Pub Date : 2022-03-16DOI: 10.3389/frsip.2022.842570
Tiziana Cattai, Alessandro Delfino, G. Scarano, S. Colonnese
Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous means of transport. However, the estimation of 3D coordinates on the surface as well as the signal defined on the surface points (vertices) is affected by noise. The presence of perturbations can jeopardize the application of PCs in real scenarios. Here, we propose a novel visually driven point cloud denoising algorithm (VIPDA) inspired by visually driven filtering approaches. VIPDA leverages recent results on local harmonic angular filters extending image processing tools to the PC domain. In more detail, the VIPDA method applies a harmonic angular analysis of the PC shape so as to associate each vertex of the PC to suit a set of neighbors and to drive the denoising in accordance with the local PC variability. The performance of VIPDA is assessed by numerical simulations on synthetic and real data corrupted by Gaussian noise. We also compare our results with state-of-the-art methods, and we verify that VIPDA outperforms the others in terms of the signal-to-noise ratio (SNR). We demonstrate that our method has strong potential in denoising the point clouds by leveraging a visually driven approach to the analysis of 3D surfaces.
{"title":"VIPDA: A Visually Driven Point Cloud Denoising Algorithm Based on Anisotropic Point Cloud Filtering","authors":"Tiziana Cattai, Alessandro Delfino, G. Scarano, S. Colonnese","doi":"10.3389/frsip.2022.842570","DOIUrl":"https://doi.org/10.3389/frsip.2022.842570","url":null,"abstract":"Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous means of transport. However, the estimation of 3D coordinates on the surface as well as the signal defined on the surface points (vertices) is affected by noise. The presence of perturbations can jeopardize the application of PCs in real scenarios. Here, we propose a novel visually driven point cloud denoising algorithm (VIPDA) inspired by visually driven filtering approaches. VIPDA leverages recent results on local harmonic angular filters extending image processing tools to the PC domain. In more detail, the VIPDA method applies a harmonic angular analysis of the PC shape so as to associate each vertex of the PC to suit a set of neighbors and to drive the denoising in accordance with the local PC variability. The performance of VIPDA is assessed by numerical simulations on synthetic and real data corrupted by Gaussian noise. We also compare our results with state-of-the-art methods, and we verify that VIPDA outperforms the others in terms of the signal-to-noise ratio (SNR). We demonstrate that our method has strong potential in denoising the point clouds by leveraging a visually driven approach to the analysis of 3D surfaces.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79124780","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 : 2022-03-11DOI: 10.3389/frsip.2022.833433
Xi Xue, S. Kamata
Nuclei segmentation is fundamental and crucial for analyzing histopathological images. Generally, a pathological image contains tens of thousands of nuclei, and there exists clustered nuclei, so it is difficult to separate each nucleus accurately. Challenges against blur boundaries, inconsistent staining, and overlapping regions have adverse effects on segmentation performance. Besides, nuclei from various organs appear quite different in shape and size, which may lead to the problems of over-segmentation and under-segmentation. In order to capture each nucleus on different organs precisely, characteristics about both nuclei and boundaries are of equal importance. Thus, in this article, we propose a contextual mixing feature Unet (CMF-Unet), which utilizes two parallel branches, nuclei segmentation branch and boundary extraction branch, and mixes complementary feature maps from two branches to obtain rich and integrated contextual features. To ensure good segmentation performance, a multiscale kernel weighted module (MKWM) and a dense mixing feature module (DMFM) are designed. MKWM, used in both nuclei segmentation branch and boundary extraction branch, contains a multiscale kernel block to fully exploit characteristics of images and a weight block to assign more weights on important areas, so that the network can extract discriminative information efficiently. To fuse more beneficial information and get integrated feature maps, the DMFM mixes the feature maps produced by the MKWM from two branches to gather both nuclei information and boundary information and links the feature maps in a densely connected way. Because the feature maps produced by the MKWM and DMFM are both sent into the decoder part, segmentation performance can be enhanced effectively. We test the proposed method on the multi-organ nuclei segmentation (MoNuSeg) dataset. Experiments show that the proposed method not only performs well on nuclei segmentation but also has good generalization ability on different organs.
{"title":"Contextual Mixing Feature Unet for Multi-Organ Nuclei Segmentation","authors":"Xi Xue, S. Kamata","doi":"10.3389/frsip.2022.833433","DOIUrl":"https://doi.org/10.3389/frsip.2022.833433","url":null,"abstract":"Nuclei segmentation is fundamental and crucial for analyzing histopathological images. Generally, a pathological image contains tens of thousands of nuclei, and there exists clustered nuclei, so it is difficult to separate each nucleus accurately. Challenges against blur boundaries, inconsistent staining, and overlapping regions have adverse effects on segmentation performance. Besides, nuclei from various organs appear quite different in shape and size, which may lead to the problems of over-segmentation and under-segmentation. In order to capture each nucleus on different organs precisely, characteristics about both nuclei and boundaries are of equal importance. Thus, in this article, we propose a contextual mixing feature Unet (CMF-Unet), which utilizes two parallel branches, nuclei segmentation branch and boundary extraction branch, and mixes complementary feature maps from two branches to obtain rich and integrated contextual features. To ensure good segmentation performance, a multiscale kernel weighted module (MKWM) and a dense mixing feature module (DMFM) are designed. MKWM, used in both nuclei segmentation branch and boundary extraction branch, contains a multiscale kernel block to fully exploit characteristics of images and a weight block to assign more weights on important areas, so that the network can extract discriminative information efficiently. To fuse more beneficial information and get integrated feature maps, the DMFM mixes the feature maps produced by the MKWM from two branches to gather both nuclei information and boundary information and links the feature maps in a densely connected way. Because the feature maps produced by the MKWM and DMFM are both sent into the decoder part, segmentation performance can be enhanced effectively. We test the proposed method on the multi-organ nuclei segmentation (MoNuSeg) dataset. Experiments show that the proposed method not only performs well on nuclei segmentation but also has good generalization ability on different organs.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87436750","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 : 2022-03-11DOI: 10.3389/frsip.2022.816186
Simeon Mayala, Ida Herdlevær, Jonas Bull Haugsøen, Shamundeeswari Anandan, S. Gavasso, M. Brun
In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the gold standard segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.
{"title":"Brain Tumor Segmentation Based on Minimum Spanning Tree","authors":"Simeon Mayala, Ida Herdlevær, Jonas Bull Haugsøen, Shamundeeswari Anandan, S. Gavasso, M. Brun","doi":"10.3389/frsip.2022.816186","DOIUrl":"https://doi.org/10.3389/frsip.2022.816186","url":null,"abstract":"In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the gold standard segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"119 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72521852","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 : 2022-03-11DOI: 10.3389/frsip.2022.819113
Yehav Alkaher, Israel Cohen
In this paper, we address the problem of dual-microphone speech reinforcement for improving in-car speech communication via howling control. A speech reinforcement system acquires speech from a speaker’s microphone and delivers it to the other listeners in the car cabin through loudspeakers. A car cabin’s small space makes it vulnerable to acoustic feedback, resulting in the appearance of howling noises. The proposed system aims to maintain a desired high amplification gain over time while not compromising the output speech quality. The dual-microphone system consists of a microphone for speech acquisition and another microphone that monitors the environment for howling detection, where its location depends on its howling detection sensitivity. The proposed algorithm contains a gain-control segment based on the magnitude-slope-deviation measure, which reduces the amplification-gain in the case of howling detection. To find the optimal locations of the howling-detection microphone in the cabin, for a devised set of scenarios, a Pareto optimization method is applied. The Pareto optimization considers the bi-objective nature of the problem, i.e., minimizing both the relative gain-reduction and the overall speech distortion. It is shown that the proposed dual-microphone system outperforms a single-microphone-based system. The performance improvement is demonstrated by showing the higher howling detection sensitivity of the dual-microphone system. Additionally, a microphone constellation design process, for optimal howling detection, is provided through the utilization of the Pareto fronts and anti-fronts approach.
{"title":"Dual-Microphone Speech Reinforcement System With Howling-Control for In-Car Speech Communication","authors":"Yehav Alkaher, Israel Cohen ","doi":"10.3389/frsip.2022.819113","DOIUrl":"https://doi.org/10.3389/frsip.2022.819113","url":null,"abstract":"In this paper, we address the problem of dual-microphone speech reinforcement for improving in-car speech communication via howling control. A speech reinforcement system acquires speech from a speaker’s microphone and delivers it to the other listeners in the car cabin through loudspeakers. A car cabin’s small space makes it vulnerable to acoustic feedback, resulting in the appearance of howling noises. The proposed system aims to maintain a desired high amplification gain over time while not compromising the output speech quality. The dual-microphone system consists of a microphone for speech acquisition and another microphone that monitors the environment for howling detection, where its location depends on its howling detection sensitivity. The proposed algorithm contains a gain-control segment based on the magnitude-slope-deviation measure, which reduces the amplification-gain in the case of howling detection. To find the optimal locations of the howling-detection microphone in the cabin, for a devised set of scenarios, a Pareto optimization method is applied. The Pareto optimization considers the bi-objective nature of the problem, i.e., minimizing both the relative gain-reduction and the overall speech distortion. It is shown that the proposed dual-microphone system outperforms a single-microphone-based system. The performance improvement is demonstrated by showing the higher howling detection sensitivity of the dual-microphone system. Additionally, a microphone constellation design process, for optimal howling detection, is provided through the utilization of the Pareto fronts and anti-fronts approach.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76801414","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 : 2022-03-09DOI: 10.3389/frsip.2022.826967
Rui Sun, Tao Lei, Qi Chen, Zexuan Wang, Xiaogang Du, Weiqiang Zhao, A. Nandi
Edge detection technology aims to identify and extract the boundary information of image pixel mutation, which is a research hotspot in the field of computer vision. This technology has been widely used in image segmentation, target detection, and other high-level image processing technologies. In recent years, considering the problems of thick image edge contour, inaccurate positioning, and poor detection accuracy, researchers have proposed a variety of edge detection algorithms based on deep learning, such as multi-scale feature fusion, codec, network reconstruction, and so on. This paper dedicates to making a comprehensive analysis and special research on the edge detection algorithms. Firstly, by classifying the multi-level structure of traditional edge detection algorithms, the theory and method of each algorithm are introduced. Secondly, through focusing on the edge detection algorithm based on deep learning, the technical difficulties, advantages of methods, and backbone network selection of each algorithm are analysed. Then, through the experiments on the BSDS500 and NYUD dataset, the performance of each algorithm is further evaluated. It can be seen that the performance of the current edge detection algorithms is close to or even beyond the human visual level. At present, there are a few comprehensive review articles on image edge detection. This paper dedicates to making a comprehensive analysis of edge detection technology and aims to offer reference and guidance for the relevant personnel to follow up easily the current developments of edge detection and to make further improvements and innovations.
{"title":"Survey of Image Edge Detection","authors":"Rui Sun, Tao Lei, Qi Chen, Zexuan Wang, Xiaogang Du, Weiqiang Zhao, A. Nandi","doi":"10.3389/frsip.2022.826967","DOIUrl":"https://doi.org/10.3389/frsip.2022.826967","url":null,"abstract":"Edge detection technology aims to identify and extract the boundary information of image pixel mutation, which is a research hotspot in the field of computer vision. This technology has been widely used in image segmentation, target detection, and other high-level image processing technologies. In recent years, considering the problems of thick image edge contour, inaccurate positioning, and poor detection accuracy, researchers have proposed a variety of edge detection algorithms based on deep learning, such as multi-scale feature fusion, codec, network reconstruction, and so on. This paper dedicates to making a comprehensive analysis and special research on the edge detection algorithms. Firstly, by classifying the multi-level structure of traditional edge detection algorithms, the theory and method of each algorithm are introduced. Secondly, through focusing on the edge detection algorithm based on deep learning, the technical difficulties, advantages of methods, and backbone network selection of each algorithm are analysed. Then, through the experiments on the BSDS500 and NYUD dataset, the performance of each algorithm is further evaluated. It can be seen that the performance of the current edge detection algorithms is close to or even beyond the human visual level. At present, there are a few comprehensive review articles on image edge detection. This paper dedicates to making a comprehensive analysis of edge detection technology and aims to offer reference and guidance for the relevant personnel to follow up easily the current developments of edge detection and to make further improvements and innovations.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85873484","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 : 2022-03-02DOI: 10.3389/frsip.2022.847980
G. Gennarelli, V. Colonna, C. Noviello, S. Perna, F. Soldovieri, I. Catapano
Indoor occupancy sensing is a crucial problem in several application fields that have progressed from intrusion detection systems to automatic control of lighting, heating, air conditioning and many other presence-related loads. Continuous wave Doppler radar is a simple technology to face this problem due to its capability to detect human body movements (e.g., walk, run) and small chest wall vibrations associated to the cardiorespiratory activity. This work deals with a radar prototype operating at 2.4 GHz as a real-time occupancy sensor. The emphasis is on data processing approaches devoted to extract useful information from raw radar signal. Three different strategies, designed to detect human presence in indoor environments, are considered and the main goal is the assessment and comparison of their performance against experimental data collected in controlled conditions. The first strategy is based on the analysis of the standard deviation of the radar signal in time-domain; whereas the second one exploits the histogram of the time-varying signal amplitude. Finally, a third strategy based on an energy measure of the received signal Doppler spectrum is considered. The proposed detection algorithms are optimized through a set of calibration measurements and their performances and robustness are assessed by laboratory trials.
{"title":"CW Doppler Radar as Occupancy Sensor: A Comparison of Different Detection Strategies","authors":"G. Gennarelli, V. Colonna, C. Noviello, S. Perna, F. Soldovieri, I. Catapano","doi":"10.3389/frsip.2022.847980","DOIUrl":"https://doi.org/10.3389/frsip.2022.847980","url":null,"abstract":"Indoor occupancy sensing is a crucial problem in several application fields that have progressed from intrusion detection systems to automatic control of lighting, heating, air conditioning and many other presence-related loads. Continuous wave Doppler radar is a simple technology to face this problem due to its capability to detect human body movements (e.g., walk, run) and small chest wall vibrations associated to the cardiorespiratory activity. This work deals with a radar prototype operating at 2.4 GHz as a real-time occupancy sensor. The emphasis is on data processing approaches devoted to extract useful information from raw radar signal. Three different strategies, designed to detect human presence in indoor environments, are considered and the main goal is the assessment and comparison of their performance against experimental data collected in controlled conditions. The first strategy is based on the analysis of the standard deviation of the radar signal in time-domain; whereas the second one exploits the histogram of the time-varying signal amplitude. Finally, a third strategy based on an energy measure of the received signal Doppler spectrum is considered. The proposed detection algorithms are optimized through a set of calibration measurements and their performances and robustness are assessed by laboratory trials.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82557074","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 : 2022-02-23DOI: 10.3389/frsip.2022.846972
Maurice Quach, Jiahao Pang, Dong Tian, G. Valenzise, F. Dufaux
Point clouds are becoming essential in key applications with advances in capture technologies leading to large volumes of data. Compression is thus essential for storage and transmission. In this work, the state of the art for geometry and attribute compression methods with a focus on deep learning based approaches is reviewed. The challenges faced when compressing geometry and attributes are considered, with an analysis of the current approaches to address them, their limitations and the relations between deep learning and traditional ones. Current open questions in point cloud compression, existing solutions and perspectives are identified and discussed. Finally, the link between existing point cloud compression research and research problems to relevant areas of adjacent fields, such as rendering in computer graphics, mesh compression and point cloud quality assessment, is highlighted.
{"title":"Survey on Deep Learning-Based Point Cloud Compression","authors":"Maurice Quach, Jiahao Pang, Dong Tian, G. Valenzise, F. Dufaux","doi":"10.3389/frsip.2022.846972","DOIUrl":"https://doi.org/10.3389/frsip.2022.846972","url":null,"abstract":"Point clouds are becoming essential in key applications with advances in capture technologies leading to large volumes of data. Compression is thus essential for storage and transmission. In this work, the state of the art for geometry and attribute compression methods with a focus on deep learning based approaches is reviewed. The challenges faced when compressing geometry and attributes are considered, with an analysis of the current approaches to address them, their limitations and the relations between deep learning and traditional ones. Current open questions in point cloud compression, existing solutions and perspectives are identified and discussed. Finally, the link between existing point cloud compression research and research problems to relevant areas of adjacent fields, such as rendering in computer graphics, mesh compression and point cloud quality assessment, is highlighted.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86912001","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}
Compressive sensing (CS) has been extensively employed in uplink grant-free communications, where data generated from different active users are transmitted to a base station (BS) without following the strict access grant process. Nevertheless, the state-of-the-art CS algorithms rely on a highly limited category of measurement matrix, that is, pilot matrix, which may be analyzed by an eavesdropper (Eve) to infer the user’s channel information. Thus, the physical layer security becomes a critical issue in uplink grant-free communications. In this article, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The simulation results show that the proposed EA-based pilot approach possesses a high level of security by scrambling the Eve’s normalized mean square error performance of channel estimation.
{"title":"Compressive Sensing-Based Secure Uplink Grant-Free Systems","authors":"Yuanchen Wang, E. Lim, Yanfeng Zhang, Bowen Zhong, Rui Pei, Xu Zhu","doi":"10.3389/frsip.2022.837870","DOIUrl":"https://doi.org/10.3389/frsip.2022.837870","url":null,"abstract":"Compressive sensing (CS) has been extensively employed in uplink grant-free communications, where data generated from different active users are transmitted to a base station (BS) without following the strict access grant process. Nevertheless, the state-of-the-art CS algorithms rely on a highly limited category of measurement matrix, that is, pilot matrix, which may be analyzed by an eavesdropper (Eve) to infer the user’s channel information. Thus, the physical layer security becomes a critical issue in uplink grant-free communications. In this article, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The simulation results show that the proposed EA-based pilot approach possesses a high level of security by scrambling the Eve’s normalized mean square error performance of channel estimation.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85196279","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 : 2022-02-18DOI: 10.3389/frsip.2022.822894
Zenon Mathews , Luca Quiriconi, Christof Schüpbach, P. Weber
Recent advances in Passive Coherent Location (PCL) systems make combined active and passive radar sensor networks very attractive for both military and civilian air surveillance. PCL systems seem promising as cost-effective gap fillers of active radar coverage especially in alpine terrain and also as covert early warning sensors. However, PCL systems are sensitive to changes of Transmitters of Opportunity (ToO). Many approaches for energy-efficient target detection have been proposed for active radar sensor networks. However, energy-efficiency and topology optimization of combined active-passive radar sensor networks in realistic scenarios have been poorly studied until today. We here propose an unsupervised learning approach for topology optimization and energy-efficient detection in combined active-passive radar sensor networks. The interdependence of active and passive sensors in the network and the given target scenario is naturally accounted for by our approach. Optimal power budget and detection sectors of active radars and the most useful ToOs for each PCL sensor are simultaneously learned over time. This is a critical contribution for minimizing the need for active radar power budget and PCL computational resources. The power budget of active radars is minimized in a way that the added value of PCL sensors is fully exploited. We also demonstrate how our approach dynamically relearns to achieve robust performance when changes in the ToO of PCL sensors occur. We test our approach in a simulation suite for active-passive radar sensor networks using real-world air surveillance data and ToOs under real-world topographical conditions.
{"title":"Learning Resource Allocation in Active-Passive Radar Sensor Networks","authors":"Zenon Mathews , Luca Quiriconi, Christof Schüpbach, P. Weber","doi":"10.3389/frsip.2022.822894","DOIUrl":"https://doi.org/10.3389/frsip.2022.822894","url":null,"abstract":"Recent advances in Passive Coherent Location (PCL) systems make combined active and passive radar sensor networks very attractive for both military and civilian air surveillance. PCL systems seem promising as cost-effective gap fillers of active radar coverage especially in alpine terrain and also as covert early warning sensors. However, PCL systems are sensitive to changes of Transmitters of Opportunity (ToO). Many approaches for energy-efficient target detection have been proposed for active radar sensor networks. However, energy-efficiency and topology optimization of combined active-passive radar sensor networks in realistic scenarios have been poorly studied until today. We here propose an unsupervised learning approach for topology optimization and energy-efficient detection in combined active-passive radar sensor networks. The interdependence of active and passive sensors in the network and the given target scenario is naturally accounted for by our approach. Optimal power budget and detection sectors of active radars and the most useful ToOs for each PCL sensor are simultaneously learned over time. This is a critical contribution for minimizing the need for active radar power budget and PCL computational resources. The power budget of active radars is minimized in a way that the added value of PCL sensors is fully exploited. We also demonstrate how our approach dynamically relearns to achieve robust performance when changes in the ToO of PCL sensors occur. We test our approach in a simulation suite for active-passive radar sensor networks using real-world air surveillance data and ToOs under real-world topographical conditions.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86607770","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 : 2022-02-11DOI: 10.3389/frsip.2021.820617
Sina Shahsavari, P. Sarangi, P. Pal
In this paper, we consider the beamspace ESPRIT algorithm for Millimeter-Wave (mmWave) channel sensing. We provide a non-asymptotic analysis of the beamspace ESPRIT algorithm. We derive a deterministic upper bound for the matching distance error between the true angle of arrival (AoA) of the channel paths and the estimated AoA considering a bounded noise model. Additionally, we leverage the insight obtained from our theoretical analysis to propose a novel max-min criterion for beamformer design which can enhance the performance of mmWave channel estimation algorithms, including beamspace ESPRIT. We consider a family of multi-resolution beamformers which can be implemented using phase shifters and introduce a design scheme for the optimal beamformers from this family with respect to the proposed max-min criteria. We can guarantee a minimum beamforming gain uniformly over a region of possible multipath directions, which can lead to more robust channel estimation. We provide several numerical experiments to verify our theoretical claims and demonstrate the superior performance of the proposed beamformers compared to existing beamformer design criteria.
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