Uniform Linear Array Research Articles

Enhanced Coprime Array Structure and DOA Estimation Algorithm for Coherent Sources.

This paper presents a new enhanced coprime array for direction of arrival (DOA) estimation. Coprime arrays are capable of estimating the DOA using coprime properties and outperforming uniform linear arrays. However, the associated algorithms are not directly applicable for estimating the DOA of coherent sources. To overcome this limitation, we propose an enhanced coprime array in this paper. By increasing the number of array sensors in the coprime array, it is feasible to enlarge the aperture of the array and these additional array sensors can be utilized to achieve spatial smoothing, thus enabling estimation of the DOA for coherent sources. Additionally, applying the spatial smoothing technique to the signal subspace, instead of the conventional spatial smoothing method, can further improve the ability to reduce noise interference and enhance the overall estimation result. Finally, DOA estimation is accomplished using the MUSIC algorithm. The simulation results demonstrate improved performance compared to traditional algorithms, confirming its feasibility.

Robust Sparse Recovery Based Vehicles Location Estimation in Intelligent Transportation System

An architecture for vehicle position estimation is introduced in this paper to tackle the issue of vehicles positioning in traffic congestion of intelligent transportation system (ITS). The introduced architecture is related to three unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), ITS center and the terminal of position estimation, in which the UAV collect data from the ULA, the ITS center store data and the terminal is responsible for executing the corresponding direction of arrival (DOA) estimation algorithm to estimate the vehicle position. In the introduced architecture, DOA estimation is the crucial issue, hence a robust sparse recovery framework based on the optimal weighted subspace fitting is put forward for DOA estimation in the presence of direction-dependent unknown mutual coupling. Firstly, a data model can be obtained by a new transformation approach. Then, a sparse recovery algorithm based on the weighted subspace fitting is used to estimate the desired DOAs. The proposed DOA estimation algorithm can achieve superior performance in both coherent and incoherent signals in the environment of direction-dependent mutual coupling, and this environment often occurs in traffic congestion. Based on the DOA estimation results obtained from the proposed efficient regularized sparse recovery algorithm, the position of vehicles is final estimated by a weighted three-points cross-positioning method. The results of various simulation experiments fully demonstrate the robustness and superiority of the proposed architecture and algorithm.

BER comparisons of MIMO radar with uniform linear arrays

A smart antenna, which consists of several antenna elements makes a gain at the desired direction, makes nulls at the direction of interferers. In this article, the bit error rate is measured in different directions and distances, and the method of the minimum variance is used with the concept of the link budget when the weights of the multiple input multiple output (MIMO) radar are calculated with the number of N receiver elements and with the number of M transmitter elements. The less the number of interferers is, or using MIMO, the better the system is.

Coupling-Informed Data-Driven Scheme for Joint Angle and Frequency Estimation in Uniform Linear Array with Mutual Coupling Present

Coupling-Informed Data-Driven Scheme for Joint Angle and Frequency Estimation in Uniform Linear Array with Mutual Coupling Present

Corrigendum to “BER comparisons of MIMO radar with uniform linear arrays”

Corrigendum to “BER comparisons of MIMO radar with uniform linear arrays”

AN X/Ku DUAL-BAND SHARED APERTURE UNIFORM LINEAR ANTENNA ARRAY FOR AIRBORNE SYNTHETIC APERTURE RADAR APPLICATIONS

An amplitude distribution of shared aperture antenna array for airborne SAR applications using a dual-band (X/Ku-band) synthesis is presented in this research. It is utilizing a linear arrangement with 1 × 8 antenna elements and a series feeding mechanism to operate in the X/Ku bands. The proposed shared aperture antenna array has eight elements providing a uniform array in the X and Ku bands. To realize the proposed scanning array, the X/Ku-band antenna array takes an inter-element spacing of 0.7 λ, which provides a scanning angle of ± 25°. When the space between adjacent antenna arrays is effectively used to reduce the influence of mutual coupling, the synthesis array's X-band and Ku-band antenna arrays can perform well in isolation. The uniform linear array exhibits Side Lobe Level (SLL) of -23.9 dB in X-Band and -24.7 dB in Ku-Band, with maximum realized gains of 14.2 dBi in X-Band and 15.7 dBi in Ku-Band. The isolation between bands and ports exceeds 25 dB. The uniform amplitude distribution shared aperture antenna (UAD SAA) has accepted an SLL performance of -13.5 dB. Additionally, it displays a uniform (X/Ku-band 200/144 MHz) impendence bandwidth that is relatively wide.

Molecular Beamforming for Actuation in Molecular Communication Networks.

The actuation accuracy of sensing tasks performed by molecular communication (MC) schemes is a very important metric. Reducing the effect of sensors fallibility can be achieved by improvements and advancements in the sensor and communication networks design. Inspired by the technique of beamforming used extensively in radio frequency communication systems, a novel molecular beamforming design is proposed in this paper. This design can find application in tasks related to actuation of nano machines in MC networks. The main idea behind the proposed scheme is that the utilization of more sensing nano machines in a network can increase the overall accuracy of that network. In other words, the probability of an actuation error reduces as the number of sensors that collectively take the actuation decision increases. In order to achieve this, several design procedures are proposed. Three different scenarios for the observation of the actuation error are investigated. For each case, the analytical background is provided and compared with the results obtained by computer simulations. The improvement in the actuation accuracy by means of molecular beamforming is verified for a uniform linear array as well as for a random topology.

Underdetermined DOA Estimation for Wide-Band Stationary Sources in Unknown Noise Environment

Underdetermined direction-of-arrival estimation for wide-band stationary signals is addressed in this correspondence. By properly employing the diversities in frequency domain, the proposed method is able to significantly increase the degrees of freedom of a uniform linear array, which enables us to handle more sources than sensors. Meanwhile, as the unknown noise can be efficiently suppressed in the proposed estimator, the accuracy and robustness improves considerably. Numerical results are presented for illustrating the effectiveness of the proposed method.

Multi-target Separation Algorithm Based on DFT Transform in Uniform Linear Array Scene

Multi-target Separation Algorithm Based on DFT Transform in Uniform Linear Array Scene

Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network.

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes' aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

Low-Complexity Variational Bayesian Inference Based Groupwise Detection for Massive MIMO Uplinks

This paper proposes a low-complexity groupwise detection scheme for massive multiple-input multiple-output (MIMO) systems based on two key features of massive MIMO channels. First, most inter-user correlations tend to be negligible as the number of antennas <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> at the base station (BS) increases, while some users might have a high correlation that is indistinguishable for linear projection-based detectors. We hence develop a variational Bayesian inference-based groupwise (VBI-G) detector based on a factorization approximation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability of transmitted symbols, which sequentially performs nonlinear detection for small groups of highly correlated users without computations of large-scale matrices. Second, the variation of normalized inter-user correlation is insignificant over a wide range of frequency, thereby user grouping based on correlation coefficients can be performed on a wideband basis. Accordingly, we propose a low-complexity incremental greedy grouping algorithm for minimizing the Kullback-Leibler divergence of the unconditioned posterior probabilities with the constraint of a maximum group size. Theoretical analysis proves that for a BS equipped with a uniform linear array, the maximum group size grows logarithmically with the number of users <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> with high probability in the regime <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M,K</i> → ∞ with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K/M</i> < 1/2 under the worst line-of-sight propagation condition. Simulation results verify that the proposed VBI-G detector achieves superior performance with an extremely low computational overhead for massive MIMO systems.

Data-Driven Scheme for Joint Estimation of Direction-of-Arrival and Frequency With Uniform Linear Array

Data-Driven Scheme for Joint Estimation of Direction-of-Arrival and Frequency With Uniform Linear Array

A Modified δ-Generalized Labeled Multi-Bernoulli Filtering for Multi-Source DOA Tracking With Coprime Array

For the target tracking problem where the number of targets fluctuates with time and the measurement is a point measurement, the random finite set (RFS) class filtering is an available solution. However, in direction of arrival (DOA) tracking, the array observation is a super-positional value, and the tracking performance can be severely impaired if the RFS-based filter approach is applied. As a result, a novel measurement association mapping (NMAP) approach has been presented to cope with the mapping problem between the array observations and sources. Nevertheless, the tracking performance is poor when the number of particles is small. In this paper, a modified delta-Generalized Labeled Multi-Bernoulli (δ-GLMB) DOA tracking particle filter is proposed in combination with the NMAP strategy, which can achieve the same tracking performance with a smaller number of particles by modifying the particles in the δ-GLMB prediction step. Furthermore, the approach is extended to a coprime array and can achieve better DOA tracking performance than a uniform linear array. Simulation experiments validate the effectiveness of the proposed algorithm.

Comments on “Mixed Near-Field and Far-Field Sources Localization Using the Uniform Linear Sensor Array”

Comments on “Mixed Near-Field and Far-Field Sources Localization Using the Uniform Linear Sensor Array”

Second-order optimal subspace estimation for ESPRIT-like DOA estimation

Signal processing via subspace-based methods require subspace estimates taken from either the eigenvectors of the sample covariance matrix or the principal singular vectors of the received data matrix. The subspaces spanned by the singular vectors or eigenvectors are perturbed away from ground truth due to the additive noise in the received data. The perturbation reduces the accuracy of algorithms that make use of these estimates. A statistically optimal estimate of the unperturbed subspace in terms of the perturbed signal and orthogonal subspaces has been derived and is accurate up to the first-order terms in the additive noise matrix (Vaccaro, 2019). Here, the second-order optimal approximation for the unperturbed subspace is derived and applied to a uniform linear array (ULA) processing model for both full and sparse geometries. The approximation provides little improvement for a full ULA highlighting the importance of first-order terms for a fully sampled geometry. However, for a sparse array, the source estimation performance is much more clearly improved for a low signal-to-noise ratio (SNR) environment with few temporal samples.

Application of compressive sensing for fast solving multi-snapshot signal model in uniform linear arrays

Application of compressive sensing for fast solving multi-snapshot signal model in uniform linear arrays

A Sparse-Array Design Method Using Q Uniform Linear Arrays for Direction-of-Arrival Estimation.

Nowadays, sparse arrays have been a hotspot for research in the direction of arrival (DOA). In order to achieve a big value for degrees of freedom (DOFs) using spatial smoothing methods, researchers try to use multiple uniform linear arrays (ULAs) to construct sparse arrays. But, with the number of subarrays increasing, the complexity also increases. Hence, in this paper, a design method, named as the cross-coarray consecutive-connected (4C) criterion, and the sparse array using Q ULAs (SA-UQ) are proposed. We first analyze the virtual sensor distribution of SA-U2 and extend the conclusions to SA-UQ, which is the 4C criterion. Then, we give an algorithm to solve the displacement between subarrays under the given Q ULAs. At last, we consider a special case, SA-U3. Through the analysis of DOFs, SA-UQ can find underdetermined signals. Moreover, SA-U3 can obtain DOFs close to other sparse arrays using three ULAs. The simulation experiments prove the performance of SA-UQ.

Photonic beamforming using a quantum-dash optical frequency comb source.

We demonstrate photonic beamforming using a quantum-dash (QD) optical frequency comb (OFC) source. Thanks to the 25 GHz free spectral range (FSR) and up to 40 comb lines available from the QD OFC, we can implement phased antenna arrays (PAAs) with directional radiation and scanning. We consider two types of PAAs: a uniform linear array (ULA) and a uniform planar array (UPA). By selecting different comb lines with a programmable optical filter, we can tune the FSR of the OFC source and realize a discrete scanning function. We evaluate the beam squint of the ULAs, and the results show that we can achieve broadband operation. Finally, we show that we can achieve both directional radiation and scanning simultaneously using the UPA.

UNet-rootMUSIC: A high accuracy direction of arrival estimation method under array imperfection

In the practical direction-finding systems, the accuracy and resolution of the direction of arrival (DOA) estimation are affected not only by the Gaussian noise and array size but also by hardware configuration imperfections, such as errors in element manufacturing and mounting. These impairments cause phase and amplitude errors in estimating the DOA of signal sources. To address this issue, this paper proposes to combine a U-shape deep neural network (UNet) with the multiple signal classification via the root of the polynomial (rootMUSIC) algorithm (so-called UNet-rootMUSIC) to improve the DOA estimation accuracy. In this approach, the UNet model plays a role in converting a covariance matrix of received signals containing phase and gain errors into a nearly perfect one of the ideal antenna array. The rootMUSIC algorithm is then employed to estimate the signal DOA based on the converted covariance matrix. The DOA estimation performance of the uniform linear array of eight elements with an inter-element distance of λ/2 is analyzed through experimental simulations. The simulation results demonstrate that our method can significantly reduce the root mean square error of DOA estimation compared to the conventional MUSIC, rootMUSIC, ESPRIT methods and two deep neural network-based angular classification methods.

Gridless sparse recovery STAP algorithm with array amplitude-phase errors for non-uniform linear array

In the continuous angle-Doppler profile, the gridless sparse recovery (SR) space-time adaptive processing (STAP) prevents the off-grid issue of conventional SR-STAP. However, the applicability of the technique is limited to uniform linear array (ULA), whose performance deteriorates significantly owing to the array amplitude-phase errors (AAPEs). To this end, this study proposes a novel gridless SR-STAP algorithm applicable to a non-ULA with AAPEs. First, considering the properties of clutter covariance matrix (CCM), the atomic norm minimization (ANM)-STAP model with AAPEs is established for non-ULA. Second, the ANM-STAP is derived as a joint optimization problem for the clutter subspace and AAPEs, which are iteratively updated using the alternating direction method of multipliers. Finally, CCM is reconstructed to design the STAP filter weight vector. Simulation results show that the proposed algorithm is advantageous for non-ULAs and improves the STAP performance in the presence of AAPEs.