Article
Engineering, Electrical & Electronic
D. Veerendra, Babur Jalal
Summary: This article presents a DOA estimation method using the fast variable step size least mean square (FVSSLMS) method. By modifying the structure of the uniform linear array (ULA), the proposed method provides uniform estimation performance for all DOAs with reduced computation complexity.
IEEE SENSORS JOURNAL
(2022)
Article
Chemistry, Analytical
Murdifi Muhammad, Minghui Li, Qammer Abbasi, Cindy Goh, Muhammad Ali Imran
Summary: This paper investigates an accurate and high-resolution technique for direction of arrival (DOA) estimation using a single snapshot of data. By manipulating signal covariance matrix and suppressing unwanted noise, the proposed technique outperforms existing methods in various signal-to-noise ratio scenarios. It also achieves the best DOA estimation results in single snapshot and single-source scenarios.
Article
Computer Science, Information Systems
Youngwoo Youn, Jeongphill Kim, Sangyeol Oh, Sang-Hwa Yi
Summary: A new approach for estimating the direction-of-arrival in a Time-Modulated Array system with a bipolar squared periodic time-modulating sequence is proposed, which enhances the power of harmonic components in the received signal. A new parameter related to the ratio of upper and lower sideband first harmonic components is formulated to improve DOA estimation accuracy in low SNR environments, with numerical analysis verifying its effectiveness.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Computer Science, Information Systems
Parth Mehta, Kumar Appaiah, Rajbabu Velmurugan
Summary: A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed, which improves the performance parameters of a beamformer. The proposed method outperforms the previous feedback beamforming approach and the conventional beamformer. By incorporating a retransmission based minimum variance distortionless response (MVDR) beamformer, the proposed method achieves lower estimation error compared to prior methods such as MUSIC, ESPRIT, robust MVDR, nested-array MVDR, and reduced-dimension MVDR methods, especially at low SNR levels.
Article
Engineering, Electrical & Electronic
Pengyu Wang, Huichao Yang, Zhongfu Ye
Summary: An improved complex-valued binary iterative hard thresholding (iCBIHT) algorithm is proposed in this research for direction-of-arrival (DOA) estimation using 1-bit quantized observation of sensor arrays. The algorithm defines an error function for signal reconstruction, utilizes gradient descending for signal estimation, and incorporates a bases-updating strategy and backtracking strategy to improve the accuracy of DOA estimation with few snapshots and low signal-to-noise ratio (SNR).
DIGITAL SIGNAL PROCESSING
(2022)
Article
Acoustics
Ishan D. Khurjekar, Peter Gerstoft
Summary: Uncertainty quantification (UQ) of deep learning (DL)-based acoustic estimation methods is crucial for real-world applicability, and conformal prediction (CP) provides statistically rigorous confidence intervals without distributional assumptions.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2023)
Article
Engineering, Electrical & Electronic
Wangjie Li, Xu Xu, Xinyue Huang, Yue Yang
Summary: In this paper, a novel algorithm is proposed for estimating the direction-of-arrival (DOA) of coherent signals using a moving coprime array (MCA). Firstly, a decoherent covariance matrix is constructed by using data vectors received at specific time instants, which has a similar structure to that of the covariance matrix for uncorrelated signals. Then, vectorization, array interpolation, and matrix completion technologies are used to reconstruct the data vector received by the augmented uniform linear array (ULA). Forward/backward spatial smoothing (FBSS) and root-MUSIC algorithms are applied for DOA estimation. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm when processing coherent signals.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Chemistry, Analytical
Yawei Tang, Weiming Deng, Jianfeng Li, Xiaofei Zhang
Summary: Due to their advantages in achieving higher DOA estimation accuracy and larger DOF using a fixed number of antennas, sparse arrays such as nested and coprime arrays have been widely studied in DOA estimation. However, the assumption of independence among signals, which is crucial for sparse arrays, is hard to guarantee in practice. To address this issue, we propose a method that combines the coherent signal subspace method (CSSM) with virtualization of the covariance matrix and enhanced spatial smoothing. The simulation results show that our proposed method outperforms other state-of-the-art algorithms in terms of RMSE and the proposed method is also validated with real data tests, demonstrating its effectiveness.
Article
Engineering, Electrical & Electronic
Jianxiong Li, Xingkai Shao, Jie Li, Lijun Ge
Summary: This paper introduces a direction of arrival (DOA) estimation method based on residual neural networks (ResNets), which is poorly adapted in the case of antenna array defects. The proposed method improves adaptability to defects and generalization ability to unknown scenes. The method divides received signals into corresponding subregions to relieve the generalization burden, and uses the output of a spatial classification network (SCN) as input for parallel ResNets. The results are combined to obtain the estimated direction of the signal.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Pranav Kumar Eranti, Buket D. Barkana
Summary: This paper reviews the DOA algorithms and evaluates the performance of MUSIC, ESPRIT, and EVD algorithms with and without using ADTFD as a preprocessing step. Simulation results show that ADTFD significantly improves the performance of the MUSIC algorithm, while it does not provide similar results for the ESPRIT and EVD methods.
Article
Physics, Applied
Qun Yan Zhou, Jun Wei Wu, Si Ran Wang, Zu Qi Fang, Li Jie Wu, Jun Chen Ke, Jun Yan Dai, Tie Jun Cui, Qiang Cheng
Summary: This study proposes a strategy for direction-of-arrival (DOA) estimation using time-domain-coding digital metasurface and a single receiver. The amplitude and phase distributions of the incident wave on the metasurface are precisely retrieved from the detected signals. Experimental results validate the effectiveness and accuracy of this strategy. The low cost and high flexibility of this approach will facilitate various wireless applications.
APPLIED PHYSICS LETTERS
(2022)
Article
Computer Science, Information Systems
Liyu Lin, Chaoran She, Yun Chen, Ziyu Guo, Xiaoyang Zeng
Summary: This paper proposes a two-branch neural network (TB-Net) for direction of arrival (DoA) estimation. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask indicating the presence of DoAs. The regression branch refines the DoA estimates by predicting the deviations from the grids. Simulation results demonstrate that this method achieves higher DoA estimation accuracy in the presence of model imperfections and has a size of 1.8 MB.
Article
Engineering, Electrical & Electronic
Trong-Dai Hoang, Xiaojing Huang, Peiyuan Qin
Summary: In this letter, a gradient descent-based spatial spectrum reconstruction (GD-SSR) algorithm is proposed for direction-of-arrival (DoA) estimation of a lens antenna array. The algorithm is inspired by the property of the sinc function and l(2)-norm optimization, and it achieves better DoA estimation performance with lower complexity compared to existing techniques, even in low-SNR regime. In addition, the proposed model does not require any pretraining process. The simulation results demonstrate that the scheme outperforms other techniques and resolves the angular ambiguity problem.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Georgios Konstantinos Papageorgiou, Mathini Sellathurai, Yonina C. Eldar
Summary: In this work, a Convolutional Neural Network (CNN) is introduced for direction-of-arrival (DoA) estimation in extreme noise environments, showing enhanced robustness and resilience to noise and a small number of snapshots. Experimental results demonstrate significant performance gains in low-SNR scenarios compared to state-of-the-art methods without requiring parameter tuning. The proposed solution also allows the network to learn the number of sources and predict DoAs with high confidence, making it highly desirable for challenging scenarios in various fields.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Engineering, Civil
Anup Das
Summary: Real-valued sparse representation methods are popular for processing directions of arrival (DOAs) of unknown and possibly correlated signals, converting the problem from the complex domain to the real domain. A fully automatic sparse Bayesian learning principle is proposed to estimate DOAs by simultaneously imposing sparsity constraints on both the real and imaginary parts of signals, providing a computational complexity reduction compared to conventional deterministic sparse signal processing methods.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2021)
Article
Audiology & Speech-Language Pathology
Sarah Gotowiec, Rebecca J. J. Bennett, Josefina Larsson, Melanie Ferguson
Summary: This study aimed to develop a self-report measure of empowerment on the hearing health journey. The initial phases involved item generation and content evaluation through a content expert panel survey and cognitive interviews. The result was a set of 33 quality-tested potential survey items that were highly rated for relevance, clarity, and fit to dimensions of empowerment.
INTERNATIONAL JOURNAL OF AUDIOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Gunjan Joshi, Ryo Natsuaki, Akira Hirose
Summary: It is important to evaluate the transferability of normalized indices to ensure accuracy and reliability when applying them to new sensors. This article proposes a method that uses all bands of multispectral optical sensors to generate multiple normalized indices and determine application-specific indices using inverse mapping. The results show that although some of the determined indices are not traditional, they are still useful for classification due to the differences between various land types. This method has the potential to automate normalized index determination and make the index development process more efficient.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Gunjan Joshi, Ryo Natsuaki, Akira Hirose
Summary: In the last decade, the increase in earth observation satellites has led to enhanced interest in data fusion techniques. This article proposes a neural network that combines and analyzes SAR and optical sensor data to provide high-resolution classification maps. It introduces a novel activation function called inverse mapping for feature analysis, which helps understand the prominent contributors for classification outputs. The fusion-based results show improved accuracy compared to independent sensors, and inverse mapping provides reasonable explanations.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Proceedings Paper
Geosciences, Multidisciplinary
Yuya Matsumoto, Ryo Natsuaki, Akira Hirose
Summary: Quaternion convolutional neural networks (QCNNs) have great potential for image processing and PolSAR land classification. Conventional QCNNs suffer from reduced degree of freedom and expression ability due to fixed rotation axes. This paper proposes QCNNs that learn all four parameters of quaternion weights, maximizing their degree of freedom and taking full advantage of quaternion learning. In addition, using Pauli RGB features and normalized Stokes vectors as different features complementarily improves classification results.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Ryo Natsuaki, Akira Hirose
Summary: Synthetic Aperture Radar (SAR) needs to share spectrum with more systems and detect and suppress radio frequency interference (RFI). To achieve better detection results, researchers performed polarimetric analysis and classification of interfering signals.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Masato Ohki, Ryo Natsuaki, Sadayoshi Aoyama, Takeo Tadono
Summary: This study investigated the relationship between the multi-look number of interferometric processing, flooded building detection using interferometric coherence, and floodwater depth through simultaneous experiments and satellite monitoring. The results showed that coherence is statistically dependent on the multi-look number, the coherence-based change detection method can classify flooded buildings with 6 cm or less water depth, and there is no clear correlation between coherence degradation and floodwater depth.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Ryuta Imai, Yicheng Song, Ryo Natsuaki, Akira Hirose
Summary: This paper proposes a model-based homogeneity (MBH) method to extend compressed sensing (CS) for subsurface object detection. By utilizing the specific scattering features and shape of landmines, the MBH value is calculated from the spatial distribution of scattering feature vectors. CS can be applied with MBH, as the calculation result is generally sparse. This method eliminates clutter and reduces measurement points, significantly reducing detection time compared to conventional methods.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Gunjan Joshi, Ryo Natsuaki, Akira Hirose
Summary: This study investigates the fusion of features obtained from the L-band ALOS-2 Synthetic aperture radar (SAR) and the Sentinel-2 optical satellite by the use of neural networks. The fusion results show an increased classification accuracy compared to that of independent sensors.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Bungo Konishi, Akira Hirose, Ryo Natsuaki
Summary: In this paper, we propose an interferometric synthetic aperture radar (InSAR) phase unwrapping method based on the dynamics of the Kuramoto model. The method generates a quality map to guide the unwrapping path by solving singular points or residues. Our results demonstrate that our proposed method achieves higher accuracy in unwrapping interferometric phase compared to other model-algorithm-based methods.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Haotian Chen, Natsuaki Ryo, Akira Hirose
Summary: In this paper, a method for predicting polarization states using a quaternion neural network (QNN) is proposed, which utilizes the geometric properties of polarization states represented on the Poincare sphere to achieve high-precision prediction. Experimental results demonstrate that predicting the polarization state enables obtaining more accurate CSI and a lower bit error rate.
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Seko Nagae, Lena Azuma, Ryo Natsuaki, Akira Hirose
Summary: This paper introduces a millimeter-wave human glucose-concentration estimation system based on CVNN and dielectric-loaded probes, demonstrating that silicon loading at the probes can enhance the estimation ability.
2022 IEEE USNC-URSI RADIO SCIENCE MEETING (JOINT WITH AP-S SYMPOSIUM)
(2022)
Article
Geochemistry & Geophysics
Ryuta Imai, Yicheng Song, Ryo Natsuaki, Akira Hirose
Summary: This article proposes a model-based homogeneity method to extend compressed sensing for landmine-detection ground penetrating radar. The method visualizes landmines based on homogeneity of high-dimensional scattering features, excluding clutter and reducing measurement points. Experiments demonstrate a significant reduction in total measurement and processing time.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Haotian Chen, Ryo Natsuaki, Akira Hirose
Summary: This paper proposes polarization state and phasor prediction methods based on quaternion neural networks and complex-valued neural networks. Experimental results show that the proposed prediction scheme can improve bit error rate performance in wireless communication systems and reveals that polarization state is an important characteristic in radio wave systems.
Proceedings Paper
Engineering, Aerospace
Roger Oliva, Paolo de Matthaeis, Ryo Natsuaki
Summary: Radio Frequency Interference contamination is a growing problem for Earth Observation satellites, but it is not being properly reported. A new IEEE standard is being developed to measure the amount of RFI in the frequency bands used by remote sensing satellites, which will enable proper monitoring of RFI trends in each frequency band.
2022 IEEE SPACE HARDWARE AND RADIO CONFERENCE (SHARC)
(2022)
Article
Engineering, Electrical & Electronic
Yuya Matsumoto, Ryo Natsuaki, Akira Hirose
Summary: Quaternion convolutional neural networks (QCNNs) have expanded their applications in processing optical and polarimetric synthetic aperture radar (PolSAR) images, and can learn the interrelationship among the channel components. This article proposes full-learning rotational QCNNs and uses two different types of features to improve classification performance.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
