To address the challenge of catastrophic forgetting in synthetic aperture radar (SAR) image recognition caused by viewpoint-sensitive, high-interference samples encountered in dynamic environments, we propose a lightweight and efficient online class-incremental learning (OCI) framework named Interference-Aware Replay with Dynamic Review for SAR Target Recognition (IAR-DR). Based on the experience replay (ER) mechanism, a Maximally Interfered Retrieval (MIR) strategy is designed to prioritize the replay of high-interference samples by measuring loss changes before and after model updates, thereby preserving decision boundaries under viewpoint variation. A Review Trick (RT) mechanism is further introduced to periodically revisit all buffered samples with a low learning rate, which complements MIR by reinforcing global feature retention and enhancing long-term memory stability. The combination of MIR and RT achieves a synergistic balance between local discrimination and global generalization, mitigating the forgetting effect while maintaining the efficiency. Extensive experiments conducted on the MSTAR and Bistatic MiniSAR datasets demonstrate that the proposed IAR-DR framework maintains high recognition accuracy while achieving a forgetting rate as low as 6.92% in ablation studies, and improving retention by 4.7% over recent SAR class-incremental methods.

Obstructive sleep apnea (OSA) is associated with psychophysiological impairments, and recent studies have shown the feasibility of using speech and craniofacial images during wakefulness for severity estimation. However, the inherent limitations of unimodal data constrain the performance of current methods. To address this, we proposed a novel hypergraph-based multimodal fusion framework (HMFusion) that integrates psychophysiological information from audio-visual data. Specifically, we employ long short-term memory (LSTM)-based encoders to extract modality-specific temporal dynamics from pre-trained audio-visual embeddings and remotely photoplethysmography (rPPG)-derived heart rate sequences. A hypergraph neural network is then utilized to capture critical cross-modal interactions for OSA severity estimation. Evaluation on a dataset of 159 participants from a clinical sleep center demonstrates that the proposed model achieves area under the receiver operating characteristic curves (AUCs) of 88.26%, 86.07%, and 85.29%, with corresponding F1-scores of 92.91%, 85.50%, and 85.30% at Apnea-Hypopnea Index (AHI) thresholds of 5, 15, and 30 events/hour, respectively, outperforming state-of-the-art approaches. This study highlights the potential of psychophysiological data in enhancing OSA severity estimation during wakefulness, offering new avenues for clinical research in this field.

Multi-view clustering leverages complementary information across views but struggles with scalability and robust inter-view fusion. We propose DREAM, a novel binary multi-view clustering method that integrates dual high-order random walks to enhance performance. First, multi-view data are mapped to bipartite graphs via RBF, then refined by high-order random walks to mitigate anchor sensitivity. Projection matrices are regularized by Enhanced Tensor Rank (ETR) for inter-view synergy, Bregman divergence for intra-view specificity, and adaptive weighting for view contribution balancing. Cluster labels are directly derived from the optimized binary codes. Experiments on four benchmarks show DREAM outperforms eight state-of-the-art methods, achieving up to 22.2% higher ACC and 29.7% higher NMI on biological datasets. The source code is available at https://github.com/HsuehBiao/DREAM.

Due to the characteristics of light weight, low cost, and high resolution, millimeter wave (MMW) multiple-input multiple-output (MIMO) radars are widely applied in remote sensing and automotive systems. The MMW MIMO radar orthogonal waveform design is a key issue based on dechirp-on-receive technique to acquire high degree of freedom (DOF). In this paper, we propose an optimized interleaved orthogonal frequency division multiplexing (I-OFDM) chirp waveform design scheme using unequal sub-chirp duration and sparse sub-band constraint to further reduce the mutual interference (MI) between waveforms, and analyze the orthogonality of the original and optimized I-OFDM chirp waveform for MMW MIMO radar based on dechirp processing from various aspects. The simulation results show the effectiveness of the proposed method.

With the development of miniaturized millimeter wave (MMW) frequency-modulated continuous-wave (FMCW) radar, the dechirp-on-receive technique has been widely used. Due to the limitations of highly integrated radar hardware, it is difficult to further increase the bandwidth of the transmitted signal. Therefore, enhanced range resolution in MMW synthetic aperture radar (SAR) imaging can be achieved only thanks to suitable post-processing. In this paper, we propose a method for range resolution enhancement based on the principle of wavenumber shift with application to cross-track miniature MMW multiple-input and multiple-output (MIMO)-SAR systems. An improved orthogonal waveform design scheme of multi-subband chirp waveforms with chirp rate changes between waveforms is proposed, which is suitable for dechirp processing. In addition, given the constraint of the position of the equivalent SAR platform of MIMO-SAR, which leads to the lack of range-dimensional spectrum, a spectral data interpolation method based on autoregressive (AR) modeling in the time-frequency (TF) domain is proposed. The effectiveness of the proposed method is verified by numerical simulation and experimental data processing.

Although numerous classification algorithms based on deep learning can effectively extract valuable features from synthetic aperture radar (SAR) images to improve the accuracy of SAR automatic target recognition, most of them have rigorous constraints in reality and fail to obtain satisfying results with limited training samples. To address the above issues, we propose a novel multi-view attention capsule network for SAR target small sample recognition. In our method, after gaining the primary capsules of SAR images under different views, a capsule-based view attention module is designed to enhance the feature relations between different views. Then a joint dynamic routing mechanism is adopted to further capture the robust inter- and intra-image spatial relations to generate SAR capsules specialized in the final classification. Especially, to alleviate the impact of SAR target angle sensitivity on recognition, rotated cropping is applied to the original SAR images in advance. Finally, experimental results on the moving and stationary target recognition (MSTAR) and OpenSARShip dataset have demonstrated the superiority and robustness of the proposed method upon limited labeled samples.

The orthogonal waveform diversity design and efficient hardware implementation are important issues in miniature multiple-input multiple-output (MIMO) radars. The orthogonal frequency division multiplexing (OFDM) chirp waveform has received attention recently because of its large time-bandwidth product, constant modulus, no range-Doppler coupling, good orthogonality, and good Doppler tolerance. The dechirp-on-receive technique can reduce the amount of raw sampled data in near-field miniature millimeter-wave (mmW) MIMO radar detection and synthetic aperture radar (SAR) imaging. However, most of the current waveform diversity design methods are based on general matched filtering (MF). In this paper, the possibility of using the traditional OFDM chirp waveform for dechirp processing at the receiving end of MIMO radar is analyzed. Then, the results of different configurations of chirp rates within and between transmitted waveforms for different signal processing procedures are investigated. A novel dechirp-based OFDM chirp waveform diversity design method for MIMO radar is proposed, and the results of the waveform design are given. Numerical results, such as pulse compression (PC) results, dechirp ambiguity function (DAF), SAR imaging processing, etc., and experiments verify the effectiveness of the proposed methods.

Deep learning models have achieved remarkable performance in synthetic aperture radar (SAR) target recognition. However, the accuracy of these methods is sensitive to the hyper-parameters and the traditional backpropagation is time consuming. In this letter, we proposed a modified convolutional random vector functional link (IntCRVFL) network for SAR target recognition, which can simplify the SAR target recognition system. The CRVFL network consists of a convolutional neural network and an RVFL network. First, the fixed convolutional layers with randomly initialized parameters extract SAR image features and then the RVFL network performs target recognition. Especially, inspired by hyperdimensional computing, the activations of the hidden layer are obtained through a new encoding manner. Besides, only the connections between hidden and output layers need to train by a closed-form solution for the ultimately precise target recognition. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the proposed IntCRVFL network can obtain a satisfying accuracy with a faster speed.

Polarimetric synthetic aperture radar (PolSAR) image classification is an essential part of PolSAR image interpretation. In recent years, convolutional neural networks (CNNs) have made significant advances in PolSAR image classification. However, the current CNN-based methods ignore complementary information among different feature maps and correlations between elements of coherence matrix, which can provide discriminative information for classification. Besides, the phase information contained in the complex-valued (CV) coherence matrix cannot be extracted effectively. In this letter, a stacked CV convolutional long short-term memory (ConvLSTM) network called CV-ConvLSTM is proposed for PolSAR classification. Compared to existing methods, CV-ConvLSTM can extract complementary information among different feature maps and utilize the dependencies of elements in the coherency matrix, which can improve the performance of classification. In addition, the CV operations are added to the network, in which phase information is used for better classification. The experimental results of two widely used PolSAR datasets demonstrate that CV-ConvLSTM can obtain superior performance compared with existing CNN methods.

The orthogonal waveform design and good hardware realization of multiple-input–multiple-output (MIMO) radar have always been important research topics. The orthogonal frequency division multiplexing (OFDM) chirp waveform has received more attention because of its large time-bandwidth product, constant modulus, no range-Doppler coupling, good orthogonality, and good Doppler tolerance. Dechirp technique can reduce the amount of raw sampled data very well in near-field miniature lightweight MIMO radar detection and synthetic aperture radar (SAR) imaging. However, most of the current waveform analysis methods are based on matched filtering (MF). In this letter, an ambiguity function (AF) based on the dechirp signal processing approach to analyze the waveform performance is proposed, called dechirp ambiguity function (DAF). The pulse compression performance of the waveform itself and the level of mutual interference between the waveforms are described from the perspective of DAF. Numerical results validate reliability and effectiveness of the DAF.

Respiratory sound is able to differentiate sleep stages and provide a non-contact and cost-effective solution for the diagnosis and treatment monitoring of sleep-related diseases. While most of the existing respiratory sound-based methods focus on a limited number of sleep stages such as sleep/wake and wake/rapid eye movement (REM)/non-REM, it is essential to detect sleep stages at a finer level for sleep quality evaluation. In this paper, we for the first time study a sleep stage detection method aiming at classifying sleep states into four sleep stages: wake, REM, light sleep, and deep sleep from the respiratory sound. In addition to extracting time-domain features, frequency-domain features of respiratory sound, non-linear features of snoring sound are devised to better characterize snoring-related signals of respiratory sound. To effectively fuse the three sets of features, a novel feature fusion technique combining the generalized canonical correlation analysis with the ReliefF algorithm is proposed for discriminative feature selection. Final stage detection is achieved with popular classifiers including decision tree, support vector machines, K-nearest neighbor, and the ensemble classifier. To evaluate our proposed method, we built an in-house dataset, which is comprised of 13 nights of sleep audio data from a sleep laboratory. Experimental results indicate that our proposed method outperforms the existing related ones and is promising for large-scale non-contact sleep monitoring.