Search Results (549)

This paper introduces a theoretical large-scale radio channel model for the downlink in cellular systems, aimed at estimating variations in received signal power at the user terminal as a function of device mobility. This enables applications such as direction-of-arrival (DoA) estimation, estimating power at subsequent points based on received power, and detection of coverage anomalies. The model is validated using real-world measurements from urban and suburban environments, achieving a maximum estimation error of 7.6%. In contrast to conventional models like Okumura–Hata, COST-231, Third Generation Partnership Project (3GPP) stochastic models, or ray-tracing techniques, which estimate average power under static conditions, the proposed model captures power fluctuations induced by terminal movement, a factor often neglected. Although advanced techniques such as wave-domain processing with intelligent metasurfaces can also estimate DoA, this model provides a simpler, geometry-driven approach based on empirical traces. While it does not incorporate infrastructure-specific characteristics or inter-cell interference, it remains a practical solution for scenarios with limited information or computational resources. Full article

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. Full article

We propose an efficient Nyström-based unitary subspace method for low-complexity two-dimensional (2D) direction-of-arrival (DOA) estimation in uniform rectangular array (URA) signal processing systems. The conventional high-resolution DOA estimation methods often suffer from excessive computational complexity, particularly when dealing with large-scale antenna arrays. The proposed method addresses this challenge by combining the Nyström approximation with a unitary transformation to reduce the computational burden while maintaining estimation accuracy. The signal subspace is approximated using a partitioned covariance matrix, and a real-valued transformation is applied to further simplify the eigenvalue decomposition (EVD) process. Furthermore, the linear prediction coefficients are estimated via a weighted least squares (WLS) approach, enabling robust extraction of the angular parameters. The 2D DOA estimates are then derived from these coefficients through a closed-form solution, eliminating the need for exhaustive spectral searches. Numerical simulations demonstrate that the proposed method achieves comparable estimation performance to state-of-the-art techniques while significantly reducing computational complexity. For a fixed array size of $M=N=20$, the proposed method demonstrates significant computational efficiency, requiring less than $50\%$ of the running time compared to conventional ESPRIT, and only $6\%$ of the time required by ML methods, while maintaining similar performance. This makes it particularly suitable for real-time applications where computational efficiency is critical. The novelty lies in the integration of Nyström approximation and unitary subspace techniques, which jointly enable efficient and accurate 2D DOA estimation without sacrificing robustness against noise. The method is applicable to a wide range of array processing scenarios, including radar, sonar, and wireless communications. Full article

The challenge of achieving high-precision direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs) under a limited number of physical array elements remains a critical issue in array signal processing. To address this limitation, this paper makes the following three key contributions: (1) a novel moving sparse array synthesis model incorporating time-frequency-spatial joint processing for coprime frequencies signal sources; (2) an optimized coprime frequencies-based unmanned aerial vehicle array (CF-UAVA) configuration with derived closed-form expressions for the distribution of synthesized array; and (3) two DOA estimation methods: a group sparsity-based approach universally applicable to the proposed aperture synthesis model and a joint group sparsity and virtual array interpolation tailored for the proposed CF-UAVA configuration. Comprehensive simulation results demonstrate the superior DOA estimation accuracy and increased DOFs achieved by our proposed aperture synthesis model and DOA estimation algorithms compared to conventional approaches. Full article

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios. Full article

Traditional antenna arrays for direction-of-arrival (DOA) estimation typically require numerous elements to achieve target performance, increasing system complexity and cost. Reconfigurable intelligent surfaces (RISs) offer a promising alternative, yet their performance critically depends on phase coding design. To address this, we propose a phase coding design method for RIS-aided DOA estimation with a single receiving channel. First, we establish a system model where averaged received signals construct a power-based formulation. This transforms DOA estimation into a compressed sensing-based sparse recovery problem, with the RIS far-field power radiation pattern serving as the measurement matrix. Then, we derive the decoupled expression of the measurement matrix, which consists of the phase coding matrix, propagation phase shifts, and array steering matrix. The phase coding design is then formulated as a Frobenius norm minimization problem, approximating the Gram matrix of the equivalent measurement matrix to an identity matrix. Accordingly, the phase coding design problem is reformulated as a Frobenius norm minimization problem, where the Gram matrix of the equivalent measurement matrix is approximated to an identity matrix. The phase coding is deterministically constructed as the product of a unitary matrix and a partial Hadamard matrix. Simulations demonstrate that the proposed phase coding design outperforms random phase coding in terms of angular estimation accuracy, resolution probability, and the requirement of coding sequences. Full article

Sound event localization and detection (SELD) is a fundamental task in spatial audio processing that involves identifying both the type and location of sound events in acoustic scenes. Current SELD models often struggle with low signal-to-noise ratios (SNRs) and high reverberation. This article addresses SELD by reformulating direction of arrival (DOA) estimation as a multi-class classification task, leveraging deep convolutional recurrent neural networks (CRNNs). We propose and evaluate two modified architectures: M-DOAnet, an optimized version of DOAnet for localization and tracking, and M-SELDnet, a modified version of SELDnet, which has been designed for joint SELD. Both modified models were rigorously evaluated on the STARSS23 dataset, which comprises 13-class, real-world indoor scenes totaling over 7 h of audio, using spectrograms and acoustic intensity maps from first-order Ambisonics (FOA) signals. M-DOAnet achieved exceptional localization (6.00° DOA error, 72.8% F1-score) and perfect tracking (100% MOTA with zero identity switches). It also demonstrated high computational efficiency, training in 4.5 h (164 s/epoch). In contrast, M-SELDnet delivered strong overall SELD performance (0.32 rad DOA error, 0.75 F1-score, 0.38 error rate, 0.20 SELD score), but with significantly higher resource demands, training in 45 h (1620 s/epoch). Our findings underscore a clear trade-off between model specialization and multifunctionality, providing practical insights for designing SELD systems in real-time and computationally constrained environments. Full article

In practical array signal processing applications, direction-of-arrival (DOA) estimation often suffers from degraded accuracy under low signal-to-noise ratio (SNR) and limited snapshot conditions. To address these challenges, we propose an off-grid DOA estimation method based on Fast Variational Bayesian Inference (OGFVBI). Within the variational Bayesian framework, we design a fixed-point criterion rooted in root-finding theory to accelerate the convergence of hyperparameter learning. We further introduce a grid fission and adaptive refinement strategy to dynamically adjust the sparse representation, effectively alleviating grid mismatch issues in traditional off-grid approaches. To address frequency dispersion in wideband signals, we develop an improved subspace focusing technique that transforms multi-frequency data into an equivalent narrowband model, enhancing compatibility with subspace DOA estimators. We demonstrate through simulations that OGFVBI achieves high estimation accuracy and resolution while significantly reducing computational time. Specifically, our method achieves more than 37.6% reduction in RMSE and at least 28.5% runtime improvement compared to other methods under low SNR and limited snapshot scenarios, indicating strong potential for real-time and resource-constrained applications. Full article

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the UG to synthesize a linear array whose elements are positioned to acquire the target signal, thereby increasing the array aperture. The dead-reckoning method is used to determine the underwater trajectory of the UG, and the UG’s trajectory was corrected by the UG motion parameters, from which the array shape was adjusted accordingly and the position of the array elements was corrected. Additionally, array distortion caused by movement offsets due to ocean currents underwent linearization, reducing computational complexity. To validate the proposed method, a sea trial was conducted in the South China Sea using the Haiyi 1000 UG equipped with a hydrophone, and its effectiveness was demonstrated through the processing of the collected data. The performance of DOA estimation prior to and following UG trajectory correction was compared to evaluate the impact of ocean currents on target DOA estimation accuracy. Full article

Multi-Beam frequency scanning leaky-wave antennas (FBS-LWAs) offer a viable solution for hardware miniaturization in direction-of-arrival (DOA) estimation systems. However, the presence of multiple spatial harmonics results in responses in multiple directions for a given incident source, introducing estimation ambiguity and significantly challenging accurate DOA estimation. Moreover, due to the nonlinear frequency response of the FBS-LWA, its response matrix does not satisfy the Vandermonde structure, which renders common rank-recovery techniques ineffective for processing coherent signals. As a result, the DOA estimation of coherent sources using multi-beam FBS-LWAs remains an open and challenging problem. To address this issue, this paper proposes a novel DOA estimation method for coherent signals based on multi-beam frequency scanning leaky-wave antennas. First, the received signals are transformed into the frequency domain via fast Fourier transform (FFT) to construct the signal data matrix from which the covariance matrix is computed.Then, conventional beamforming (CBF) is employed to obtain an initial estimate of the angle set, which will be further refined by a smaller grid to form a candidate angle set. Finally, a maximum likelihood algorithm based on the stochastic principle (Sto-ML) is used to suppress the interference of the parasitic directions and select the final DOA estimates from the candidate angle set. Simulation results show that the proposed method effectively mitigates the impact of parasitic directions and achieves an accurate DOA estimation of multiple coherent sources, even under both low and medium-to-high signal-to-noise ratio (SNR) conditions. Full article

High-precision Direction-of-Arrival (DOA) estimation leveraging multi-sensor array architectures represents a frontier research domain in advanced array signal processing systems. Compared to traditional model-driven estimation methods like MUSIC and ESPRIT, data-driven approaches offer advantages such as higher estimation accuracy and simpler structures. Convolutional neural networks (CNNs) currently dominate deep learning approaches for DOA estimation. However, traditional CNNs suffer from limitations in capturing global features of covariance matrices due to their restricted local receptive fields, alongside challenges such as noise sensitivity and poor interpretability. To address these issues, we propose a novel Discrete Fourier Transform (DFT)-based deep learning framework for DOA estimation called DFNeT, leveraging the advantages of Fourier transform-enhanced networks in global modeling, computational efficiency, and noise robustness. Specifically, our approach introduces a DFT-based deep feature fusion network to denoise covariance matrices by integrating spatial and frequency-domain information. Subsequently, a series of DFT modules are designed to extract discriminative frequency-domain features, enabling accurate and robust DOA estimation. This method effectively mitigates noise interference while enhancing the interpretability of feature extraction through explicit frequency-domain operations. The simulation results demonstrate the effectiveness of the proposed method. Full article

In this paper, we establish criteria for the design of small aperture antenna arrays for Direction of Arrival (DOA) estimation. We define a small aperture antenna array as one consisting of a few elements with an average interelement spacing less than or equal to half a wavelength. We use the spatial covariance matrix of the antenna array to derive the design criterion. It is well known that the DOA estimation performance of an antenna array is strongly related to the amount of information in this matrix. Also, the Cramer-Rao Bound of the estimated DOA is closely related to this matrix. We establish and demonstrate that, for optimal DOA estimation performance, a small aperture antenna array should have non-uniformly spaced and dissimilar antenna elements. Since mutual coupling between antenna elements makes their in situ responses dissimilar, instead of mitigating mutual coupling, one should include mutual coupling in the DOA estimation process to enhance the DOA estimation performance of antenna arrays. Full article

In the field of Direction of Arrival (DOA) estimation, due to the complexity of ocean noise and the limitations of using array apertures, the bearing time record (BTR) obtained by CBF typically exhibits low signal-to-noise ratio (SNR) and wide mainlobe width. To address this issue, we propose an adaptive 2D convolution kernel construction method that utilizes an improved k-means clustering algorithm to extract adaptive mainlobe visual patterns from historical BTR data as convolution kernels. Experimental results show that our method can effectively reduce noise levels within multiple target environments. Full article

Existing array calibration methods rely on the geometric characteristics of the array or signal matrix, which limits their flexibility and robustness. This study addresses this challenge by proposing a novel joint sparse estimation method for array gain and phase calibration. By leveraging the sparsity of calibration signals and the dictionary mismatch model, the proposed method, based on the fast iterative shrinkage-thresholding algorithm (FISTA), jointly estimates the discrete on-grid azimuths and continuous off-grid offsets of the direction of arrival (DOA) of calibration signals. The method employs a spatial domain filtering technique based on the maximum a posteriori probability to mitigate the bias induced by phase errors in the calibration signal estimation, enhancing calibration accuracy. Furthermore, the iterative estimation framework was optimized to extend the applicability of the method from linear to uniform planar arrays. The results demonstrated that the root mean squared error (RMSE) of the beam pattern for various array types decreased by one to two orders of magnitude after calibration. Compared with existing state-of-the-art methods, the proposed approach exhibited stable performance and superior estimation accuracy under conventional signal-to-noise ratio conditions. Moreover, the proposed method maintained high stability and lower RMSE as the gain and phase error values increased. Full article

A significant advantage of off-grid direction-of-arrival (DOA) estimation algorithms using difference coarrays is their ability to resolve more sources than the number of physical sensors. Current coarray-based off-grid DOA estimation algorithms experience a significant decline in estimation accuracy with limited snapshots. Moreover, most existing DOA estimation techniques exhibit a high computational complexity, limiting their practical implementation in real-time systems. To address these limitations, in this work, we propose a novel coarray-based off-grid DOA estimation algorithm that achieves a computationally efficient performance while maintaining a high estimation accuracy under snapshot-constrained conditions. The proposed algorithm first performs DOA estimation through coarray-augmented spatial smoothing multiple signal classification (SS-MUSIC), followed by noise suppression via multiplication with a constructed selection matrix. The off-grid angular deviations are sequentially refined based on the iterative correction mechanism. The disadvantage of a large number of snapshots requirement is overcome thanks to the combination of noise elimination and sequential angle refinement. Theoretical performance bounds are established through Cramér–Rao bound (CRB) analysis, while comprehensive simulations validate the estimation accuracy of the proposed algorithm and the robustness in off-grid scenarios. Full article