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Efficiently scanning for space signals and accurately detecting them from noisy environment is essential in space communication. Various unwanted interferences also present in space that may hamper the perfect detection process. This paper proposes a novel near-field circular beamformer (NCB) that will perfectly detect the desired source signal from any direction and position in space. To improve the robustness of NCB against Direction of Arrival (DOA) error, distance error, unwanted interferences and noises, this work also offers robust NCBs (RNCB) using robust Optimal Diagonal Loading (ODL) and Variable Diagonal Loading (VDL) techniques. While searching for wanted signal, the beamformer provides a primary lobe at the look direction and shows some secondary unwanted side lobes for noise and interference. Sometimes these undesired side lobe levels (SLL) become so severe that it may create conflict in locating the precise position of the desired source. To reduce these SLL, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) techniques have been applied to RNCB. The simulation results show that the optimized RNCB significantly diminishes the objectionable SLL of non-optimized RNCB by choosing appropriate weight vector of antenna array without affecting the other antenna parameters. Artificial Neural Network (ANN) have also been used to predict the weight vector for minimum SLL. [ABSTRACT FROM AUTHOR]

The immense degree of freedom (DOF), high array aperture, non-uniform linear arrays, and reduced mutual coupling have developed interest in the estimations of the direction of arrival (DOA). Due to complex previous structures, this paper investigates the bi-level sparse linear nested array (SNA) concepts to discuss element spacing and different ranges on uniform DOF. Then features of flower pollination algorithm is applied to the proposed two-level SNA to generalize and enhance the proposed structure further. In order to boost DOF, it is also investigated local and global minima of highly non-linear functions. The proposed technique for quantifying the DOA is reviewed analytically using evaluation parameters like cumulative distributive function, accuracy, root mean square error, and robustness against noise and snapshots. The simulation findings prove its validation with the analytical model and target the accuracy with fewer separations and the minimum number of physical sensors in relation to particle swarm optimization. Moreover, the strength of the proposed study further validated by comparing with Cramer Rao Bound for minimum variance which shows that the FPA outperforms. [ABSTRACT FROM AUTHOR]

In underwater acoustic applications, the conventional cyclic direction of arrival algorithm faces challenges, including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies. In response to these issues, this paper introduces a novel, robust, and broadband cyclic beamforming algorithm. The proposed method substitutes the conventional cyclic covariance matrix with the variance of the cyclic covariance matrix as its primary feature. Assuming that the same frequency band shares a common steering vector, the new algorithm achieves superior detection performance for targets with specific modulation frequencies while suppressing interference signals and background noise. Experimental results demonstrate a significant enhancement in the directibity index by 81% and 181% when compared with the traditional Capon beamforming algorithm and the traditional extended wideband spectral cyclic MUSIC (EWSCM) algorithm, respectively. Moreover, the proposed algorithm substantially reduces computational complexity to 1/40th of that of the EWSCM algorithm, employing frequency band statistical averaging and covariance matrix variance. [ABSTRACT FROM AUTHOR]

Today, the common solutions for underwater source angle detection require manned vessels and towed arrays, which are associated with high costs, risks, and deployment difficulties. An alternative solution for such applications is represented by acoustic vector sensors (AVSs), which are compact, lightweight and moderate in cost, and which have promising performance in terms of the bearing discrimination in two or three dimensions. One of the most popular devices for passive monitoring in underwater surveillance systems that employ AVSs is the directional frequency analysis and recording (DIFAR) sonobuoy. In this paper, direct data-processing (DDP) algorithms are implemented to calculate the azimuth angle of underwater acoustic sources by using short-time Fourier transform (STFT) via the arctan method instead of using fast Fourier transform (FFT). These algorithms for bearing estimation use the 'Azigram' to plot the estimated bearing of a source. It is demonstrated that by knowing the active sound intensity of the sound field and applying the inverse tangent to its real part, this matrix can be obtained. Announcing the time and frequency of the source simultaneously is one of the main advantages of this method, enabling the detection of multiple sources concurrently. DDP can also provide more details about sources' characteristics, such as the frequency of the source and the time of the source's presence. [ABSTRACT FROM AUTHOR]

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present the performance of Direction of Arrival(DoA) estimation algorithms for underwater sound source localization using an acoustic Vector Sensor Array (VSA) that is developed by the National Institute of Ocean Technology, Chennai. Algorithms such as conventional beam forming, Multiple Signal Classification (MUSIC) with Eigen value decomposition, and MUSIC with Singular Value Decomposition (SVD) are used for estimation of DoA and performance study. An experiment has been conducted with the VSA at the Acoustic Test Facility of NIOT with the source transmission of 1 kHz to 5 kHz for different azimuth angles. The estimation of DoA using the above three algorithms and the comparison of the results on resolution and accuracy have been studied in detail in terms of the number of vector elements. Results reveal that the MUSIC method gives results with higher accuracy and resolution than the conventional method. The maximum deviation from the true angle in the conventional method is 4°; in MUSIC, it is 2°, whereas in MUSIC with SVD, it is 1°. While the standard MUSIC algorithm involves computing the eigenvectors of the covariance matrix, which can be computationally expensive, MUSIC with SVD provides a more efficient way to achieve better results. SVD enables straightforward computation of the signal subspace, making it more practical for real-time applications like coastal surveillance. Further to the laboratory experiment, the vector sensor system has been deployed in an open sea environment near the harbor and a known source experiment is carried out. The DoA estimated using MUSIC with SVD for the field data reveals that the results are in good comparison with the measured azimuth and elevation positions. The deviations in the field results are due to dynamic conditions of the ocean,and more sea trials need to be carried out for further study. [ABSTRACT FROM AUTHOR]

The application field of global navigation satellite systems continues to expand, and their security and stability have received widespread attention. Navigation spoofing has the characteristics of solid concealment and significant harm, posing a severe security threat to navigation systems. In current spoofing detection methods based on signal spatial correlation, multiple antennas/receivers or moving single antennas are required, which means high cost and complexity in implementation. To this end, we propose a spoofing detection method based on the intersection angle between two directions of arrival (IA-DOA) for single-antenna receivers. The essence of this method is to accurately estimate the IA-DOA between a pair of signals based on pseudorange observations and navigation information. The observation should be consistent with the prediction when there is no spoofing. Otherwise, due to geometric and kinematic differences between the navigation satellite and the spoofer or the pulling off of the spoofing, the spoofing may disrupt the consistency between the observation and prediction of IA-DOA. Theoretically, since the proposed method makes no assumptions about spoofing, it can detect multi-antenna spoofing. We conducted a Monte Carlo simulation to analyze the impact of different parameters on spoofing detection performance and conducted experimental verification and evaluation through open datasets. The results show that the method proposed in this article can effectively detect multi-antenna spoofing, reducing the requirements of receiver antennas for spoofing detection methods based on signal spatial correlation. [ABSTRACT FROM AUTHOR]

In the face of the challenges posed by the advent of 5G, Internet of Things (IoT) and requirements of future communication generations, the urgent need for cutting‐edge technologies capable of adapting their operation to a variety of dynamic tasks for efficient signal propagation management is clear. Connected intelligent metasurfaces, distinguished by their ability to dynamically and autonomously adapt their responses, and able to interact with an external uncontrollable and fast‐changing communication environment to benefit from collective intelligence so as to determine the required action, are emerging as an innovative solution in this field. Herein, an intelligent connected system based on a programmable metasurface reflector, is presented and assessed in two scenarios simulating dynamic uplink and downlink communications. For the uplink configuration, the metasurface is exploited for the estimation of the direction of arrival (DOA) of an incoming wave, while for the downlink scenario, it enables the automated tracking of mobile targets using global positioning system (GPS) data updated from a real‐time database. Experimental results highlight the responsiveness of the metasurface and demonstrate efficiency in wave focusing and mobile target tracking. [ABSTRACT FROM AUTHOR]

In sound signal processing, angle of arrival indicates the direction from which a propagating sound signal arrives at a point where multiple omnidirectional microphones are positioned. Considering a small underwater platform (e.g., underwater unmanned vehicle), this article addresses how to estimate a non-cooperative target's signal direction utilizing the minimum number of omnidirectional microphones. It is desirable to use the minimum number of microphones, since one can reduce the cost and size of the platform by using small number of omnidirectional microphones. Suppose that each microphone measures a real-valued sound signal whose speed and frequency information are not known in advance. Since two microphones cannot determine a unique AOA solution, this study presents how to estimate the angle of arrival using a general configuration composed of three omnidirectional microphones. The effectiveness of the proposed angle of arrival estimator utilizing only three microphones is demonstrated by comparing it with the state-of-the-art estimation algorithm through computer simulations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS. [ABSTRACT FROM AUTHOR]

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

In this paper a comparative study, restricted to one-dimensional stationary case, between several Direction of Arrival (DOA) estimation algorithms of narrowband signals is presented. The informative signals are corrupted by an Additive White Gaussian Noise (AWGN), to show the performance of each method by applying directly the algorithms without pre-processing techniques such as forward-backward averaging or spatial smoothing. [ABSTRACT FROM AUTHOR]

Cognitive Radio Networks (CRNs) aim to optimize the limited frequency spectrum by enabling spectrum sharing among different networks and making use of unoccupied frequency bands. The combination of massive multiple-input multiple-output (mMIMO) and CRNs has the potential to greatly improve the efficiency of upcoming wireless communication networks. In our research, we introduce an innovative approach to spectrum sharing in cognitive radio, utilizing 3D spatial data acquisition and Deep Learning for learning and decision-making. We incorporate mMIMO structures into cognitive radio base stations (CRBS) to extract angular information from user equipment (UE) and estimate Direction of Arrival (DoA) using Iterative Hard Thresholding (IHT). Our method involves deploying two base stations per cell for comprehensive 3D spatial spectrum coverage during spectrum prediction. We employ advanced Deep Learning techniques for spectrum sensing instead of reinforcement learning, enhancing CRN performance. Our approach includes a two-fold spectrum scheduling strategy, one focused on maximizing CR coverage and the other on optimizing transmission rates in CRN mMIMO scenarios. By fine-tuning Deep Learning parameters, our model achieves significantly higher Average Aggregate Sum Rate (AASR) compared to previous CRN spectrum sharing methods, without relying on reinforcement learning for spectrum sensing. Our research underscores the effectiveness of integrating Deep Learning into cognitive radio networks, offering the potential for enhanced spectrum utilization and network performance. Additionally, we address energy efficiency using the Nakagami fading channel model and evaluate key metrics, including channel occupancy and energy efficiency, through experimental analysis.

The auditory system is considered one of the crucial pathways for robots to perceive environmental information. The perception and decision-making capabilities of mobile robots are greatly enhanced by accurate and effective sound source localization, making it highly significant for applications in hazardous environment rescue and inspection. With the widespread adoption of deep learning, the effectiveness of sound source localization has been notably improved through the introduction of convolutional neural networks (CNNs). Sound source localization for mobile robots was comprehensively compared and analyzed from four perspectives: network architecture and improvements, types of sound features, data simulation and augmentation, as well as the fusion of multimodal information. Reflections and prospects on the application of the technology are also presented. [ABSTRACT FROM AUTHOR]

Spoofing against the Global Navigation Satellite System (GNSS) is an attack with strong concealment, posing a significant threat to the security of the GNSS. Many strategies have been developed to prevent such attacks, but current detection methods based on signal direction for multi-agent spoofing require multiple antennas/receivers, leading to increased cost and complexity in implementation. Additionally, methods utilizing a moving single antenna cannot effectively detect multi-agent spoofing. Therefore, we introduce a novel spoofing-detection technique based on the intersection angle between two directions of arrival (IA-DOA) using a single rotating antenna. The essence of this approach lies in estimating the IA-DOA between a pair of signals by utilizing the carrier-to-noise ratio (CNR) and carrier phase single difference (CPSD) of the received signal. The estimation of IA-DOA should be consistent with the prediction when there is no spoofing. With spoofing, it is difficult to accurately simulate the directionality of navigation signals, which can disrupt the consistency between the estimation and prediction of IA-DOA. Therefore, estimations and predictions of IA-DOA can be used to establish detection variables through generalized likelihood ratio testing (GLRT) to detect multi-agent spoofing. We conducted a simulation to analyze the impact of the antenna's parameters on the detection performance and evaluated it through on-site experiments. The results indicate that the method proposed in this article can efficiently achieve real-time detection of multi-agent spoofing. [ABSTRACT FROM AUTHOR]

The paper presents the concept of the method of determining the direction of ultrasonic signal arrival, i.e., the azimuth and elevation angles. This method is an extension of the previous approach which was proposed to determine only the azimuth angle. The approach is based on the indirect phase determination. This makes it possible to tolerate spacing of receivers greater than half the wavelength of the received signal. At the same time, it provides increased measurement accuracy and reduced hardware requirements. To check the robustness of the method, simulations were carried out for the geometric arrangement of the receivers of the sonar module, for which the method was then implemented. This sonar module was used in the conducted experiments. The results of these simulations and experiments are included in the paper and discussed. [ABSTRACT FROM AUTHOR]

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.

In sound signal processing, angle of arrival indicates the direction from which a propagating sound signal arrives at a point where multiple omnidirectional microphones are positioned. Considering a small underwater platform (e.g., underwater unmanned vehicle), this article addresses how to estimate a non-cooperative target’s signal direction utilizing the minimum number of omnidirectional microphones. It is desirable to use the minimum number of microphones, since one can reduce the cost and size of the platform by using small number of omnidirectional microphones. Suppose that each microphone measures a real-valued sound signal whose speed and frequency information are not known in advance. Since two microphones cannot determine a unique AOA solution, this study presents how to estimate the angle of arrival using a general configuration composed of three omnidirectional microphones. The effectiveness of the proposed angle of arrival estimator utilizing only three microphones is demonstrated by comparing it with the state-of-the-art estimation algorithm through computer simulations.

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).

Spoofing against the Global Navigation Satellite System (GNSS) is an attack with strong concealment, posing a significant threat to the security of the GNSS. Many strategies have been developed to prevent such attacks, but current detection methods based on signal direction for multi-agent spoofing require multiple antennas/receivers, leading to increased cost and complexity in implementation. Additionally, methods utilizing a moving single antenna cannot effectively detect multi-agent spoofing. Therefore, we introduce a novel spoofing-detection technique based on the intersection angle between two directions of arrival (IA-DOA) using a single rotating antenna. The essence of this approach lies in estimating the IA-DOA between a pair of signals by utilizing the carrier-to-noise ratio (CNR) and carrier phase single difference (CPSD) of the received signal. The estimation of IA-DOA should be consistent with the prediction when there is no spoofing. With spoofing, it is difficult to accurately simulate the directionality of navigation signals, which can disrupt the consistency between the estimation and prediction of IA-DOA. Therefore, estimations and predictions of IA-DOA can be used to establish detection variables through generalized likelihood ratio testing (GLRT) to detect multi-agent spoofing. We conducted a simulation to analyze the impact of the antenna’s parameters on the detection performance and evaluated it through on-site experiments. The results indicate that the method proposed in this article can efficiently achieve real-time detection of multi-agent spoofing.

Recently, deep learning techniques have been applied in the field of direction of arrival (DOA) to enhance beamforming in various practical applications, such as traffic positioning and hydro-acoustic detection. However, the effectiveness of these algorithms relies heavily on the availability of accurate training data that closely resembles real-world data, which can be challenging to obtain, particularly in hydro-acoustic environments. This is primarily due to the limited array gain (AG) and the constant fluctuations in ambient noise. Additionally, the presence of non-Gaussian noise in the background further complicates the process of identifying the desired signal, and it is worth noting that state-of-the-art deep learning methods have a limitation in detecting up to four sources. To address these challenges, we propose a novel method that employs a diagonal beam spectrum (DBS) feature to suppress non-Gaussian noise and enhance array gain. By incorporating DBS, our method surpasses the theoretical 10logM array gain and achieves an impressive 25logM array gain, while effectively detecting signals as low as -38dB (where M denotes the number of sensors). We also employ SA-U-NET, an image edge detection network based on DBS, to detect more than four sound sources and optimize angular positioning. The experiments focus on accuracy and array gain improvements of algorithms. Our proposed method achieves up to 91.67% correctness and 35dB array gain (i.e., 4 times better than the next best baseline) in experiments compared to other methods, demonstrating the potential of DBS and SA-U-NET. • A beamforming algorithm for insufficient array gain under non-Gaussian noise. • Enhance angle search capability by sending spatial high-order spectra to SA-UNet. • Estimation robust to its initial assumption, under narrowband and wide-band occasions. • Improved array gain to 25logM with angle accuracy exceeding 95% in low SNR cases. • Reduced demand of similarity between test and training data for deep learning. [ABSTRACT FROM AUTHOR]

Real-time source localization test for the moving multiple quadcopter drones. Spatial scene analysis using an open-access audio dataset for machine learning. [Display omitted] • A novel visual representation method for multi-dimensional sound scene analysis. • Representation of the estimated localization result as RGB color channels. • Encoding with any localization algorithm in the preprocessing. • Possibility of adopting any localization method in the preprocessing for encoding. • Human-interpretable dataset capable of quantitative analysis with decoding. • Exported image file including metadata for decoding. A visual representation method, DoAgram, for multi-dimensional sound scene analysis is suggested. The visual representation of the sound source localization result gives intuitive information about the estimated one to the end user. Also, image-based deep learning is popularly used in the acoustic field nowadays, so such a visualized one can be used for data augmentation. To analyze the spatial sound scene for the moving source, the method displays the estimated azimuth angle and elevation angle of the source, and its corresponding time stamp and frequencies as RGB color channels and metadata by mapping the spatial coordinate to color space. Even though the suggested method is human-interpretable, decoding is needed for the quantitative analysis. Therefore, the time and frequency scanning method, and a histogram to estimate the DoA of the source are proposed. An experiment is conducted in an anechoic chamber to localize two quadcopter drones that have a mean angular velocity of 8°/s ± 9°/s (95 % CI) and 25°/s ± 31°/s (95 % CI), respectively, and the spatial sound scene analysis is implemented using the proposed methods. The test result shows that the trajectories with respect to the time of each source are well separated. Also, an additional test is conducted using an open-access audio dataset for machine learning. The cumulative source mapping method is adopted for the spatial sound scene analysis, and the decoded result shows that the DoAgram is feasible to adopt for machine learning applications. [ABSTRACT FROM AUTHOR]