish subtle human movements. Moreover, because signal transmission relies on brief pulses, IR-UWB radar has low power consumption, making it highly advantageous for constructing lightweight HAR systems [4]. The process of through-wall HAR using IR-UWB low-frequency radar consists of four main steps: clutter suppression, signal transformation, feature extraction, and classifier recognition. The echo data obtained from pulse radar are in the form of a two-dimensional range-time (RT) matrix, which directly reflects information about the target's spatial position and temporal variations. By performing clutter suppression on the raw data, an RT matrix predominantly containing human motion information can be obtained. Feature visualization operations on the RT matrix result in a range-time map (RTM). It is possible to skip the signal transformation step and directly perform feature extraction on the RTM. For instance, [PERSON] et al. [5] argued that the transformed signal data might not effectively represent the intrinsic features of the movement and, therefore, they directly used the RTM as the input feature for their proposed method, an autoencoder self-organized mapping network. However, the mainstream perspective still holds that frequency information can reveal the periodic characteristics of signals and is key to analyzing signal variations. The micro-movements of different actions vary, leading to different micro-Doppler frequency features for different actions. By leveraging this characteristic, performing time-frequency transformation on the filtered RT data can yield a time-Doppler map (TDM), which depicts different actions. In the past, the features used for classification and recognition were typically manually extracted from TDMs and then input into classifiers built with traditional machine learning (ML) algorithms. Additionally, applying a Fourier transform to each range cell in the RT matrix results in a range-Doppler map (RDM), which reflects the relationship between the action frequency and range. However, the RDM is rarely used as a standalone feature map and is often fused with RTM and TDM features. Radar-based HAR methods relying on manual feature extraction are heavily dependent on experience-based heuristic algorithms and prior knowledge. The selected features are often shallow features, which means the final recognition performance is significantly influenced by the quality of the extracted features and their independence. In recent years, deep learning has achieved remarkable success in feature extraction across multiple research fields, such as image recognition [6], image segmentation [7], and speech recognition [8].