Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050627
Qixi Tan, W. Xie, Haojin Tang, Yanshan Li
Remote sensing images (RSI) have a large range of variations in the aspect of inter- and intra-class size variability across objects. As a key technology in the field of RSI processing, RSI object detection has been widely applied. Multilevel features fusion network is commonly used to improve the performance of object detection. However, the existing multilevel feature fusion networks for RSI lack the ability to combine global information. Aiming at this problem, A multi-scale attention adaptive network (MA2Net) is proposed to object detection in RSI. The main contributions of this paper are twofold. Firstly, a multi-scale attention adaptive network is designed to adaptively integrate the multilevel features. This network is composed of integrating (IG) block, channel self-attention (CS) block, and adaptive fusion (AF) block. Specifically, IG is designed to transform the multi-level features into an intermediate size. The CS block is an embedded gaussian self-attention module used to model the relationship between the feature channels. AF is developed to learn the multilevel expression of self-attention features to obtain multi-scale feature maps. Secondly, to achieve a balance between multi-task and higher accuracy, a feature align head is utilized to correctly locate and classify objects. The experimental results on DIOR show that our network can achieve higher detection accuracy than the state-of-the-art RSI object detector.
{"title":"Multi-scale Attention Adaptive Network for Object Detection in Remote Sensing Images","authors":"Qixi Tan, W. Xie, Haojin Tang, Yanshan Li","doi":"10.1109/ICICSP55539.2022.10050627","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050627","url":null,"abstract":"Remote sensing images (RSI) have a large range of variations in the aspect of inter- and intra-class size variability across objects. As a key technology in the field of RSI processing, RSI object detection has been widely applied. Multilevel features fusion network is commonly used to improve the performance of object detection. However, the existing multilevel feature fusion networks for RSI lack the ability to combine global information. Aiming at this problem, A multi-scale attention adaptive network (MA2Net) is proposed to object detection in RSI. The main contributions of this paper are twofold. Firstly, a multi-scale attention adaptive network is designed to adaptively integrate the multilevel features. This network is composed of integrating (IG) block, channel self-attention (CS) block, and adaptive fusion (AF) block. Specifically, IG is designed to transform the multi-level features into an intermediate size. The CS block is an embedded gaussian self-attention module used to model the relationship between the feature channels. AF is developed to learn the multilevel expression of self-attention features to obtain multi-scale feature maps. Secondly, to achieve a balance between multi-task and higher accuracy, a feature align head is utilized to correctly locate and classify objects. The experimental results on DIOR show that our network can achieve higher detection accuracy than the state-of-the-art RSI object detector.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124037913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050691
Fangchao Chen, Youhong Xiao, Liang Yu, Lin Chen
Fast and accurate acoustic source localization methods have essential application value in the field of aircraft. Compared with traditional model-based methods, acoustic source localization technology based on deep learning shows a good application prospect. However, the uninterpretability of deep learning limits the further development of this technology. This paper proposes a deep network based on fast iterative shrinkage threshold algorithm unfolding (FISTA-Net), which combines the advantages of model-based and deep learning methods. In FISTA-Net, the iterative algorithm steps are mapped into the deep network, and the model parameters can be adaptively determined through end-to-end learning. The effectiveness of the proposed method is validated by a simulated dataset for training. The results show that FISTA-Net has higher spatial resolution and accuracy in acoustic source localization than the classical deconvolution algorithms.
{"title":"Fast Iteration Shrinkage Thresholding Unfolding Network for Acoustic Source Localization","authors":"Fangchao Chen, Youhong Xiao, Liang Yu, Lin Chen","doi":"10.1109/ICICSP55539.2022.10050691","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050691","url":null,"abstract":"Fast and accurate acoustic source localization methods have essential application value in the field of aircraft. Compared with traditional model-based methods, acoustic source localization technology based on deep learning shows a good application prospect. However, the uninterpretability of deep learning limits the further development of this technology. This paper proposes a deep network based on fast iterative shrinkage threshold algorithm unfolding (FISTA-Net), which combines the advantages of model-based and deep learning methods. In FISTA-Net, the iterative algorithm steps are mapped into the deep network, and the model parameters can be adaptively determined through end-to-end learning. The effectiveness of the proposed method is validated by a simulated dataset for training. The results show that FISTA-Net has higher spatial resolution and accuracy in acoustic source localization than the classical deconvolution algorithms.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124295976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050656
Zhixia Wu, Shengqi Zhu, Jingwei Xu, Lan Lan, Mengdi Zhang, Ximin Li
In the issue of interference suppression, the performance of traditional adaptive methods will decrease when mainlobe interference and sidelobe interference have angle error. To this end, a robust adaptive beamforming technique based on frequency diversity array (FDA) multiple-input multiple-output (MIMO) is proposed in this work. Firstly, preprocessing in data domain is adopted for mainlobe interference cancellation. Then, the sidelobe interference is suppressed in the receiving dimension. Finally, robust adaptive beamforming method is applied to suppress sidelobe interference. Simulation results show the effectiveness of the proposed algorithm.
{"title":"A Robust Interference Suppression Method Based on FDA-MIMO Radar","authors":"Zhixia Wu, Shengqi Zhu, Jingwei Xu, Lan Lan, Mengdi Zhang, Ximin Li","doi":"10.1109/ICICSP55539.2022.10050656","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050656","url":null,"abstract":"In the issue of interference suppression, the performance of traditional adaptive methods will decrease when mainlobe interference and sidelobe interference have angle error. To this end, a robust adaptive beamforming technique based on frequency diversity array (FDA) multiple-input multiple-output (MIMO) is proposed in this work. Firstly, preprocessing in data domain is adopted for mainlobe interference cancellation. Then, the sidelobe interference is suppressed in the receiving dimension. Finally, robust adaptive beamforming method is applied to suppress sidelobe interference. Simulation results show the effectiveness of the proposed algorithm.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122796236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050582
Jun-qing Ye, Yi-Hao Peng, Peichang Zhang, Qiang Li, Lei Huang
Reconfigurable Intelligent surface (RIS) has been envisioned as a promising technique for the 6th generation (6G) communications. Generally, a RIS is consist of a number of reflective elements that collaboratively adjust the direction of incident signals, and is considered to have the advantages of combating fading and shadowing problems in communication systems. In this article, by exploiting these RIS advantages, we propose to utilize the concept of RIS for the non-line-of-sight (NLOS) target radar detection. More specifically, we opt for using semi-definite relaxing (SDR) to obtain the optimal phase shift of RIS for each detection angle to build a code-book for all the optimal phase shifts, which can then be used for detecting the direction of target. The Doppler frequency shift can also be obtained via processing the echo with maximum power. Simulation results show that RIS is capable of significantly improving the coverage of radar detection.
{"title":"RIS-Assisted Radar NLOS Target Detection","authors":"Jun-qing Ye, Yi-Hao Peng, Peichang Zhang, Qiang Li, Lei Huang","doi":"10.1109/ICICSP55539.2022.10050582","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050582","url":null,"abstract":"Reconfigurable Intelligent surface (RIS) has been envisioned as a promising technique for the 6th generation (6G) communications. Generally, a RIS is consist of a number of reflective elements that collaboratively adjust the direction of incident signals, and is considered to have the advantages of combating fading and shadowing problems in communication systems. In this article, by exploiting these RIS advantages, we propose to utilize the concept of RIS for the non-line-of-sight (NLOS) target radar detection. More specifically, we opt for using semi-definite relaxing (SDR) to obtain the optimal phase shift of RIS for each detection angle to build a code-book for all the optimal phase shifts, which can then be used for detecting the direction of target. The Doppler frequency shift can also be obtained via processing the echo with maximum power. Simulation results show that RIS is capable of significantly improving the coverage of radar detection.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122944245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050665
Cheng Ken, Wang Fangyong, Du Shuanping
Non-cooperative passive detection is a new type of underwater acoustic detection technology, which originated in the field of radar. It detects the target scattered echoes through active signals emitted by non-cooperative sources. Since this mode can perform high-gain detection of long-distance targets without transmitting a detection signal, it has great development prospects. In this paper, a target detection method based on non-cooperative pulse signal is proposed, and the non-cooperative transmitted signal is reconstructed by fractional Fourier transform (FRFT) as a matched filter template. Taking the emission sound source as the positioning assistant point, the position of the assistant point is determined by using the dual-array positioning technology, and the target is measured by calculating the relative positional relationship between the assistant point and the target. The simulation experiments show that the non-cooperative target positioning technology used in this paper has obvious advantages compared with the traditional dual-array passive positioning technology. The mean positioning error of this method for passive targets at a distance of 15km is less than 5%.
{"title":"Passive Detection Method Based on Non-cooperative Underwater Acoustic Pulse Signal","authors":"Cheng Ken, Wang Fangyong, Du Shuanping","doi":"10.1109/ICICSP55539.2022.10050665","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050665","url":null,"abstract":"Non-cooperative passive detection is a new type of underwater acoustic detection technology, which originated in the field of radar. It detects the target scattered echoes through active signals emitted by non-cooperative sources. Since this mode can perform high-gain detection of long-distance targets without transmitting a detection signal, it has great development prospects. In this paper, a target detection method based on non-cooperative pulse signal is proposed, and the non-cooperative transmitted signal is reconstructed by fractional Fourier transform (FRFT) as a matched filter template. Taking the emission sound source as the positioning assistant point, the position of the assistant point is determined by using the dual-array positioning technology, and the target is measured by calculating the relative positional relationship between the assistant point and the target. The simulation experiments show that the non-cooperative target positioning technology used in this paper has obvious advantages compared with the traditional dual-array passive positioning technology. The mean positioning error of this method for passive targets at a distance of 15km is less than 5%.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050620
Yuanyuan Wang, Haosheng Fu, Fengzhou Dai
High-resolution imaging from gapped data has become a research hotspot in radar imaging field. Among many imaging algorithms, sparse Bayesian learning (SBL) is more robust and has greater estimation accuracy, which attracts active interest from researchers. Unfortunately, the inversion and multiplying operations are involved in each iteration of SBL lead to heavy computational complexity when they are implemented directly. In this paper, we propose a fast Fourier dictionary (FD)-based SBL algorithm to solve high-resolution imaging from gapped data, greatly reducing the calculation cost. Finally, the experimental results verify the effectiveness of the proposed method.
{"title":"High-Resolution Imaging from Gapped Data Based on Fast Sparse Bayesian Learning","authors":"Yuanyuan Wang, Haosheng Fu, Fengzhou Dai","doi":"10.1109/ICICSP55539.2022.10050620","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050620","url":null,"abstract":"High-resolution imaging from gapped data has become a research hotspot in radar imaging field. Among many imaging algorithms, sparse Bayesian learning (SBL) is more robust and has greater estimation accuracy, which attracts active interest from researchers. Unfortunately, the inversion and multiplying operations are involved in each iteration of SBL lead to heavy computational complexity when they are implemented directly. In this paper, we propose a fast Fourier dictionary (FD)-based SBL algorithm to solve high-resolution imaging from gapped data, greatly reducing the calculation cost. Finally, the experimental results verify the effectiveness of the proposed method.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125077154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050679
Luting Lin, Xianpeng Wang, Xiang Lan, Zhiguang Han
In this paper, an effective algorithm for direction-of-arrival (DOA) estimation in the presence of uncertain nonuniform noise is proposed. The Centro-Hermitian characteristic of the covariance matrix is used to turn the complex-value matrices into real-value ones with the unitary transformation. Then a unitary matrix completion technique via alternating projection is applied to determine the noise-free covariance matrix. Finally, the DOA estimation is obtained by utilizing the unitary subspace-based algorithms. In comparison with existing algorithms, the proposed method provides better performance especially with limited snapshots. Numerical simulations demonstrate the effectiveness of the proposed method.
{"title":"Alternating Projection Based Unitary Matrix Completion Method for DOA Estimation in Nonuniform Noise","authors":"Luting Lin, Xianpeng Wang, Xiang Lan, Zhiguang Han","doi":"10.1109/ICICSP55539.2022.10050679","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050679","url":null,"abstract":"In this paper, an effective algorithm for direction-of-arrival (DOA) estimation in the presence of uncertain nonuniform noise is proposed. The Centro-Hermitian characteristic of the covariance matrix is used to turn the complex-value matrices into real-value ones with the unitary transformation. Then a unitary matrix completion technique via alternating projection is applied to determine the noise-free covariance matrix. Finally, the DOA estimation is obtained by utilizing the unitary subspace-based algorithms. In comparison with existing algorithms, the proposed method provides better performance especially with limited snapshots. Numerical simulations demonstrate the effectiveness of the proposed method.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126619327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050571
Xu Jin, Zhu Wei, Ding Shengyao, Hu Kunjiao
The multi-path signal and the direct signal, lying within a beam-width of the receiving antenna, are highly correlated, which cause badly effect to the performance of low-angle altitude measurement for very high frequency (VHF) radar. A novel adaptive altitude measurement is proposed. Combine digital elevation model (DEM) with distributed sources model, a multipath signal model was constructed which is much closer to the practical rough reflection surface. The height of target was obtained through multi-dimension alternating projection and synthesized vector maximum likelihood algorithm. Simulation results and the measured data processing demonstrate the validity and feasibility of the proposed method.
{"title":"Adaptive Altitude Measurement Based on Digital Elevation Model in VHF Array Radar","authors":"Xu Jin, Zhu Wei, Ding Shengyao, Hu Kunjiao","doi":"10.1109/ICICSP55539.2022.10050571","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050571","url":null,"abstract":"The multi-path signal and the direct signal, lying within a beam-width of the receiving antenna, are highly correlated, which cause badly effect to the performance of low-angle altitude measurement for very high frequency (VHF) radar. A novel adaptive altitude measurement is proposed. Combine digital elevation model (DEM) with distributed sources model, a multipath signal model was constructed which is much closer to the practical rough reflection surface. The height of target was obtained through multi-dimension alternating projection and synthesized vector maximum likelihood algorithm. Simulation results and the measured data processing demonstrate the validity and feasibility of the proposed method.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121986634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050652
Xingguo Chen, Lin Geng, Geoffrey J. Zhang
A sparse real-time near-field acoustic holography method is proposed to precisely and stably reconstruct a transient sound field. In the proposed method, by using a time-domain impulse response function, a time domain convolution equation between the time-wavenumber pressure spectra on the hologram and reconstruction planes is first established. Then, for obtaining the time-wavenumber pressure spectrum on the reconstruction plane, a smoothed $ell_{0}$ -norm optimization algorithm is applied to solve the serious ill-conditioned problem in the inverse process. The key of solving this problem is to approximate replace the discontinuous $ell_{0}$ -norm by using a suitable continuous Gaussian function family, and the steepest ascent algorithm is introduced to minimize the continuous function for obtaining the optimal solution. Finally, the pressure time-wavenumber spectra on the reconstruction plane at all wavenumbers for all times are solved, and the corresponding time-dependent pressures are acquired by the two-dimensional inverse Fourier transform. A numerical simulation with a baffled planar piston is conducted to observe the performance of the proposed method. The simulation results prove that the proposed method can accurately reconstruct the transient sound field. The reconstruction results are also compared to those of real-time near-field acoustic holography with Tikhonov regularization and YALL1 modal to verify the superiority of the proposed method.
{"title":"A Sparse Real Time Acoustic Holography Method for Nonstationary Acoustic Source Reconstruction","authors":"Xingguo Chen, Lin Geng, Geoffrey J. Zhang","doi":"10.1109/ICICSP55539.2022.10050652","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050652","url":null,"abstract":"A sparse real-time near-field acoustic holography method is proposed to precisely and stably reconstruct a transient sound field. In the proposed method, by using a time-domain impulse response function, a time domain convolution equation between the time-wavenumber pressure spectra on the hologram and reconstruction planes is first established. Then, for obtaining the time-wavenumber pressure spectrum on the reconstruction plane, a smoothed $ell_{0}$ -norm optimization algorithm is applied to solve the serious ill-conditioned problem in the inverse process. The key of solving this problem is to approximate replace the discontinuous $ell_{0}$ -norm by using a suitable continuous Gaussian function family, and the steepest ascent algorithm is introduced to minimize the continuous function for obtaining the optimal solution. Finally, the pressure time-wavenumber spectra on the reconstruction plane at all wavenumbers for all times are solved, and the corresponding time-dependent pressures are acquired by the two-dimensional inverse Fourier transform. A numerical simulation with a baffled planar piston is conducted to observe the performance of the proposed method. The simulation results prove that the proposed method can accurately reconstruct the transient sound field. The reconstruction results are also compared to those of real-time near-field acoustic holography with Tikhonov regularization and YALL1 modal to verify the superiority of the proposed method.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124884517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High spatial resolution and high accuracy acoustic source identification is valuable in the field of aircraft. Compressive beamforming is a potential method to achieve a high-quality acoustic map. However, the off-grid problem and convex relaxation loss will reduce the performance of compressive beamforming. The weighted block L1 norm regularized two-dimensional (2D) off-grid beamforming method is proposed in this paper. In the proposed method, the solution containing the source amplitudes and off-grid differences is constrained by the weighted block L1 norm. The weighted block L1 norm can induce a more sparse solution to enhance the spatial resolution of the obtained acoustic map. Moreover, it can also recover the source amplitudes more accurately due to the fair penalty value. The performance of the proposed method is also validated by a numerical simulation. It turns out that the proposed method can identify the acoustic sources with higher spatial resolution and higher accuracy compared with conventional compressive beamforming methods.
{"title":"Weighted Block L1 Norm Regularized Two-dimensional Off-grid Compressive Beamforming for Acoustic Source Identification","authors":"Chenyu Zhang, Liang Yu, Baohong Bai, Chen Xu, Ran Wang, Youhong Xiao","doi":"10.1109/ICICSP55539.2022.10050594","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050594","url":null,"abstract":"High spatial resolution and high accuracy acoustic source identification is valuable in the field of aircraft. Compressive beamforming is a potential method to achieve a high-quality acoustic map. However, the off-grid problem and convex relaxation loss will reduce the performance of compressive beamforming. The weighted block L1 norm regularized two-dimensional (2D) off-grid beamforming method is proposed in this paper. In the proposed method, the solution containing the source amplitudes and off-grid differences is constrained by the weighted block L1 norm. The weighted block L1 norm can induce a more sparse solution to enhance the spatial resolution of the obtained acoustic map. Moreover, it can also recover the source amplitudes more accurately due to the fair penalty value. The performance of the proposed method is also validated by a numerical simulation. It turns out that the proposed method can identify the acoustic sources with higher spatial resolution and higher accuracy compared with conventional compressive beamforming methods.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134347832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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