High-resolution Direction Research Articles

A near-field SIMO millimeter-wave imaging system based on RMA

Abstract In the imaging of human security checks, high-resolution millimeter wave imaging systems are required. The millimeter wave imaging system has a large amount of data and requires a long time. So, in this design, in terms of data acquisition, the system uses the SIMO method, using one transmitting antenna and four receiving antennas to maintain imaging resolution while reducing data acquisition time by four times. In this system, MATLAB sends instructions to the rail control board through the serial port to control the movement trajectory of the radar. MATLAB implements collaborative radar acquisition through Lua scripts. The system scans 50 lines of data and finally obtains a raw data size of 400x200x256. Then, the RMA algorithm is used to calculate the imaging scene image. The imaging scheme of this system is simple to implement and has good imaging effects. It has great potential in millimeter wave high-resolution imaging direction and millimeter wave security inspection.

A high-resolution method for direction of arrival estimation based on an improved self-attention module.

The high-resolution direction of arrival (DOA) estimation is a prominent research issue in underwater acoustics. The existing high-resolution methods include subspace methods and sparse representation methods. However, the performance of subspace methods suffers from low signal-to-noise ratio (SNR) and limited snapshots conditions, and the computational complexity of sparse representation methods is too high. The neural network methods are emerging high-resolution methods. However, insufficient support for big data is frequently observed in underwater acoustics, and conventional network structures present challenges in further enhancing performance. To address the aforementioned problems, we propose a neural network method based on an improved self-attention module to achieve high accuracy and robust DOA estimation. First, we design a multi-head self-attention module with large-scale convolutional kernels and residual structures to improve the estimated accuracy. Second, we propose an improved input feature to enhance the robustness to non-uniform noise and unequal-intensity targets. The simulations demonstrate that the proposed method exhibits superior angle resolution compared to sparse representation methods under the same simulation conditions. The proposed method demonstrates exceptional accuracy and robustness in DOA estimation under challenging conditions of low SNR, limited snapshots, and unequal-intensity targets. The experimental results further prove the effectiveness of the proposed method.

Performance characterization of a wireless sensors network system (WSNS) for measurements of hurricane wind effects on structures

This paper presents a new wireless sensors network system (WSNS) designed for high-resolution absolute pressure measurements, wind speed, and direction. The system is tailored to assess the impact of hurricane winds on residential structures, both in laboratory settings and field environments. Importantly WSNS stands out for its unique ability to provide waterproof, surface-mounted external pressure measurements. The system's performance is evaluated during deployment on a full-scale house model at the Wall of Wind facility. The WSNS sensors were installed on different surfaces of a single-story residential building model. The sensor locations mirrored the locations of surface pressure taps connected to a Scanivalve differential pressure measurement system. Due to the size and shape of the WSNS pressure module, a casing effect was observed, which may result in pressure offsets under certain wind speeds and directions, depending on the sensor's location and the conditions, including dry and light rain (50 mm per hour). The comparison between WSNS and Scanivalve indicates that the sensor's casing geometry does not cause significant differences in the time-averaged measurements for low-turbulence regions. Conversely, this is not true for high-turbulence regions, which should be marked for future deployments using smaller surface-mounted pressure taps.

Retracted: High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

Retracted: High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Traditional direction of arrival (DOA) estimation algorithms typically have poor spatial resolution and robustness. In this paper, we propose a broadband high-resolution DOA estimation method based on the generalized weighted Radon transform (GWRT). The array signal can be converted into the frequency-wavenumber (f-k) domain using the conditional wavenumber spectrum function (CWSF). Then, a linear integral mathematical model for high-resolution DOA estimation is derived by transforming the f-k domain into the azimuth-energy domain using the GWRT. Computer simulation and sea trials were conducted to validate the feasibility and performance of the proposed method. The results obtained indicate that the proposed method yields a lower sidelobe level and can more effectively suppress the output energy in the non-target direction when compared to the conventional beamforming (CBF), steered minimum variance (STMV), and deconvolution (DCV) methods. Further, the proposed method provides improved spatial resolution and robustness in a multi-target environment.

Directional characteristics of two gamelan gongs.

The distinctive geometry and structural characteristics of Balinese gamelan gongs lead to the instrument's unique sound and musical style. This work presents high-resolution directivity measurements of two types of gamelan gongs to quantify and better understand their acoustic behavior. The measured instruments' structural modes clearly impact their far-field directivity patterns, with the number of directional lobes corresponding to the associated structural mode shapes. Many of the lowest modes produce dipole-like radiation, with the dipole moment determined by the positions of the nodal and antinodal regions. Higher modes exhibit more complex patterns with multiple lobes often correlated with the location and number of antinodal regions on the gong's edge. Directivity indices correspond to dipole radiation at low frequencies and quadrupole radiation at intermediate and higher frequencies. Symmetry analysis confirms that the gong's rim significantly impacts the resultant directivity.

Acoustic Sensing Performance Investigation Based on Grooves Etched in the Ring Resonators

Acoustic detection based on optical technology has moved in the direction of high sensitivity and resolution. In this study, an optical waveguide acoustic sensor based on a ring resonator with the evanescent field is proposed. Grooves are introduced into the ring resonators as a direct sensitive structure to excite the evanescent field. A series of resonators with diverse grooves are fabricated for a comparative analysis of acoustic performance. The acoustic parameters of bandwidth, sensitivity, and signal-to-noise ratio (SNR) vary with different grooves indicated by the Q-factor. The results show that the ring resonators with variable-sized grooves exhibit excellent capability of acoustics detection. A maximum frequency of 160 kHz and a high sensitivity of 60.075 mV/Pa is achieved, with the minimum detectable sound pressure being 131.34 µPa/Hz1/2. Furthermore, the resonators with high Q-factors represent a remarkable sound resolution reaching 0.2 Hz. This work is of great significance for optimizing acoustic sensors and broadening the application range.

Fast Sparse Non-Negative Least Squares via ADMM for High Resolution DOA Estimation

High-resolution and low-complexity direction of arrival (DOA) estimation deserves a warm welcome in a real radar sensor. However, achieving this goal is nontrivial. For the DOA estimation of the uniform linear array (ULA), we offer an efficient approach to reach a good compromise between complexity, resolution and accuracy. Under the covariance fitting framework, we construct a sparse non-negative least squares (NNLS) that is solved by the alternating direction method of multipliers (ADMM) at a low-computational complexity. The regularization item involved in the ADMM procedure yields an inherent improvement on robustness. Moreover, a large-scale matrix inversion that is a computationally expensive operation in the ADMM procedure is recursively computed by exploiting the diagonal matrix property of the regularization item, resulting in a reduction on complexity. In addition, the pre-estimation of the noise variance further reduces the scale of the sparse NNLS problem and alleviates the ill-conditioning effect to some extent. Compared to several sparse covariance fitting methods, both the simulation data and the 77 GHz radar-measured data demonstrate that the proposed algorithm has the good resolution, robustness, and accuracy performance with lower computationally cost.

Wind Characteristics and Potentials of Two-Parameter Weibull Distribution and Maximum Entropy-Based Distribution Functions at an Equatorial Location

Thorough knowledge of the wind characteristics and variations are of great importance in the development of wind energy resource in any location. This study examines the wind characteristics and assess the potential of two distribution functions in a low wind equatorial region of West Africa. High resolution wind speed and direction data were obtained from a site in Nsukka, a location chosen in the region of study. Diurnal, seasonal and annual variations of both the wind speed and directions were examined. The potentials of two-parameter Weibull distribution and another distribution function based on Maximum Entropy principle (MEP) were assessed using R2 and root mean squared error (RMSE). The results indicated that day-time is windier than night-time. The transitions months of February, March and April have the highest wind speed. The dry season has greater energy potential than rainy season. The predominant wind direction lay within the sectors: South-South-West and East. The predominant wind sector for February, March and April is South-East. The R2for daily, sub-seasonal day-time and night-time, monthly, and annual ranged between 0.90 and 0.99 for both MEP-based and Weibull distributions. The daily, sub-seasonal day-time and night-time, monthly, and annual RMSE also ranged between 0.011 to 0.075 for MEP-based and Weibull distribution respectively. Thus, both MEP-based and Weibull two-parameter distribution functions can be used to model wind data at the location of study.

Hospitalization due to fire-induced pollution in the Brazilian Amazon: A causal inference analysis with an assessment of policy trade-offs

Developing countries by relying on agricultural fires trade respiratory health for cheaper land preparation and greater food security of subsistence farmers. In the Brazilian Amazon, thousands of kilometres of vegetation are annually burned, releasing pollutants that impact the health of 24 million inhabitants. The paper seeks to fill two crucial informational gaps for policy planning, namely, the size of the impact on the most pollution-susceptible groups, i.e., children and the elderly, and the priority locations for health-oriented intervention on fires. A municipal-monthly panel covering ten years of the Amazonian territory was analysed by relying on exogenous and high-resolution wind direction variation to identify the effect of fires on pollution and hospitalizations. This is an unparalleled effort for applying to a large geographical area spanning five million km2 a highly refined identification strategy relying on hourly gridded wind and fire data. As the result, one extra standard deviation of “upwind” fires was estimated to increase asthma-related hospitalization of the elderly in 0.03 days/month, that is, 4 % of the bed-days commonly demanded by such kind of hospitalization, an effect whose size decayed with the distance between fires and hospitals. A policy assessment uncovered the trade-off between respiratory health of the elderly and nutritional health of fire-dependent subsistence farmers, presenting a priority map for tackling the issue with municipal-level interventions. The targeting of non-subsistence fires is advised, what could avoid 28 days of hospitalization per year. It is thus demonstrated that the trade-offs inherent to agricultural fires could be better balanced by evidence-based targeting of fire prevention policy.

Novel Design of Grey Wolf Optimization Heuristics for High Resolution Direction of Arrival Estimation in Acoustic Plane Waves

Novel Design of Grey Wolf Optimization Heuristics for High Resolution Direction of Arrival Estimation in Acoustic Plane Waves

Directivity of the muted trumpet

The directivity function of a played musical instrument describes the angular dependence of its acoustic radiation and diffraction about the instrument, musician, and musician’s chair. In this study, high angular resolution directivity data were acquired in an anechoic chamber of a muted trumpet being played by a seated musician. The chair height and horizontal displacement ensured that the geometric center of the instrument’s radiating region fell at the circular center of a computer-controlled semi-circular array of 36 microphones positioned at Δθ = 5° polar-angle increments. Azimuthal rotations progressed in Δφ = 5° increments, such that the measurements involved 2,521 unique positions over a sphere. Additional measurements at a position within the rotating reference frame facilitated post-processing. The musician played chromatic scales at each rotation position, and this process was repeated for straight, cup, and wow wow mutes in order to draw comparisons in the directivity patterns of each mute to the unmuted trumpet. Radiation behind the musician increased as a result of the mute, and mute-dependent changes to the directivity patterns primarily occurred above 1 kHz.

Direction of Arrival Estimation of Acoustic Sources with Unmanned Underwater Vehicle Swarm via Matrix Completion

With the rapid development of vibration and noise reduction technologies, underwater target detection is facing great challenges. Particularly, the task of high-resolution direction of arrival (DOA) estimation with sonar array is becoming more and more tough. In recent years, unmanned underwater vehicles (UUVs) have been developed considerably, with the improvements of target localization performance in terms of adaptability, detection range, operation efficiency, and anti-interference ability. Nevertheless, in general, the size of UUV is small such that current passive sonar systems usually have relatively limited localization accuracy, detection distance, and environmental robustness in complex ocean noise. This motivates us to present a new approach to construct a large-aperture virtual array with multiple small-aperture arrays of unmanned underwater vehicle swarm (UUVS) which consists of multiple UUVs in this paper. However, for the UUVS array, the received data could suffer from unobserved and corrupted samples. This makes it challenging to analyze and process large-aperture array data. Towards this end, the matrix completion technique is employed to recover the unobserved and corrupted data for virtual array construction based on the low rank property of array data matrix. The recovered matrix is then exploited for underwater target bearing estimation using the traditional DOA estimation approach. Numerical results verify that the proposed method is capable of detecting underwater targets with high precision and resolution.

High Resolution Detection, Estimation and Location Using GTF DoA Method for Smart Antenna System

Accommodating multiple users within the limited available bandwidth and providing the same Quality of Service (QoS) to all the users is a challenging task. Channel capacity can be increased by using the Spatial Division Multiple Access (SDMA) technique. Smart Antenna Systems are used to implement the SDMA technique in Real-Time and it also helps in finding the high-resolution Direction of Arrival (DoA) detection of the desired mobile users. In this paper novel Gaussian Triangular Factor (GTF) method is proposed for the detection of the desired mobile users from the 3-D spatial domain. This method is based on vector subspaces, which perform the triangular decomposition of the entire Eigenspace into the Lower Element Factor (LEF) and Upper Element Factor (UEF). These are then supplied for the computation of the power spectrum where peaks represent the detected locations of the desired mobile users in the 3-D spatial field. The proposed method was able to detect all the desired users, which were spaced nearer or far apart spatially, it provided high-quality detection regardless of the number of antenna elements used at the Base Station (BS). The GTF Method was able to detect all the desired users under the presence of heavy noise, fading, and interference. It was able to suppress the side lobes, back lobes, and grating lobes thus immensely improving the detection quality, detection range, system power consumption, detection efficiency, and effectiveness. The proposed GTF method was compared with several existing methods and it provided the best results for different performance parameters like Detection Error, Resolution, Time Complexity, and Disturbance Error.

A low‐computational‐cost alternating switches algorithm for real‐valued spatial covariance matrix reconstruction

The application of the emerging hybrid analog–digital architecture for future millimeter-wave communications has attracted significant attention. Although this architecture can reduce the hardware cost and power consumption considerably, the high-resolution direction of arrival (DOA) estimation in the architecture based on switches is still a challenge. As a solution, an alternating switches algorithm (ASA) is proposed in this paper. By appropriately adjusting the switch connected to each antenna, the real-valued spatial covariance matrix (SCM) is reconstructed with low-dimensional vector-to-vector multiplication. Subsequently, the DOA angle of each signal is estimated based on a split subspace extracted from the real-valued SCM. Finally, a set of simulation experiments are performed to verify the performance of the proposed algorithm. The results indicate that the ASA can reconstruct the real-valued SCM and realize high-resolution DOA estimation with a substantially low computational cost.

Measured high-resolution directivities of guitar amplifiers

Guitar amplifiers comprise specialized powered loudspeakers with distinct configurations and directivity characteristics. Recently, the authors measured the directivities of six guitar amplifiers using a new measurement system. The system employs a rotating semicircular microphone array in an anechoic chamber, with 2522 unique microphone positions over a surrounding sphere (i.e., 5-deg resolution in both the polar and azimuthal angles). This presentation will describe the measurement and processing procedures and compare the measured results to those produced by theoretical directivity models. An online archival database provides easy access to the guitar amplifier data and other directivity data from the human voice, played musical instruments, and other sound sources. The data are available in several commonly used file formats and should provide valuable information for many applications, including spatial audio, architectural acoustics modeling, and microphone placement.

High-Resolution and Low-Complexity Direction of Arrival Estimation for Hybrid Array of Subarrays

In this paper, the problem of direction-of-arrival (DoA) estimation for a hybrid array of subarrays is considered in millimeter-wave communication systems. Unlike much prior work using the outputs from the analog subarrays with symbol-wise processing, the proposed framework jointly exploits multiple outputs in time domain for high-resolution DoA estimation. With block-wise processing, the proposed method designs an optimal hybrid combiner maximizing beamforming gain, while reducing a receiver complexity based on fast Fourier transform filtering for each blocked signal. The performance of the proposed method is analyzed, including estimation performance with a limited training signal, training overhead, and computational complexity. An extension of the proposed method to a uniform planar array case is also discussed. Numerical results show the effectiveness of the proposed algorithm.

2D-DOD and 2D-DOA Estimation Using Sparse L-Shaped EMVS-MIMO Radar

It is well-known that EMVS-MIMO radar is an emerging technique that it allows for 2D-DOD and 2D-DOA estimation. Unfortunately, existing array geometries on the EMVS-MIMO radar can rarely reach a good compromise between the estimation accuracy and the computational burden. This paper is aimed at proposing an L-shaped sparse array topology for a bistatic EMVS-MIMO radar, whose inter-element distance is much larger than half-wavelength. A fast algorithm proposed herein to estimate the 2D-DOD and 2D-DOA. Firstly, the direction cosine estimates are obtained via the rotational invariance properties of the sparse subarrays, which are ambiguous yet exhibit high-resolution. Thereafter, the direction cosine estimates are achieved via the vector cross-product of the normalized Poynting vectors, which are unambiguous but have low-resolution. The unambiguous high-resolution direction cosine estimates are determined by combining the previous results, following which the 2D-DOD and 2D-DOA can be easily recovered. It is shown that the proposed framework can obtain a better accuracy of estimation than the other existing methods. Moreover, it is more flexible than the current sparse array methodologies. Finally, the theoretical derivations have been validated by simulation results.

A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the desired spatial resolution, limited by the total antenna array aperture. This work gives an overview of the recent progress and work in the field of deep learning based direction of arrival estimation in the automotive radar context, i.e. using only a single measurement snapshot. Additionally, several deep learning models are compared and investigated with respect to their suitability for automotive angle estimation. The models are trained with model- and data-based approaches for data generation, including simulated scenarios as well as real measurement data from more than 400 automotive radar sensors. Finally, their performance is compared to several baseline angle estimation algorithms like the maximum-likelihood estimator. All results are discussed with respect to the estimation error, the resolution of closely spaced targets and the total estimation accuracy. The overall results demonstrate the viability and advantages of the proposed data generation methods, as well as super-resolution capabilities of several architectures.

A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models

Abstract. Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream–downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives representative sub-grid river length and slope parameters, which are required for resolution-independent model results. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec (∼ 1 km), 5 arcmin (∼ 10 km) and 15 arcmin (∼ 30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often-applied upscaling methods. Furthermore, we show that MERIT Hydro IHU minimizes the errors made in the timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is open source and fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.