The modeling and analysis of sea clutter are of great significance in radar target detection studies in marine environments. Sea clutter typically exhibits non-Gaussian characteristics and spatiotemporal correlations, posing challenges for modeling, especially when generating simulation data of continuous correlated non-Gaussian random processes. This paper proposes a novel method for sea clutter modeling. First, feature description functions are constructed to individually characterize the amplitude, temporal, and spatial correlations of sea clutter, allowing for an accurate depiction of its characteristics with fewer parameters. Subsequently, simulation data are generated based on these feature description functions, satisfying the amplitude distribution, temporal correlation, and spatial correlation characteristics of sea clutter. Additionally, complex signal forms are introduced in the underlying signal processing to generate texture and speckle components of sea clutter, enhancing the alignment of simulation data with actual data. Through comparison with measured sea clutter data, the proposed method has been shown to accurately simulate complex sea clutter with real-world characteristics.

Mainlobe suppression jamming significantly degrades radar detection performance. The conventional blind source separation (BSS) algorithms often fail under high-jamming-to-signal-ratio (JSR) and low-signal-to-noise-ratio (SNR) conditions. To overcome this limitation, we propose an enhanced BSS method combining variational mode decomposition (VMD) and wavelet packet decomposition (WPD), termed VMD-WPD-JADE. The proposed approach first applies VMD-WPD for noise reduction in radar signals and then utilizes the JADE algorithm to compute the separation matrix of the denoised signals, effectively achieving blind source separation of radar echoes for interference suppression. We evaluate the method using noise-amplitude modulation and noise-frequency modulation jamming scenarios. The experimental results show that at a JSR = 50 dB and an SNR = −5 dB, our method successfully separates the target signals. Compared with the conventional blind source separation (BSS) algorithms, the proposed technique demonstrates superior robustness, achieving a 4–11% improvement in the target detection probability under noise-amplitude modulation (NAM) jamming and a 4–16% enhancement under noise-frequency modulation (NFM) jamming within a signal-to-noise ratio (SNR) range of −5 dB to 5 dB.

Shared electric vehicles (SEVs) are becoming a new way for urban residents to travel because of their environmental protection, energy saving, and sustainable development. However, at present, the operation mode of shared electric vehicles has the problem that the vehicle cannot be utilized efficiently. For this reason, this paper studied the mode of SEVs with ride-sharing (MSEVRS) and SEVs routing optimization under this mode. Firstly, the operation principle of MSEVRS is presented, which includes the collection of user demand information and SEVs information and the routing optimization of SEVs, both of which are completed by the user and SEVs management center. Secondly, the routing optimization model of SEVs with ride-sharing is proposed, in which the SEVs operation cost, user time cost, user rental cost, and user ride-sharing bonus are taken into account. And the genetic algorithm is designed to solve the model. Finally, a case study is carried out to illustrate the effectiveness of the proposed model. The results show that the proposed routing optimization model achieves the optimal SEVs route, realizes the MSEVRS, and improves the utilization rate of SEVs. Compared with the current SEVs mode (CSEVM), the MSEVRS reduces the number of vehicles, user rental cost, the total cost of users, and the total cost of user and company of SEVs. And the total distance is reduced, which means saving energy. Moreover, it shows that MSEVRS obtains a better cost performance and service for users and has a better application prospect.

To address the insufficient utilization of network model features and low search efficiency in kill chain analysis for Weapon System of Systems (WSoS), a complex network model of WSoS based on OODA loop was constructed, which converts the indicator system into attribute features embedded in network nodes, and analyzes the kill chain mode through the metapath. Subsequently, a Depth First Search (DFS) algorithm combined with Graph Attention Network (GAT) is proposed for kill chain search evaluation. The algorithm utilizes GAT to extract topological information and node attribute features from graph data to obtain node-embedding vectors, and optimizes the DFS algorithm process by computing the cosine similarity of node-embedding vectors. Simulation results demonstrated that the proposed algorithm achieves high search efficiency and accuracy, providing robust support for combat decision-making.

To address the challenge of strong sea clutter masking weak target signals for shore-based radar under complex sea states, this article proposes a complex-valued single-stage sea clutter suppression network (CV-SCSNet) which is characterized by a multilevel residual structure. The interpretability of the network is enhanced by establishing a theoretical linkage between traditional subspace cancellation methods and residual learning. To mitigate the challenges posed by the nonuniform and nonstationary characteristics of sea clutter, a Feature Residual Attention Module (FRAM) is designed by incorporating large-kernel depthwise separable convolutions, a nonlinear feature fusion process, and an attention mechanism. This design enables the fusion of multiscale features and contextual information, thereby improving the network’s feature extraction capabilities. Furthermore, a complex-valued Huber loss function and an evaluation metric that considers target reconstruction performance are constructed to ensure training robustness and evaluation accuracy. Experimental results demonstrate that under input Signal-to-Clutter Ratio (SCR) conditions of down to $-$20 dB, the proposed CV-SCSNet can maintain the distortion rate of the reconstructed target below 20% on the radar scan evaluation dataset for sea states 3–4. It achieves a net SCR gain of over 40 dB, showcasing excellent clutter suppression performance and target reconstruction capability.

To address the poor performance of conventional sea clutter suppression methods when target echoes share the same frequency as sea clutter, this paper proposes a suppression approach based on feature transformation. The autocorrelation matrix of sea clutter is first employed to construct a feature transformation matrix, through which the received radar data are normalized in the feature space. A differential operation is then applied to cancel clutter components, and the residual energy is combined with a cell-averaging constant false alarm rate (CA-CFAR) detector to achieve target detection in co-frequency scenarios. The proposed method requires no additional prior information and provides a new solution for detecting low-speed maritime targets. Simulation results demonstrate that the method significantly improves both signal-to-clutter ratio (SCR) and detection probability under various sea states and SCR conditions, with particularly notable advantages when the target frequency is close to the sea clutter spectral peak.