Direction Of Arrival Research Articles

DOA method based on CSI and improved neural network

Abstract In wireless communication systems, accurate estimation of signal direction has always been a key research area. Channel State Information (CSI) is an important parameter that can be used to estimate the Direction of Arrival (DOA) of a signal. In order to improve the accuracy and robustness of DOA estimation, this study aims to explore a DOA estimation method based on CSI and improved neural networks. Firstly, the role of CSI in localization technology and related theories are detailed. Subsequently, the application of neural network-based fingerprint matching algorithms, including BP neural networks, Whale Optimization Algorithm (WOA), and chaotic mapping techniques, is analyzed. In light of this, an improved BP neural network integrating chaotic mapping and adaptive weighting is proposed to improve the positioning accuracy and convergence rate of the algorithm. Finally, through the collection and training of experimental data, an algorithm proposed in this article achieves a test set accuracy of 96.4286% and a training set accuracy of 82.7243%. The better training effect is obtained, which verifies the efficacy and superiority of the proposed approach.

2-D DOA Estimation of Coherent Signals Using the Dual-Size Spreading Array

Abstract We introduce a novel approach for high-precision estimation of two-dimensional (2-D) Direction-of-Arrival (DOA) under conditions of coherent interference. This study innovatively devises an antenna configuration, termed the Dual-Size Spreading Array, specifically tailored to estimate DOAs in such environments. The method involves integrating a subarray with the primary array to create a Dual-Size Extended Array structure. This integration allows for a transformation of the covariance matrix into a new form, where its rank solely depends on the number of impinging waves. Both the subarray and primary array must have a scale no less than the number of incoming wavefronts. This proposed technique not only amplifies the effective aperture but also significantly reduces the computational intricacy associated with DOA estimation procedures. The efficacy of our methodology has been substantiated through simulation results, which demonstrate that it outperforms the conventional spatial smoothing techniques. Moreover, we provide several practical guidelines designed to enhance estimation accuracy while concurrently minimizing computational demands. In summary, this research presents an advanced solution that effectively addresses challenges in 2D DOA estimation under coherent scenarios, achieving improved performance and computational efficiency compared to existing methods.

DIRECTION OF ARRIVAL ESTIMATION BASED ON SMOOTH MUSIC ALGORITHM COMBINED SPECTRUM SELECTION TECHNIQUES OF NOISE CHARACTERISTICS OF MARINE TARGETS EQUIPPED PROPELLER

This article demonstrates a proposal for a direction of arrival (DOA) estimation algorithm for passive sonar systems based on the combination of the smooth multiple signal classification (Smooth MUSIC) algorithm and frequency bin selection (BS) of the sound of marine propeller-equipped targets, so-called BS-SMUSIC. The harmonics of propeller shaft and blade frequencies are considered useful information for detecting targets and estimating their parameters, including DOA. Low frequency analysis recording (LOFAR) is utilized to implement BS. By analyzing MUSIC-based algorithm together BS, passive DOA estimation algorithms have been significantly improved. Simulation results have been given in both cases: uncorrelated and correlated sources. Outcomes demonstrate that the BS-SMUSIC algorithm has the best performance in comparison to three MUSIC-based algorithms (MUSSIC, Modified MUSIC and Root MUSIC) in all investigation cases: narrow band signals at 5 dB of signal to noise (SNR); wideband signals at -5 dB of SNR. Besides, BS-SMUSIC algorithm also archives the lowest root mean square error (RMSE) in SNR range -5 dB to 20 dB. In the SNR range, the RMSE of BS-SMUSIC algorithm remains stable and is about 0.3 ◦ at -5 dB of SNR. This performance reflects its ability to ensure stable operation, super resolution and high accuracy of BS-SMUSIC algorithm for the passive sonar systems.

Localization of Speaker using Fusion Techniques and Neural Network Algorithms

ABSTACT Sound source localization especially speech and speaker is sole of the most significant techniques recently because used in various applications like smart environments, industry, robots, and audio conferences. So, the usage of these techniques needs more accuracy. In this paper, a speaker localization proposed it depends on the speech signals in closed spaces by employing fusion techniques and neural networks (NN) algorithms to get more accuracy. The proposed work included finding the classification of the speaker signals, which included three phases: the preprocessing phase, the phase of the feature extraction and classification phase. Data Fusion technique used to generate the dataset of speakers. In feature extraction phase features fusion technique was used for constructing a feature vector by using Generalized Cross Correlation (GCC) for time delay estimation, Root_MUSIC, and Minimum Variance Distortion Less (MVDR) for a direction of arrival for the signal source. In the classification stage two NN algorithms used, Restricted Boltzmann Machine (RBM), which implemented using Tensor flow library and Long Short-Term Memory (LSTM), which implemented using Keras library. The experiments results shows that the accuracy of the two methods was 99.84%, 99.15% for RBM, and LSTM respectively.

Distributed extended Kalman filtering for acoustic simultaneous localization and environment mapping

The localization accuracy of Acoustic Simultaneous Localization and Mapping (ASLAM) often suffers from environmental noise and room reverberation. To solve this problem, an ASLAM method based on distributed extended Kalman filter is proposed. Specifically, the distance between the sound source and the robot is effectively estimated by the gating mechanism method in the case of unknown location of the sound source. Then, the Time Difference of Arrival (TDOA) between robots, the Direction of Arrival (DOA) and the distance between the sound source and the robot are presented as observation. Next, the robots' trajectories and the sound source's position are tracked by multiple extended Kalman filters. Finally, the ASLAM localization is completed by fusing the sound source and the robots' positioning information through average consensus algorithm, respectively. The proposed method can efficiently promote the ASLAM localization accuracy in complex acoustic environments. The experimental results verify the effectiveness of the proposed method.

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning.

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

GLFER-Net: a polyphonic sound source localization and detection network based on global-local feature extraction and recalibration

Polyphonic sound source localization and detection (SSLD) task aims to recognize the categories of sound events, identify their onset and offset times, and detect their corresponding direction-of-arrival (DOA), where polyphonic refers to the occurrence of multiple overlapping sound sources in a segment. However, vanilla SSLD methods based on convolutional recurrent neural network (CRNN) suffer from insufficient feature extraction. The convolutions with kernel of single scale in CRNN fail to adequately extract multi-scale features of sound events, which have diverse time-frequency characteristics. It results in that the extracted features lack fine-grained information helpful for the localization of sound sources. In response to these challenges, we propose a polyphonic SSLD network based on global-local feature extraction and recalibration (GLFER-Net), where the global-local feature (GLF) extractor is designed to extract the multi-scale global features through an omni-directional dynamic convolution (ODConv) layer and multi-scale feature extraction (MSFE) module. The local feature extraction (LFE) unit is designed for capturing detailed information. Besides, we design a feature recalibration (FR) module to emphasize the crucial features along multiple dimensions. On the open datasets of Task3 in DCASE 2021 and 2022 Challenges, we compared our proposed GLFER-Net with six and four SSLD methods, respectively. The results show that the GLFER-Net achieves competitive performance. The modules we designed are verified to be effective through a series of ablation experiments and visualization analyses.

A geometric solution to microphone array position calibration and its confidence interval analysis

Wireless acoustic sensor networks (WASNs) comprised of spatially distributed acoustic nodes have been increasingly popular in many areas such as speech enhancement, blind source separation, and audio surveillance, where node position information is generally required to implement relevant processing algorithms. In this article, we focus on the node position calibration only using direction-of-arrival (DOA) information with each node consisting of a microphone array. State-of-the-art techniques translate the position calibration into optimization of DOA-based cost functions, and have shown notable success. However, a compromise between position calibration accuracy and computational load still exists. Furthermore, none of the existing studies can offer a quantitative evaluation of calibration performance before the optimization procedure is finished. To address these problems, a geometric solution to array node position calibration is proposed using DOA measurements. For a given array node, DOAs of three sources are first utilized to construct cylinder, plane, and cone equations. Next, the two-dimensional projection of intersections among these equations is calculated using Sylvesterresultantmatrix. Finally, a closed-form solution of node position in three-dimensional space is established. Moreover, explicit mathematical derivation of the confidence interval analysis on the calibration results under given node angle estimation error condition is presented, indicating that the proposed method can provide predictable performance with corresponding probabilities. The Cramér-Rao bound (CRB) is further investigated. Simulation results reveal that as compared with existing methods our approach achieves comparable calibration accuracy with much less computational burden. Real-world experiments also verify its effectiveness.

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

A Novel Generalized Nested Array MIMO Radar for DOA Estimation with Increased Degrees of Freedom and Low Mutual Coupling.

In array signal processing, the mutual coupling among physical sensors can inevitably affect the estimation of the direction of arrival (DOA). Despite the fact that multiple-input and multiple-output (MIMO) radar can provide greater degrees of freedom (DOFs), the influence of mutual coupling is largely overlooked in many current MIMO radar designs. To tackle this issue, we propose the utilization of a generalized nested array (GNA) in transmitter array and we introduce an expansion factor into the nested array in the receiver array. Thereby, a novel GNA-MIMO radar is put forward. The proposed MIMO radar offers O(N4) consecutive DOFs with N sensors and avoids the adverse effects of high mutual coupling caused by closely located sensors. Furthermore, we derive the closed-form expressions for the position of physical sensors and the attainable consecutive DOFs of the proposed MIMO radar. Through simulation experiments, we demonstrate the superior accuracy of the proposed MIMO configuration in DOA estimation and angle resolution under the condition of mutual coupling effect.

Distance- and Angle-Based Hybrid Localization Integrated in the IEEE 802.15.4 TSCH Communication Protocol

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.

Spatial Spectrum Estimation of Weak Scattering Wave Signal in Range-Doppler Domain

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data.

Fast estimation of array shape and direction of arrival using sparse Bayesian learning for manoeuvring towed line array

AbstractThe sparse Bayesian learning (SBL) algorithm has demonstrated its advantage in the direction of arrival (DOA) estimation. However, it requires a lot of computational cost to iteratively estimate the SBL hyperparameters from measurement data. This paper focuses on fast estimating the array shape and DOAs using SBL for a short towed line array (TLA) during manoeuvring. A parabolic model is utilised to describe the bent TLA whose bow as a hyperparameter is estimated in the SBL iterative process. Then, the basis vector pruning strategy is considered in the iteration to reduce computational cost by neglecting the impossible directions of signal presence. The converged speed of the joint estimation algorithm is further improved by approximately calculating the posterior probability density with the message passing approach. The effectiveness of the optimising joint estimation algorithm is verified using the experimental results from South China Sea.

Adaptive track approach for multiple sources scenarios during radar survey for space surveillance applications

The increasing population of resident space objects is currently fostering many space surveillance initiatives, which rely on the use of on-ground sensors. In particular, survey radars allow to first characterise the target orbit from a single transit, through measurements which are Doppler shift, slant range and angular profile. In this framework, the Music Approach for Track Estimate and Refinement (MATER) algorithm was developed to compute the angular track through the MUltiple SIgnal Classification (MUSIC), by solving possible ambiguities which may arise because of the receiver array geometry.This work presents the MATER extension to the case in which multiple sources are simultaneously detected by the sensor. For each detected source, the signal Direction of Arrival (DOA) is computed through MUSIC, and, if no prediction is exploited, possible ambiguous solutions arise. The computation is repeated for the entire observation, and all the estimations related to a specific target are grouped based on the angular sequence shape and the detection epochs. Finally, the possible ambiguity problem is solved, and the angular profile is obtained through a quadratic regression in time.The algorithm is numerically tested on a survey observation simulation. The detection length depends on the impinging signal intensity, and the angular accuracy is in the order of 1e-03 deg. A sensitivity analysis highlights that a transmitted power decrease shortens the detection length, with no remarkable angular accuracy deterioration. An additional simulation shows that in proximity operation monitoring MATER performance depends on the mutual geometry between target and chaser, as it may bring down the reciprocal angular distance under the resolution level. Nevertheless, it is always possible to identify the presence of both sources through the eigenvalues analysis of the signal correlation matrix. Finally, the simulation of a fragments cloud observation highlights that MATER performance depends both on the size of the observed fragment, as this is strictly linked to the signal detected by the receiver, and on the simultaneous detection of other fragments with a more effective signal.

A nonconvex sparse recovery method for DOA estimation based on the trimmed lasso

Sparse direction-of-arrival (DOA) estimation methods can be formulated as a group-sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconvex regularized approach called the trimmed lasso for DOA estimation. We define the penalty term of the trimmed lasso in the multiple measurement vector model by ℓ2,1-norm. First, we use the smooth approximation function to change the nonconvex objective function to the convex weighted problem. Next, we derive sparse recovery guarantees based on the extended Restricted Isometry Property and regularization parameter for the trimmed lasso in the multiple measurement vector problem. Our proposed method can control the desired level of sparsity of estimators exactly and give a more precise solution to the DOA estimation problem. Numerical simulations show that our proposed method overperforms traditional approaches, which is more close to the Cramer-Rao bound.

Direction of arrival (DOA) estimation using switched beam antenna with butler matrix at mm-wave frequency

This paper presents a butler matrix-based microstrip hexagonal patch antenna with a direction of arrival (DOA) estimation approach for mm-wave application. The hexagonal-shaped patch antenna had been designed with a Z-shaped slot with an eight-port butler matrix. The DOA estimation is also done for the switched beam antenna using a novel direction of arrival (DOA) estimation algorithm known as Cramer-Rao lower bound (CRLB). The proposed design leads to significant size reduction and loss minimization. In addition to this, the design had the advantages of being low-cost, lightweight, and small-volume. The entire proposed design provides an operating frequency range of 28 GHz to 39 GHz with a center frequency of 33 GHz. The proposed work makes use of two butler matrices with two 45° phase shifters and 120° phase shifters. The effectiveness of the proposed algorithm and antenna design using the Butler matrix is evaluated for various performance metrics separately. The antenna is designed using Rogers RT/duroid 5880(tm) substrate, and the fabricated prototype is studied. The designed antenna attains high radiation efficiency, and it ranges between 97 and 98 % under both the measures and simulated scenarios under the operating frequency range of 28 GHz to 39 GHz.

Joint parameter estimation algorithm for polarization-sensitive channel compression arrays based on atomic norm minimization

To reduce the complexity of direction-of-arrival (DOA) algorithms in polarization-sensitive arrays and maintain high estimation accuracy while decreasing runtime, this paper proposes a channel compression model based on orthogonal dipole arrays. In addition, a DOA estimation algorithm using atomic norm minimization (ANM) is introduced for this model. This algorithm performs eigenvalue decomposition on the covariance matrix of the data received from the polarization-sensitive channel compressed array. A new observation vector is constructed for the ANM problem under the channel compression model using the obtained eigenvalues and eigenvectors. Subsequently, a Toeplitz matrix is constructed from the optimal solution of the semi-positive definite programming (SDP) problem. The Vandermonde decomposition of the Toeplitz matrix provides estimates of the signal DOA parameters. By combining the vectorization result of the covariance matrix and the least-squares calculation, the polarization auxiliary angle and polarization phase angle information of the incident source are also obtained. Simulation experiments compare the root-mean-squared error (RMSE) of each algorithm at different angular intervals and signal-to-noise ratios (SNR) to confirm the validity of estimating DOA and polarization parameters.

A novel hybrid algorithm for two-dimensional direction-of-arrival estimation in massive MIMO systems

Massive Multiple-Input Multiple-Output (mMIMO) systems have been significantly exploited to enhance mobile network capacity. Increasing the number of antennas provided higher direction-of-arrival (DOA) estimation accuracy, especially when utilizing a two-dimensional multi-signal classification algorithm (2D-MUSIC). However, this algorithm suffers from the heavy computational complexity imposed by the 2D angle search. This paper proposes a novel hybrid algorithm called the 2D-MM, which combines the strengths of the 2D-MUSIC algorithm with the 2D-Minimum Variance Distortionless Response (2D-MVDR) algorithm to reduce the computational complexity using a fast new DOAs search strategy. To be more precise, the new search strategy involves two stages: the 2D-MVDR stage detects the approximate DOAs within a whole angular field of view using as few antennas as possible and snapshots of received data. Then, the 2D-MUSIC stage accurately determines the number and locations (azimuths and elevations) of those sources within the angular sectors in which they appear. Furthermore, the choice of utilizing a uniform circular array (UCA) configuration for representing mMIMO systems is enhanced by introducing a new geometric structure featuring two concentric rings. The simulation results indicate that confining the spectral peak searching process into octant areas with an angular sector spanning π⁄4 instead of the total angular field of view (0–2π) would avoid searching empty areas and then reduce the computational complexity, where the improvement percentage in the proposed algorithm's running time was approaching 80 % when utilized with mMIMO arrays. In contrast, the proposed algorithm proves effective for mMIMO systems, irrespective of the increased number of antennas utilized.

Joint velocity and angle estimation for single-cycle TDM MIMO automotive radar

Time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radar systems have gained popularity in automotive applications, thanks to their high-resolution and cost-effective properties. However, when the relative motion between the target and the radar is significant and possibly fast time-varying, TDM MIMO systems suffer for reduced maximum unambiguous velocity and angle-Doppler coupling. In this work, a novel off-grid estimation method for TDM MIMO systems is developed that operates on a single cycle and is able to accurately estimate unambiguously the instantaneous velocity and the target direction of arrival (DOA). We model the angle-Doppler coupling through a space-time steering vector. Then, an iterative adaptive approach (IAA)-based method is exploited to reconstruct the 2D velocity-angle spectrum. Additionally, we account for off-grid errors when the targets do not lie on the search grids. A coarse-to-fine strategy is proposed to alleviate the computational burden, where a maximum likelihood (ML) estimator iteratively corrects the off-grid velocity and DOA estimates of each target. Simulated and experimental data are processed to demonstrate the effectiveness of the proposed method.

Research on direction of arrival estimation for mixed broadband signals

In practical applications, the signals received by a hydrophone array include coherent and incoherent signals, while estimating the direction of arrival (DOA) of coherent or incoherent signals has been studied for simplicity with poor performance for a mixed signal. The present study proposes a novel approach for estimating the DOA of mixed broadband signals to solve this problem, namely the oblique method. The unitary oblique method is simultaneously proposed to improve operational efficiency with the complex matrix shifted into a real valued matrix through a unitary transform. For independent signals, the incoherent signal subspace method (ISSM) is used to estimate the DOA. Matrix difference and oblique projection methods are adopted for multi-group coherent signals in order to eliminate the influence of incoherent signals and other groups of coherent signals. For broadband signals, the Tasseled Cap Transformation focusing transform is utilized to convert the broadband signal to the focusing frequency, and then the high-resolution Multiple Signal Classification algorithm method is employed to solve the problem. Simulation results indicate that the oblique method reconciles DOA estimation performance and computational efficiency and can be extended to other types of arrays.