Gesture recognition with millimeter-wave radar has broad application prospects in human-computer interaction. However, traditional recognition methods generate overly redundant features and construct large-scale networks, rendering them unsuitable for embedded devices with limited memory. To address this challenge, we propose a sparse-driven dynamic gesture recognition network in millimeter-wave radar, named TCNFormer (Time Convolutional Network and transFormer). Firstly, we employ a two-dimensional fast Fourier transform (2D-FFT) to obtain the range-Doppler maps (RDM). These maps are then processed through incoherent integration of multiple frames to produce Doppler-time maps (DTM). We subsequently use the orthogonal matching pursuit algorithm to achieve a sparse representation of the Doppler-time trajectories and integrate the RDM to extract the range features of gestures, obtaining the multi-dimensional sparse sequences encompassing the range-Doppler-time feature. We then design a TCNFormer network tailored to these multi-dimensional sparse sequences. This network leverages a shallow TCN to learn local features, a Transformer network to capture global features and an adaptive weighting method to fuse these local and global features effectively. Experimental results demonstrate that our network fully exploits the sparse multi-dimensional sequences, achieving a recognition accuracy of 99.17% on a self-built dataset. The parameter size of the network is only 0.13M, significantly outperforming existing state-of-the-art models in relevant metrics, thereby proving its suitability for embedded applications in human-computer interaction.

In passive radar, the multiple model probability hypothesis density (MM-PHD) filter has demonstrated robust capability in tracking multi-maneuvering targets. Nevertheless, non-uniform clutter in practical scenarios causes misestimation of component weights, thereby generating false targets. To solve the false targets problem, a feature-matching MM-PHD (FM-MM-GM-PHD) algorithm for passive radar tracking is proposed in this paper. First, the measurement likelihood function was refined by leveraging target radar cross-section (RCS) and Doppler features to assist in suppressing false targets and reduce clutter interference. Additionally, the proposed algorithm incorporated adaptive component pruning and absorption processes to enhance tracking accuracy. Finally, a missed-alarm correction mechanism was introduced to compensate for measurement losses. Simulations of the passive radar results validated the findings that the proposed algorithm outperformed the traditional MM-PHD filter in both tracking accuracy and cardinality estimation. This superiority was particularly pronounced in non-uniform clutter environments under low detection probabilities.

In this paper, we investigate the memory effects introduced by the time-fractional Schrödinger equation proposed by Naber on quantum entanglement and quantum dense coding under amplitude damping noise. Two formulations are analyzed: one with fractional operations applied to the imaginary unit and one without. Numerical results show that the formulation without fractional operations on the imaginary unit may be more suitable for describing non-Markovian (power-law) behavior in dissipative environments. This finding provides a more physically meaningful interpretation of the memory effects in time-fractional quantum dynamics and indirectly addresses fundamental concerns regarding the violation of unitarity and probability conservation in such frameworks. Our work offers a new perspective for the application of fractional quantum mechanics to realistic open quantum systems and shows promise in supporting the theoretical modeling of decoherence and information degradation.

Hybrid deep learning frameworks integrating convolutional neural networks (CNNs) and Transformer-based attention mechanisms have recently achieved promising results in synthetic aperture radar (SAR) ship detection. However, they still suffer from inherent limitations, including missed detections of small-scale ships under large variations in target scales, multiplicative speckle noise, and complex sea clutter interference. To address these issues, this paper proposes ACmixSAR-YOLO11, a SAR ship detection framework built upon YOLO11. The proposed model incorporates three complementary improvements to enhance feature representation and multi-scale detection capability. First, an ACmixSAR module is designed by integrating convolutional operations with self-attention mechanisms. This design strengthens feature discriminability. Second, a SARNoiseGate gating mechanism is embedded into the ACmixSAR module, which is designed to suppress low signal-to-noise ratio (SNR) feature responses while preserving discriminative ship features. Third, we develop a scale-weighted complete intersection over union (SW-CIoU) loss function, which assigns adaptive regression weights based on target sizes to rebalance the optimization across small, medium, and large ships. All improvements below are reported as relative percentage changes. Experimental results on the SSDD dataset demonstrate that the proposed ACmixSAR-YOLO11 outperforms the original YOLO11 Baseline, achieving a 1.50% increase in precision, a 0.15% growth in recall, a 1.47% enhancement in mAP@0.5, and a 4.26% promotion in mAP@0.5:0.95. Significance testing was applied to mAP@0.5 only (paired one-sided t-test, p = 0.0058 < 0.01); the other three metrics are reported as three-seed means and spreads. As supplementary evidence, experiments on HRSID show improved recall, mAP@0.5 and mAP@0.5:0.95 under conventional patch-level splitting at the cost of slightly reduced precision. However, this patch-level performance gain is partially inflated by scene overlap between training and test patches. Further validation on a strictly leakage-free scene-level split of HRSID shows that the overall mAP@0.5 gain diminishes to within random seed fluctuation, while improvements in mAP@0.5:0.95 and precision remain observable. These results demonstrate that the proposed framework effectively enhances detection accuracy with a moderate parameter increase (9.69 M → 11.75 M) while maintaining real-time inference.

In maneuvering target tracking, traditional model-driven tracking algorithms require a predefined target motion model. The estimation accuracy degrades significantly when the actual target maneuver does not match the model assumption. Data-driven tracking algorithms can learn motion patterns directly from trajectory data, making them more robust to complex maneuvers. To improve the tracking performance in high-maneuver scenarios, this paper proposes a jerk-aware multi-feature fusion algorithm for maneuvering target tracking (JAF-MTT). The algorithm adopts jerk as the indicator of maneuver intensity. A parallel structure of convolution and multi-head self-attention is introduced to extract local and global trajectory features. These extracted features are adaptively fused in accordance with maneuver intensity. Finally, a bidirectional LSTM decodes the fused features to derive target state estimation, with the jerk adaptively modulating the gating response. Simulation results demonstrate that the performance of the proposed algorithm is better than that of the compared algorithms in high-maneuver scenarios. Moreover, the proposed algorithm maintains low tracking error under strong measurement noise.