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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]

Now a days, huge amount of data is communicated through channels in wireless network. It requires an efficient parallel operation for the optimal utilization of frequency, time allocation and coding model for signal subspace factorization in smart antenna. In view of this requirement, an efficient reconfigurable optimal source detection and beam allocation algorithm (RoSDBA) is proposed. The proposed algorithm is able to allocate desired signal to the user space to reduce the noise and also for efficient allocation of subspace to remove disturbance in all directions. The proposed method efficiently utilizes the antenna array elements by accurate identification and allocation of antenna array elements such as individual radiators, radiation beam, signal strength, and disturbance factor. With respect to simulation analysis, the proposed method shows better performance for the resolution, radiation beam allocations, identification bias, distribution factor and time taken for the detection of various array arrangements and source numbers. [ABSTRACT FROM AUTHOR]

Perturbation analysis of direction of arrival (DOA) estimation has been studied mostly on one-dimensional arrays rather than planar arrays, which remains room for exploration. For uniform circular arrays with various model errors, we establish the corresponding system model. Then a performance analysis of the two-dimensional MUSIC algorithm is proposed by first-order error analysis and verified by simulation. The results show that the root-mean-squared error of DOA estimation is proportional to the standard deviation of the model errors, including gain-phase errors, sensor position errors, and mutual coupling effect.

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.

Aiming at the problem of the excessive error of direction of arrival (DOA) estimation caused by the position disturbance of a UAV swarm during flight, a robust polarization-DOA estimation method based on sparse Bayesian learning (SBL) is proposed. First, the algorithm decomposes the covariance matrix of the received data of the UAV swarm vector array and then constructs the determination matrix of the UAV position coordinates by exploiting the orthogonality of the eigenvalues and eigenvectors. Then, the optimal solution of the semi-positive definite programming (SDP) problem is solved using the constrained global least square method, and the exact self-positioning coordinates of UAVs are obtained. Second, we construct a spatially discrete grid to model the received data of the UAV group vector array. The SBL theory is then applied to obtain the posterior probability distribution of the sparse signal matrix. The sparsity of the signal matrix is controlled with a hyperparameter, and the estimation of the DOA is conducted using a fixed-point iteration to obtain the maximum posterior estimate of the signal matrix. Finally, according to the estimated DOA, the polarization parameter is obtained from the constructed objective function of the polarization parameter estimation. The simulation results show that the proposed algorithm achieves higher accuracy and robustness than the traditional 2D DOA estimation algorithm in the direction-finding system for UAV swarm vector arrays. [ABSTRACT FROM AUTHOR]

An investigation on fractal-based sub-array (FBSA), of isotropic elements, that evolves from two Non-uniform Linear arrays, i.e. the minimum redundancy array and fractal Array, with a hole-free difference co-array shows competitive performance in direction of arrival estimation using (spatial smoothing-) MUSIC algorithm. Numerical experiments demonstrate that the algorithm can resolve 2° separation between sources. Considering mutual coupling, in terms of RMSE, the FBSA performs better than other competing configurations. [ABSTRACT FROM AUTHOR]

A novel approach that does not require the number of sources as a priori is proposed to estimate the direction of arrival (DOA) based on a sparse non-uniform linear antenna array. To ensure the identifiability of the DOA, a specific configuration scheme of sparse array is designed. Based on this specific sparse array, firstly the fourth-order cumulant (FOC) is adopted to eliminate the impact imposed by Gaussian noise. Secondly, to circumvent eigenvalue decomposition or singular value decomposition, a propagator is constructed by using a Hermitian FOC matrix and a hyperparameter. Finally, a projection onto an irregular Toeplitz set is proposed to further improve estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper studies blind source separation (BSS) for frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over (i) line-of-sight (LOS), (ii) single-cluster, and (iii) multiple-cluster Spatial Channel Model (SCM) settings. The sources are stationary, spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by leveraging direction of arrival (DOA) information through an FH estimation stage, a DOA estimation stage, and a pairing stage with the latter associating FH patterns with physical sources via their estimated DOAs. Current methods in the literature do not perform the association of multiple frequency hops to the sources they are transmitted from. We bridge this gap by pairing the FH estimates with DOA estimates and labeling signals to their sources, irrespective of their hopped frequencies. A state filtering technique, referred to as hidden state filtering (HSF), is developed to refine DOA estimates for sources that follow a HMM. Numerical results demonstrate that the proposed approach is capable of separating multiple intermittent FH sources. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation for a sparse channel has attracted serious attention recently. Better signal analysis and denoising achieve accuracy in DOA determination. This paper proposes an underdetermined DOA estimation for multiple input and multiple outputs (MIMO) sparse channels. A novel multi-kernel-based non-negative sparse Bayesian learning (MK NNSBL) framework is implemented using the multiplied form of basis vector within the manifold matrix for a defined grid. Meanwhile, virtual antenna locations are reconfigured by exploiting the conventional cuckoo search algorithm (CCSA) for the fine reception of incoming signals on a nonuniform linear array (NULA). The simulated results reveal that the novel approach outperforms in its optimal root mean square error (RMSE) for various signal-to-noise ratio (SNR) limits and the compilation time. The convergence comparative graph indicates the improved performance in the proposed framework over existing algorithms. [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.

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).

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter's signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model's generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our results demonstrate an average reduction of over 90% in the prediction error and 50% in the prediction time achieved by our proposal when compared to the MUSIC algorithm. These results establish DTs as competitive alternatives for DOA estimation in signal reception systems. [ABSTRACT FROM AUTHOR]

This paper proposes a diffuseness estimation-based single-source time–frequency point (SSTP) detection method for multisource direction of arrival (DOA) estimation. According to the composition, time–frequency (TF) points are divided into three types: SSTP, multisource TF, and interference TF. SSTPs and multisource TF points are defined as weak interference time–frequency points (WITPs). An SSTP is a TF point consisting only of the direct component of one sound source, which is beneficial for DOA estimation. Therefore, multisource DOA estimation is transformed into single-source DOA estimation by SSTP detection. Diffuseness estimation is introduced for a sound field microphone array. WITPs are detected by a diffuseness estimation–based detection method. Phase similarity determination is adopted to identify SSTPs from detected WITPs. Multiple sound source localization is completed by searching peaks in the normalized histogram of DOA estimates corresponding to the detected SSTPs. Experiments demonstrate that the proposed method achieves the precise detection of SSTPs, and evaluations show that it has superior accuracy of multiple sound source counting and localization in reverberant and noisy environments. [ABSTRACT FROM AUTHOR]

In this research article, a concept for a secondary RAdio Direction And Ranging (RADAR) angle of arrival based system with cooperative targets transmitting at 2.4 GHz and using Multiple Signal Classification (MuSiC) to determine the angles of incidence is investigated. In addition to introducing common algorithms and presenting thorough derivations, the system is first examined through simulations. To prove the concept, hardware, firmware, and software are developed. For MuSiC, we propose three novel methods to obtain the correct incident angle from the spectrum, especially in strong multipath environments. These methods work either for a single spectrum or for a combination recorded at multiple times. Together with the estimated angles of incidence, our methods determine measures on the respective likelihoods. Based on this, we additionally propose two algorithms for computing the final position. Our system is characterized in both a simple 20 m × 15 m outdoor and a 17 m × 13 m multipath indoor environment, where we achieve a mean angular error of 3◦ and a mean positioning error of 0.67 m for the former using only four base stations with four antennas each. Our novel approach shows position accuracy improvements of 15% outdoors and 25% indoors compared to classical MuSiC estimation. [ABSTRACT FROM AUTHOR]

The canonical polyadic decomposition (CPD) of higher-order tensors, a.k.a. PARAFAC, has shown excellent performance in two-dimensional direction of arrival (DOA) estimation using the acoustic vector sensor array (AVSA). However, most existing studies pay little attention to the manifold matrix structure of the AVSA during the CPD and are designed for uncorrelated sources. This paper presents a constrained CPD-based algorithm for DOA estimation using a uniform linear AVSA, whose manifold matrix is highly structured. Specifically, the manifold matrix equals to the Khatri-Rao product of a Vandermonde matrix and a proportional column-norm matrix. We show that DOA estimation accuracy is further improved by incorporating the prior structured information. Besides, we also extend the Toeplitz decorrelation technique to the AVSA to handle possibly correlated sources. The algorithm does not require iteration or peak searching and thus is computationally effective. Numerical simulations verify the effectiveness and superior performance of the algorithm. [ABSTRACT FROM AUTHOR]

Beamforming is one of the most important techniques for multibeam echo sounder (MBES) for engineering applications, such as seafloor topographic survey and underwater target detection. However, due to the Rayleigh limit in the conventional beamforming (CBF) algorithm, the output of the beamforming is often limited by the aperture and the number of elements, thus leading to low resolution and signal-to-noise ratio (SNR). To further improve the resolution and array gain (AG) of the beamforming algorithm for MBES, a dynamic background suppression deconvolved multiple signal classification (DBSD–MUSIC) algorithm is proposed in this work. The dynamic variable point scattering function (PSF) is constructed using the signal characteristics of the signal covariance matrix for each moment. The MUSIC azimuth spectrum is deconvolved using the dynamic PSF and Richardson–Lucy (R–L) iterative algorithm. Thus, the azimuth spectrum of DBSD–MUSIC algorithm is obtained. Based on the analysis and comparison of the simulation results and experimental multibeam data, we show that the resolution of the proposed algorithm is better than the traditional MUSIC algorithm. In addition, it also has a better AG and the direction of arrival (DOA) estimation performance. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the timedomain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment. [ABSTRACT FROM AUTHOR]

In Structural Health Monitoring (SHM) applications, the Direction of Arrival (DoA) estimation of Guided Waves (GW) on sensor arrays is often used as a fundamental means to locate Acoustic Sources (AS) generated by damages growth or undesired impacts in thin-wall structures (e.g., plates or shells). In this paper, we consider the problem of designing the arrangement and shape of piezo-sensors in planar clusters in order to optimize the DoA estimation performance in noise-affected measurements. We assume that: (i) the wave propagation velocity is unknown, (ii) the DoA is estimated via the time delays of wavefronts between sensors, and (iii) the maximum value of the time delays is limited. The optimality criterion is derived basing on the Theory of Measurements. The sensor array design is so that the DoA variance is minimized in an average sense by exploiting the Calculus of Variations. In this way, considering a three-sensor cluster and a monitored angles sector of 90°, the optimal time delays–DoA relations are derived. A suitable re-shaping procedure is used to impose such relations and, at the same time, to induce the same spatial filtering effect between sensors so that the sensor acquired signals are equal except for a time-shift. In order to achieve the last aim, the sensors shape is realized by exploiting a technique called Error Diffusion, which is able to emulate piezo-load functions with continuously modulated values. In this way, the Shaped Sensors Optimal Cluster (SS-OC) is derived. A numerical assessment via Green's functions simulations shows improved performance in DoA estimation by means of the SS-OC when compared to clusters realized with conventional piezo-disk transducers. [ABSTRACT FROM AUTHOR]

For large-scale high-dimensional positioning scenes, the massive number of grid points brings challenges to the multi-target positioning algorithms based on compressed sensing. To cope with the challenges, a fast multi-target localization method based on direction of arrival is proposed. A compressed sensing model is constructed for multi-target localization based on the DOA sequence measured by positioning nodes. Then, a two-stage fast matching pursuit algorithm is presented for sparse reconstruction, which consists of preliminary estimation and supports rectification. A process similar to orthogonal matching pursuit algorithm is adopted to get preliminary estimate result, but no nonlinear operations is employed for complexity reduction. Then another iterative process is carried out to rectify the chosen supports in preliminary result sequentially. Simulation results verify the effectiveness and accuracy of the proposed method for multi-target localization.

The ionosphere can make the short-wave source localization problem further non-linear, leading to a complicated solution process. This paper proposed a coordinated positioning algorithm based on the ionospheric virtual height (IVH) model to jointly estimate the target position and ionosphere reflection height using direction of arrival (DOA) and time difference of arrival (TDOA) measurements. The method is divided into two stages, the first stage is DOA localization and the second stage is TDOA localization. For the difficulty of establishing the pseudo-linear equation of elevation angle in the first stage, this paper proposed solving quadratic equations to establish the pseudo-linear equation. Moreover, since the TDOA pseudo-linear equations require the target position, the TDOA pseudo-linear equations can be established based on the estimates of DOA stage, which can lead to cooperative localization. Based on the pseudo-linear equations, the short-wave source localization problem is modeled as an optimization problem with double quadratic equation constraints. This paper proposed to solve the optimization problem combining the differentiable exact penalty method. The proposed method has a larger convergence region and requires fewer iterations to converge than the Lagrange method. Theoretical analysis shows that the localization performance of the proposed method can reach the Cramér-Rao lower bound (CRLB) and has higher accuracy than single parameter localization. Finally, the validity of theoretical analysis and the differentiable exact penalty method is verified by simulations.

Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots. We address this by reformulating source detection as a multi-class classification problem solved using deep learning frameworks. Incoming waveforms are sampled using a centro-symmetric linear array with omni-directional elements and the normalized upper triangle of the autocorrelation matrix is extracted as the input feature to a modified convolutional neural network with uni-dimensional filters, trained to detect the sources in the presence of both uncorrelated and correlated signals. Two detection algorithms are introduced and referred to as CNNDetector and RadioNet, and subsequently benchmarked against the conventional source detection algorithms. By including pre-processing in forward backward spatial smoothing, RadioNet can also resolve the number of uncorrelated sources in the presence of correlated paths. Finally, the algorithms are stress tested under challenging operational conditions and extensive evaluations are presented showing the efficacy and contributions of the introduced predictive models. To the best of our knowledge, this is the first time the source detection problem has resolved L-1 sources, for an antenna array of L elements using a deep learning framework.

This paper proposes a single circular radiator with a multi-port (SRMP) antenna that can estimate the direction-of-arrival (DoA) in the azimuth and elevation directions. The proposed SRMP antenna is designed to minimize the size of the ultra-wideband system by using only one patch radiator. To verify the feasibility, the proposed antenna is fabricated, and the reflection coefficient and boresight gain are measured (−13.3 dB and 3.4 dBi at 8 GHz). Then, to observe the direction-finding performance, the DoA estimation results using the Bartlett beamformer are compared with the typical array. At all incident angles, a root-mean-square error of less than 1° is observed when the signal-to-noise ratio is higher than 6 dB.

This paper illustrates a flexible design strategy for a three-element non-uniform linear array (NULA) aimed at estimating the direction of arrival (DoA) of a source of interest. Thanks to the spatial diversity resulting from non-uniform sensor spacings, satisfactory DoA estimation accuracies can be achieved by employing a very limited number of receiving elements. This makes NULA configurations particularly attractive for low-cost passive location applications. To estimate the DoA of the source of interest, we resort to the maximum likelihood estimator, and the proposed design strategy is obtained by constraining the maximum pairwise error probability to control the errors occurring due to outliers. In fact, it is well known that the accuracy of the maximum likelihood estimator is often degraded by outliers, especially when the signal-to-noise power ratio does not belong to the so-called asymptotic region. The imposed constraint allows for the defining of an admissible region in which the array should be selected. This region can be further modified to incorporate practical design constraints concerning the antenna element size and the positioning accuracy. The best admissible array is then compared to the one obtained with a conventional NULA design approach, where only antenna spacings multiple of λ / 2 are considered, showing improved performance, which is also confirmed by the experimental results. [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.

An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly

Abstract Spread spectrum communication is a common communication method in underwater communication. Based on the space-time processor received by the array, it can filter the signals arriving along each path separately. Combined with the diversity of space-time clusters, it can effectively improve the communication system’s reliability. The core problem of the space-time processor is the direction of arrival (DOA) and signal source number estimation. Based on the good self-coherence of the spread spectrum sequence, this paper proposes a multiple signal classification algorithm (MUSIC) for accurate DOA estimation. However, since the MUSIC algorithm uses the received signal’s covariance matrix for DOA estimation, the number of sources needs to be predicted in advance. Under a low signal-to-noise ratio (SNR), the signal eigenvalues and the noise eigenvalues of the covariance matrix differ slightly, which makes signal source number estimation difficult. To address this issue, a singular value decomposition method using the delay structure information of the array element is proposed to estimate the number of sources of the spreading sequence under a low SNR. The method proposed in this paper can well estimate the DOA of the signal under a low SNR. Meanwhile, there is no need to convert the signal to the individual sub-bands, which effectively reduces the calculation overhead. At the same time, the Hankel matrix is used to solve the problem that the MUSIC algorithm cannot accurately estimate the number of signal sources under the condition of low SNR. Compared with the conventional algorithm, the Hankel matrix can more accurately estimate the number of signal sources in the case of low SNR. Through simulation experiments, the effectiveness of our DOA estimation algorithm is validated under a low SNR.

Abstract By means of spatial clustering and time-frequency masking, a mixture of multiple speakers and noise can be separated into the underlying signal components. The parameters of a model, such as a complex angular central Gaussian mixture model (cACGMM), can be determined based on the given signal mixture itself. Then, no misfit between training and testing conditions arises, as opposed to approaches that require labeled datasets to be trained. Whereas the separation can be performed in a completely unsupervised way, it may be beneficial to take advantage of a priori knowledge. The parameter estimation is sensitive to the initialization, and it is necessary to address the frequency permutation problem. In this paper, we therefore consider three techniques to overcome these limitations using direction of arrival (DOA) estimates. First, we propose an initialization with simple DOA-based masks. Secondly, we derive speaker specific time annotations from the same masks in order to constrain the cACGMM. Thirdly, we employ an approach where the mixture components are specific to each DOA instead of each speaker. We conduct experiments with sudden DOA changes, as well as a gradually moving speaker. The results demonstrate that particularly the DOA-based initialization is effective to overcome both of the described limitations. In this case, even methods based on normally unavailable oracle information are not observed to be more beneficial to the permutation resolution or the initialization. Lastly, we also show that the proposed DOA-guided source separation works quite robustly in the presence of adverse conditions and realistic DOA estimation errors.

High resolution direction of arrival estimation for closely spaced target in low signal to noise ration (SNR) is an active research area in underwater acoustic and SONAR signal processing The resolution performance of classical/traditional subspace based algorithms including MUSIC and ESPRIT procedures degraded under sever scenarios of low signal to noise ratio (SNR), less number of samples/snapshots and short distant targets. In this work, optimization courage of swarming intelligence of grey wolf optimization (GWO) is carried out for viable DOA estimation in the challenging instances of worst underwater scenarios using a uniform linear array (ULA). The high resolution DOA for short distant emitters is achieved using less number of samples vitally with GWO by inspecting the global minima of the highly nonlinear objective model of ULA having multi local minimas. The performance is analyzed for different number of targets employing estimation accuracy, resolution certainty, variability analysis, frequency distribution of RMSE, robustness against different SNR of additive-white Gaussian measurement noise and comparative studies with MUSIC and Particle Swarm Optimization PSO with the validation strength of Cramer Rao bound reveals the valuation of the scheme. [ABSTRACT FROM AUTHOR]

Given the increasing number of spoofing attacks, keeping global navigation satellite system transmissions secure has recently become a focus. Many approaches have been proposed to defend against spoofing. Typical antispoofing methods against a multiagent spoofer usually use the direction of arrival (DOA) with an antenna array to detect spoofing. However, not only the process of calibration but also the information on the attitude and position of the antenna array platform is required in advance, which means high cost and complexity in terms of implementation. To this end, we propose an antispoofing method based on the intersection angle between two directions of arrival (IA-DOA) for a multiantenna receiver. This angle metric is presented here for the first time. Once spoofing exists, the IA-DOA may not be satisfied with the specific constraint. In addition, the IA-DOA is demonstrated to be insensitive to the attitude of the platform. It is shown that the proposed method can significantly relax the requirements of antispoofing. The method can address spoofing detection and make it possible for spoofing classification. Furthermore, a generalized likelihood ratio test and a simplified detector are developed in the case of a quadruple-antenna configuration with a particular geometry and noise covariance. Finally, the statistical characteristics are analyzed theoretically through Monte Carlo simulations, and field experiments verify the detection performance. [ABSTRACT FROM AUTHOR]