Search Results (45)

This paper investigates the use of adaptive beamforming algorithms for communication systems and sensing networks using motion-dynamic distributed random arrays. These distributed arrays include swarms of unmanned aerial vehicles (UAVs) and are formed by unconnected antennas mounted on independent mobile platforms. This paper investigates the robustness of adaptive beamforming algorithms subject to nonidealities intrinsic to distributed random arrays such as positional error, hardware noise variations, and non-uniform elements. A simulation framework developed to evaluate various beamforming algorithms in the presence of non-idealities demonstrates that minimum variance distortionless response (MVDR) beamforming is sensitive to nominal positional errors, while minimum mean squared error (MMSE) beamforming maintains interference suppression regardless of positional error and is robust to non-uniform elements. Experiments confirm that MMSE beamforming demonstrates interference suppression in real-world channels with heterogeneous hardware. These results establish adaptive mean-squared-error-based beamforming as a robust solution for distributed random arrays. Full article

Time Difference of Arrival (TDOA)-based localization is widely used for its stability and high accuracy. However, in multi-source scenarios, TDOA measurements from multiple sources become entangled, making it difficult to separate and correctly associate them for accurate localization. To address this challenge, this paper proposes a cascaded DOA-TDOA-based multi-source weighted localization algorithm that leverages the strengths of Direction of Arrival (DOA)-based methods for separating multi-source signals and the high precision of TDOA-based methods for single-source localization. The proposed method first estimates the DOAs of multiple sources and performs DOA matching based on geometric consistency to obtain initial coarse position estimates. Subsequently, it applies wideband spatial filtering to wideband signals using the Minimum Variance Distortionless Response (MVDR) to separate multi-source signals, enhance the signal-to-noise ratio (SNR), and thereby guide the selection of the reference station and the performance of TDOA estimation. Then, TDOA estimation is performed, while the weights are assigned based on the difference in GDOP (D-GDOP), computed from the initial coarse estimate, and a weighted least-squares (WLS) method is applied to obtain the refined estimate. Finally, the D-GDOP of the refined estimate can be computed and used to reassign weights, yielding more accurate position estimate. Simulation results validate the effectiveness of the proposed method, showing superior estimation accuracy and robustness relative to existing approaches. Full article

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying frequency-difference processing to both the measured field and the replica field. However, conventional FDSL typically relies on large-aperture arrays with numerous elements, resulting in high deployment costs and bulky systems. Furthermore, it exhibits limited resolution and elevated sidelobes. These limitations are exacerbated under reduced element counts and low signal-to-noise ratio (SNR) conditions. To improve performance under low SNR and small-array configurations, this paper proposes the FDSL-MVDR and FDSL-MUSIC methods by deriving adaptive weight vectors based on the frequency-difference covariance structure and redefining the ambiguity surface. Numerical simulations in a deep-sea Munk environment (source range 195 km, depth 1000 m) using a 15-element vertical line array demonstrate that the adaptive FDSL methods outperform conventional FDSL in terms of peak sharpness and sidelobe suppression. FDSL-MUSIC achieves approximately 100% localization success at SNR = −5 dB, a 4 dB improvement over conventional FDSL. Performance analyses under representative environmental mismatches indicate that the adaptive FDSL methods maintain robust localization performance and high-resolution characteristics in complex deep-sea environments. These results validate the feasibility of high-precision deep-sea localization using a few-element array. Full article

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

We improved signal reception by minimizing interference in dynamic communication environments with a minimum-variance distortionless response (MVDR) algorithm. The conditions of the MVDR algorithm were simulated using MATLAB and GNU Radio to enhance its capabilities in noise and interference suppression. Through a MATLAB simulation, the adaptive beamforming performance of MVDR was examined and compared with that of conventional beamforming techniques to identify the advantages of beam steering for obtaining the desired signals. MVDR was effective in interference reduction and the improvement of signal clarity, with superiority over conventional approaches in cases with complex interference patterns. Based on the results of the MATLAB simulations, GNU Radio was used in a complete software-defined radio (SDR) environment that enabled the replication of real-world conditions to study MVDR. We simulated real-world applications by integrating GNU Radio to ensure the robustness and adaptability of the algorithm in live signal processing. The results from these two simulations prove the potential of MVDR as a strong dynamic interference suppressor that enables superior signal reception. The results enable the implementation of the MVDR algorithm in communication systems. Full article

In this paper, we introduce a robust beamforming technique using array antennas. The proposed solution constitutes two stages; the first stage exploits the space-alternating generalized expectation-maximization (SAGE) algorithm to decompose the received GNSS signal into its constituent signals, i.e., direct and reflected signals. The SAGE algorithm estimates the angle of arrival (AoA) and the received covariance matrix for both the direct and reflected signals. The second stage, on the other hand, utilizes the Minimum Variance Distortionless Response (MVDR) algorithm to produce the weight vector that steers the main beam towards the satellite’s direction and the nulls towards the multipath effect. The MVDR uses the AoA of the direct path and the covariance matrix of the reflected path to minimize the multipath effect. The experimental results reveal that the proposed technique improves the received signal strength and the location estimation accuracy, as compared to a single-antenna system. Furthermore, the proposed technique outperforms the traditional MVDR technique in the tested environment. Finally, the 95% 3D position error of the proposed solution is 5.2 m, and the position dilution of precision (pdop) is 0.84. Full article

Among damage imaging methods based on Lamb waves, the Minimum Variance Distortionless Response (MVDR) method adaptively calculates channel weights to suppress interference signals, improving imaging resolution and the signal-to-noise ratio (SNR). However, the MVDR method involves matrix inversion, which introduces a high computational burden to the implementation process and makes real-time damage detection challenging. We propose constructing a Convolutional-Neural-Network (CNN)-based network architecture based on the Delay-and-Sum (DAS) beamforming method. This architecture replaces the MVDR’s adaptive weight calculation by establishing a nonlinear mapping from multi-channel data to weighting factors, enabling efficient high-resolution Lamb wave damage imaging with an enhanced SNR. To verify the effectiveness and imaging performance of the CNN-based method, damage in an aluminum plate is imaged using both simulation and experimental methods. The imaging results are compared and analyzed against those of the DAS and MVDR methods. The results show that the proposed CNN-based adaptive Lamb wave beamforming method, which combines the advantages of a high resolution and signal-to-noise ratio, as well as rapid imaging, can provide reference and support for real-time Lamb-wave-based Structural Health Monitoring (SHM). Full article

In shallow-water environments, source localization often suffers from reduced performance in conventional array signal processing techniques for frequency bands above 1 kHz due to environmental mismatch. A recently proposed technique, called the steered frequency–wavenumber (SFK) analysis method, overcomes this limitation. By incorporating beam-steering techniques into frequency–wavenumber analysis, this method enables target localization even in sparse conditions where high-frequency signals are received. This study extends the SFK method by applying various adaptive signal processing techniques, with a particular focus on the minimum-variance distortionless response and white noise gain constraint methods. Using snapping shrimp sounds from the SAVEX15 experiment, we analyzed localization performance and compared it with the Bartlett SFK approach. The snapping shrimp signals have frequency components ranging from 5 to 24 kHz and exhibit impulsive characteristics with a duration of 0.2 ms. Signals recorded by a sparse vertical array of 16 sensors, with a 60-m aperture in 100-m shallow water, enabled the localization of a source at a range of 38 m and a depth of 99.8 m. Full article

To achieve high-precision positioning using the global navigation satellite system (GNSS), the onboard GNSS receiver integrates real-time kinematic (RTK) technology to enable centimeter-level positioning accuracy. Furthermore, array anti-interference technology is incorporated into the RTK receiver to enhance positioning reliability and mitigate interference in complex environments such as urban areas or regions with high electromagnetic activity. However, this approach can introduce signal distortion, which adversely affects the convergence of RTK positioning. To address the issue of bias introduced by interference suppression in RTK positioning, this paper focuses on error modeling and bias compensation through a phase bias compensation algorithm. A novel phase compensation algorithm is proposed, leveraging the anti-interference weighting coefficients of array elements and the anti-interference output signal. Compared to the conventional minimum variance distortionless response (MVDR) algorithm, the proposed method features a simpler architecture and achieves phase compensation at a lower computational cost using the power inverse (PI) algorithm. Simulation experiments demonstrate the effectiveness of the compensation method, achieving a mean phase bias of approximately 0.25 degrees and a variance of 4.62 degrees. This level of accuracy makes it highly suitable for UAVs operating in challenging environments where precision and reliability are paramount. Full article

In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. The input dataset for this neural architecture is constructed by applying multi-source tracking using multi-sensor generalized labeled multi-Bernoulli (MS-GLMB) filtering, which belongs to the labeled RFS framework, to obtain estimations of the sources’ positions and the associated labels (corresponding to each source) at each time frame with high accuracy under the effect of undesirable factors like reverberation and background noise. The tracked sources’ positions and associated labels help to correctly discriminate the target source from the interferers across all time frames and generate time–frequency (T-F) masks corresponding to the target source from the output of a time-varying, minimum variance distortionless response (MVDR) beamformer. These T-F masks constitute the target label set used to train the proposed deep neural architecture to perform target speech enhancement. The exploitation of MS-GLMB filtering and a time-varying MVDR beamformer help in providing the spatial information of the sources, in addition to the spectral information, within the neural speech enhancement framework during the training phase. Moreover, the application of the GAN framework takes advantage of adversarial optimization as an alternative to maximum likelihood (ML)-based frameworks, which further boosts the performance of target speech enhancement under reverberant conditions. The computer simulations demonstrate that the proposed approach leads to better target speech enhancement performance compared with existing state-of-the-art DL-based methodologies which do not incorporate the labeled RFS-based approach, something which is evident from the 75% ESTOI and PESQ of 2.70 achieved by the proposed approach as compared with the 46.74% ESTOI and PESQ of 1.84 achieved by Mask-MVDR with self-attention mechanism at a reverberation time (RT60) of 550 ms. Full article

Corrosion damage presents significant challenges to the safety and reliability of connected vehicles. However, traditional non-destructive methods often fall short when applied to complex automotive structures, such as bolted lap joints. To address this limitation, this study introduces a novel corrosion monitoring approach using Lamb wave-based weighted fusion imaging methods. First, the Minimum Variance Distortionless Response (MVDR) is utilized to process Lamb wave signals collected under bolt-loosening and bolt-tightening conditions to image the bolt locations. Second, based on the identified bolt positions, the weighted Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) is applied to the Lamb wave signals acquired before and after corrosion, enabling precise imaging of the actual positions of the corroded bolts. Experiments are conducted on three-bolt lap joints in cases of single-corrosion and two-corrosion using A0 mode Lamb waves and piezoelectric sensor networks. The results demonstrate that the proposed method effectively images multiple types of damage and achieves maximum location deviations of 7.43 mm. This approach enables precise and visual multi-damage assessment, particularly in hard-to-access regions. When integrated with V2X-enabled (Vehicle-to-Everything) systems, the method offers potential for incorporation into automotive structural health monitoring systems for remote diagnosis in complex structures, thereby enhancing monitoring efficiency and accuracy. Full article

The combination of neural networks and beamforming has proven very effective in multi-channel speech separation, but its performance faces a challenge in complex environments. In this paper, an iteratively refined multi-channel speech separation method is proposed to meet this challenge. The proposed method is composed of initial separation and iterative separation. In the initial separation, a time–frequency domain dual-path recurrent neural network (TFDPRNN), minimum variance distortionless response (MVDR) beamformer, and post-separation are cascaded to obtain the first additional input in the iterative separation process. In iterative separation, the MVDR beamformer and post-separation are iteratively used, where the output of the MVDR beamformer is used as an additional input to the post-separation network and the final output comes from the post-separation module. This iteration of the beamformer and post-separation is fully employed for promoting their optimization, which ultimately improves the overall performance. Experiments on the spatialized version of the WSJ0-2mix corpus showed that our proposed method achieved a signal-to-distortion ratio (SDR) improvement of 24.17 dB, which was significantly better than the current popular methods. In addition, the method also achieved an SDR of 20.2 dB on joint separation and dereverberation tasks. These results indicate our method’s effectiveness and significance in the multi-channel speech separation field. Full article

In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°. Full article