Radar-based human activity classification is crucial for applications such as healthcare monitoring, fall detection and assisted living due to its superior sensing capabilities and privacy protection. Traditional classification methods generally retrieve features from the time-range domain or the time-frequency domain. Such two-dimensional representation neglects the underlying dependence between the three radar signal variables of time, range and Doppler frequency, and cannot fully depict the dynamic human motion features. In this paper, we propose a time-range-Doppler radar point clouds (RPC)-based learning model for human activity classification using a frequency-modulated continuous waveform (FMCW) radar. The human echoes are first transformed into a series of three-dimensional (3D) point cloud cubes integrating the motion signatures in three domains, namely time-range, time-Doppler, and range-Doppler domains. The generated RPC cubes are then fed into a newly developed two-layer convolutional multilinear principal component analysis network (CMPCANet) for feature extraction and motion classification. The CMPCANet comprises a simple network architecture with small training parameters, and can be directly implemented on the 3D tensor dataset to extract highly discriminative features. Experimental results demonstrate that proposed framework can achieve superior classification accuracy and noise robustness compared to other methods using multi-domain information, even with small training samples.

Due to the ability to handle low-light environments, poor weather conditions and privacy protection, person identification based on radar micro-Doppler (m-D) signatures has emerged as a research interest. Moreover, as it is always difficult to construct large-scale radar data sets, we propose an approach to recognize a person’s identity with limited training sample. In which, the fine m-D signal processing technology is first used to obtain high-quality m-D data spectrograms. A three-layer convolutional principal component analysis network with a dimension optimization architecture (CPCAN-3) is then designed to learn the highly discriminative features and address the identification problem. The model has few network parameters, is easy to train, and has low dependence on a large number of training samples. Different daily activities data are captured in an indoor environment to evaluate the performance of proposed method, and the state-of-the-art algorithms are chosen for comparison. The experimental results show that the proposed scheme performs better performance than the others on small data sets. Especially when the motion of “running” is adopted to identify persons, the model achieves 98% accuracy on the identification of ten people.

In the low-altitude urban background with heavy interference, especially in the face of corner interference with higher intensity than the target, infrared (IR) dim and small target is extremely lack of prior information (i.e., size, shape and contrast information). In such case, the existing detection methods usually suffer from high false alarm or even failure. To deal with this situation, we develop a novel spatial-temporal tensor model with tensor spectral k -support norm minimization (STTM-TSNM) for detecting IR dim and small target. Firstly, the spatial-temporal information of the original image sequence can be preserved completely by constructing the holistic STTM. Then, according to the spatial-temporal related prior knowledge of the target and background, the target detection task is customized as an optimization problem of low rank and sparse tensor recovery. To better preserve the internal structure and capture more global information, the tensor spectral k -support norm minimization is introduced as the regularization term of the constraint background. Finally, draw support from the framework of alternating direction method of multipliers (ADMM) algorithm, the precise separation of target and background is achieved. In addition, to promote the prosperity of sequential detection methods, we released to the scientific community a small IR target dataset containing six image sequences with urban background. The experimental results on six real IR sequences demonstrate that our method outputs the most outstanding detection performance compared with the latest sequential detection methods.

Passive radar often suffers from the direct-path and multipath clutter interference, causing the target echo to be submerged by clutter, so the clutter must be suppressed at first. A novel approach to suppress clutter effectively is proposed. Firstly, the reference and surveillance signals are divided into multiple short segments, and then the segmented signals are transformed from time domain to frequency domain. Subsequently, clutter suppression is performed on each frequency bin. Finally, the surveillance signal after clutter suppression is transformed back to the time domain. Compared with conventional clutter suppression methods, the proposed method does not require the high-order computation adopted by adaptive filtering, thus reducing the computational complexity significantly. Furthermore, the proposed method is not confined to orthogonal frequency division multiplex (OFDM) signal, making it widely applicable. The superiority of the proposed method is demonstrated by application to simulated and experimental data.

Passive radar systems, which utilize external illuminators such as broadcast or communication signals, have garnered growing interest due to their cost-effectiveness, low detectability, and anti-jamming capabilities. They typically rely on micro-Doppler (MD) features generated by moving components (e.g., rotors or vibrational elements) for target identification. However, strong noise interference in passive radar environments degrades the quality of MD signals (MDSs). Additionally, the rapid instantaneous frequency (IF) variations exhibited by MDSs result in blurred time-frequency (TF) representations (TFRs), thereby complicating feature extraction. To address these challenges, this article presents an enhanced TF analysis (TFA) method based on the multisynchrosqueezing transform (MSST). Through the definition of squeezing bandwidth (SB) and the incorporation of constant false alarm rate (CFAR) processing, the proposed method enhances TFR clarity and noise robustness. Building upon the principle of limited IF ridge mutation, we further develop a novel IF ridge extraction scheme employing SB to precisely characterize the IF of MDSs under noisy conditions. Comprehensive simulations and experimental evaluations validate the method’s effectiveness in capturing micromotion features and processing nonstationary signals.

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost.