We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. Leveraging the difference coarray technique, the sparse array is transformed into a virtual uniform linear array (VULA) to enrich the spatial sampling; real-valued covariance matrices derived from the array measurements are used as the network’s input features. A final multi-layer perceptron (MLP) regression module then maps the learned representations to continuous DOA angle estimates. This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. By focusing on methodological innovation rather than bespoke architectural tuning, the framework shows promise for data-efficient DOA estimation in challenging practical conditions.

Three-dimensional sensing technologies can rapidly acquire 3D point cloud data for object perception and scene understanding. However, point cloud-based object classification is still constrained by limited labeled data and high computational complexity. At present, feature extractors pretrained on large-scale 2D datasets have achieved strong performance in zero-shot 2D classification. Therefore, projecting 3D point clouds into 2D images and leveraging well-established 2D pretrained models for zero-shot point cloud classification has become an effective strategy. In this strategy, generating high-fidelity 2D projections is a critical challenge. To address this challenge, this paper proposes a zero-shot 3D object classification framework based on a multi-scale local geometric feature extraction module and depth-aware multi-view projection. Specifically, point clouds are first modeled as graph structures. Multi-scale radii are used to adjust the receptive field during feature extraction, thereby capturing both fine-grained and large-scale local geometric features. Multi-view 2D images are then generated through depth-wise feature accumulation. These images are fed into a frozen CLIP model for zero-shot classification. The proposed method preserves the structural characteristics of point clouds and reduces the domain gap between point clouds and 2D images. Experiments are conducted on the ModelNet10, ModelNet40, and ScanObjectNN datasets. The results show that the proposed method outperforms current mainstream zero-shot 3D point cloud classification methods. These results suggest that the proposed framework offers an effective solution for point cloud object classification in complex scenarios.

This paper proposes a novel hybrid deep learning framework for direction-of-arrival (DOA) estimation using a uniform linear array. Direction of Arrival estimation is a fundamental problem in array signal processing with critical applications in radar, sonar, wireless communications, and speech processing. Traditional methods like MUSIC and ESPRIT provide high resolution but suffer from high computational complexity and poor performance in low signal-to-noise ratio (SNR) environments. Recent advances in deep learning have shown promise in improving DOA estimation accuracy and robustness. The framework synergistically combines a ResNet-based feature extractor with a Mamba state-space model through a feature fusion mechanism. The ResNet branch extracts high-level spatial features from the covariance matrix, while the Mamba branch captures long-range dependencies and sequential patterns. These complementary features are fused and then passed to an MLP for DOA regression. Extensive experiments on simulated datasets demonstrate that, at low SNRs, our fusion model significantly outperforms traditional methods such as MUSIC and ESPRIT, as well as other baseline models, in terms of both estimation accuracy and computational efficiency. Quantitatively, at SNR = −5 dB, the proposed method reduces the RMSE by 41.6% compared to MUSIC.

Accurate direction-of-arrival (DOA) estimation with high resolution is fundamental to many array sensing applications. In practice, however, sparse arrays with missing sensors and snapshot-limited observations often lead to incomplete and noisy second-order statistics, which substantially degrades the performance of conventional eigendecomposition-based estimators. In this paper, we propose a two-stage Transformer framework for sparse-array DOA estimation that explicitly separates correlation recovery from angle inference. The first stage operates in the correlation domain and learns to reconstruct a clean and complete correlation vector from partially observed measurements using masking-aware tokenization and global-context modeling. The recovered representation can be further converted into a structured covariance matrix, providing an interpretable interface to classical signal processing back-ends. Based on the recovered features, the second stage adopts a Transformer regressor to directly predict multi-source DOAs. Extensive simulations on a large-scale dataset with SNRs from −5 to 10 dB and various snapshot numbers demonstrate that the proposed method delivers robust accuracy and improved stability in low-SNR and snapshot-limited regimes, while maintaining competitive performance at higher SNRs. Additional evaluations with an ESPRIT back-end further confirm that the recovery-based covariance yields more reliable DOA estimation than conventional difference–coarray processing, with particularly evident gains under challenging noise conditions.

Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework.

Off-grid direction-of-arrival (DOA) estimation based on sparse Bayesian learning (SBL) can alleviate angular discretization mismatch, but its practical performance may be affected by unreliable posterior relevance statistics, sensitivity of effective error precision learning, and unstable offset correction. This paper proposes a reliability-guided stabilized off-grid SBL method for multisnapshot DOA estimation. The method is developed within the standard first-order multiple-measurement-vector Bayesian model and introduces three stabilization modules. First, a confidence-guided MAP-type shrinkage relevance update is introduced to suppress weak and non-dominant posterior components through reliability-controlled non-expansive shrinkage. Second, a posterior-concentration-guided damped noise update is introduced to stabilize scalar effective error precision learning when the sparse support is uncertain. Third, a trust-region cubic-regularized Newton refinement is formulated to obtain bounded active-support off-grid corrections from the posterior expected reconstruction error. Simulation results under off-grid deviation, varying SNRs, varying snapshot numbers, different source separations, and random-angle scenarios show that the proposed method achieves competitive and stable estimation performance compared with representative classical and sparse Bayesian baselines.