Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909536
Y. Altmann
In this paper, we investigate and compare approximate Bayesian methods for high-dimensional linear inverse problems where sparsity-promoting prior distributions can be used to regularized the inference process. In particular, we investigate fully factorized priors which lead to multimodal and potentially non-smooth posterior distributions such as Bernoulli-Gaussian priors. In addition to the most traditional variational Bayes framework based on mean-field approximation, we compare different implementations of power expectation-propagation (EP) in terms of estimation of the posterior means and marginal variances, using fully factorized approximations. The different methods are compared using low-dimensional examples and we then discuss the potential benefits of power EP for image restoration. These preliminary results tend to confirm that in the case of Gaussian likelihoods, EP generally provides more reliable marginal variances while power EP offers more flexibility for generalised linear inverse problems.
{"title":"On approximate Bayesian methods for large-scale sparse linear inverse problems","authors":"Y. Altmann","doi":"10.23919/eusipco55093.2022.9909536","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909536","url":null,"abstract":"In this paper, we investigate and compare approximate Bayesian methods for high-dimensional linear inverse problems where sparsity-promoting prior distributions can be used to regularized the inference process. In particular, we investigate fully factorized priors which lead to multimodal and potentially non-smooth posterior distributions such as Bernoulli-Gaussian priors. In addition to the most traditional variational Bayes framework based on mean-field approximation, we compare different implementations of power expectation-propagation (EP) in terms of estimation of the posterior means and marginal variances, using fully factorized approximations. The different methods are compared using low-dimensional examples and we then discuss the potential benefits of power EP for image restoration. These preliminary results tend to confirm that in the case of Gaussian likelihoods, EP generally provides more reliable marginal variances while power EP offers more flexibility for generalised linear inverse problems.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133189994","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909593
Sai Kiran Kadambari, Robin Francis, S. P. Chepuri
In this work, we focus on denoising smooth signals supported on simplicial complexes in a distributed manner. We assume that the simplicial signals are dominantly smooth on either the lower or upper Laplacian matrices, which are used to compose the so-called Hodge Laplacian matrix. This corresponds to denoising non-harmonic signals on simplicial complexes. We pose the denoising problem as a convex optimization problem, where we assign different weights to the quadratic regularizers related to the upper and lower Hodge Laplacian matrices and express the optimal solution as a sum of simplicial complex operators related to the two Laplacian matrices. We then use the recursive relation of the Chebyshev polynomial to implement these operators in a distributed manner. We demonstrate the efficacy of the developed framework on synthetic and real-world datasets.
{"title":"Distributed Denoising over Simplicial Complexes using Chebyshev Polynomial Approximation","authors":"Sai Kiran Kadambari, Robin Francis, S. P. Chepuri","doi":"10.23919/eusipco55093.2022.9909593","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909593","url":null,"abstract":"In this work, we focus on denoising smooth signals supported on simplicial complexes in a distributed manner. We assume that the simplicial signals are dominantly smooth on either the lower or upper Laplacian matrices, which are used to compose the so-called Hodge Laplacian matrix. This corresponds to denoising non-harmonic signals on simplicial complexes. We pose the denoising problem as a convex optimization problem, where we assign different weights to the quadratic regularizers related to the upper and lower Hodge Laplacian matrices and express the optimal solution as a sum of simplicial complex operators related to the two Laplacian matrices. We then use the recursive relation of the Chebyshev polynomial to implement these operators in a distributed manner. We demonstrate the efficacy of the developed framework on synthetic and real-world datasets.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133929856","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909827
N. Aburaed, Mohammed Q. Alkhatib, S. Marshall, J. Zabalza, Hussain Al-Ahmad
The spatial enhancement of Hyperspectral Imagery (HSI) is a popular research area among the community of image processing in general and remote sensing in particular. HSI contribute to a wide variety of industrial applications, such as Land Cover Land Use. The characterstic that distinguishes HSI from other type of images is the ability to uniquely describe objects with spectral signatures. This can be achieved due to the sensor's ability to capture reflectance in narrowly spaced wavelength bands, which yields an HSI cube with hundreds of bands. However, this ability compromises the spatial resolution of HSI, which must be improved for practicality and usability. There are several studies in the literature related to HSI Super Resolution (HSI-SR), especially using Convolutional Neural Networks (CNNs). Nonetheless, the investigation of the most suitable loss functions to train these networks is necessary and remains as an area to investigate. This paper conducts a comparative study of the most widely used loss functions and their effect on one of the state-of-the-art HSI-SR CNNs, mainly 3D-SRCNN. The paper also proposes a hybrid loss function based on the comparative results, and proves its superiority against other loss functions in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Spectral Angle Mapper (SAM).
{"title":"A Comparative Study of Loss Functions for Hyperspectral SISR","authors":"N. Aburaed, Mohammed Q. Alkhatib, S. Marshall, J. Zabalza, Hussain Al-Ahmad","doi":"10.23919/eusipco55093.2022.9909827","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909827","url":null,"abstract":"The spatial enhancement of Hyperspectral Imagery (HSI) is a popular research area among the community of image processing in general and remote sensing in particular. HSI contribute to a wide variety of industrial applications, such as Land Cover Land Use. The characterstic that distinguishes HSI from other type of images is the ability to uniquely describe objects with spectral signatures. This can be achieved due to the sensor's ability to capture reflectance in narrowly spaced wavelength bands, which yields an HSI cube with hundreds of bands. However, this ability compromises the spatial resolution of HSI, which must be improved for practicality and usability. There are several studies in the literature related to HSI Super Resolution (HSI-SR), especially using Convolutional Neural Networks (CNNs). Nonetheless, the investigation of the most suitable loss functions to train these networks is necessary and remains as an area to investigate. This paper conducts a comparative study of the most widely used loss functions and their effect on one of the state-of-the-art HSI-SR CNNs, mainly 3D-SRCNN. The paper also proposes a hybrid loss function based on the comparative results, and proves its superiority against other loss functions in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Spectral Angle Mapper (SAM).","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132637076","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909666
Siwei Zhang, Kimon Cokona, R. Pöhlmann, E. Staudinger, T. Wiedemann, A. Dammann
Swarm robotics has gained an increasing attention in applications like extraterrestrial exploration and disaster management, due to the ability of simultaneously observing at different locations and avoiding a single point of failure. In order to operate autonomously, robots in a swarm need to know their precise poses, including their positions, velocities and orientations. When external navigation infrastructures like the global navigation satellite systems (GNSS) are not ubiquitously accessible, the swarm of robots need to rely on internal measurements to estimate their poses. In this paper, we propose a cooperative 3D pose estimation framework, based on the insights of sensor characteristics that we gained from outdoor swarm navigation experiments. A decentralized particle filter (DPF) operates on each robot to estimate its pose via fusing radio-based ranging, inertial sensor data, control commands and the pose estimates of its neighbors. This framework is integrated in the swarm navigation ecosystem developed at the German Aerospace Center (DLR), and is unified for both simulations and experiments.
{"title":"Cooperative Pose Estimation in a Robotic Swarm: Framework, Simulation and Experimental Results","authors":"Siwei Zhang, Kimon Cokona, R. Pöhlmann, E. Staudinger, T. Wiedemann, A. Dammann","doi":"10.23919/eusipco55093.2022.9909666","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909666","url":null,"abstract":"Swarm robotics has gained an increasing attention in applications like extraterrestrial exploration and disaster management, due to the ability of simultaneously observing at different locations and avoiding a single point of failure. In order to operate autonomously, robots in a swarm need to know their precise poses, including their positions, velocities and orientations. When external navigation infrastructures like the global navigation satellite systems (GNSS) are not ubiquitously accessible, the swarm of robots need to rely on internal measurements to estimate their poses. In this paper, we propose a cooperative 3D pose estimation framework, based on the insights of sensor characteristics that we gained from outdoor swarm navigation experiments. A decentralized particle filter (DPF) operates on each robot to estimate its pose via fusing radio-based ranging, inertial sensor data, control commands and the pose estimates of its neighbors. This framework is integrated in the swarm navigation ecosystem developed at the German Aerospace Center (DLR), and is unified for both simulations and experiments.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121998378","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909669
Genjia Liu, Maosen Li, Siheng Chen
Graph scattering transform (GST) is mathematically-designed graph convolutional model that iteratively applies graph filter banks to achieve comprehensive feature extraction from graph signals. While GST performs excessive decomposition of graph signals in the graph spectral domain, it does not explicitly achieve multiresolution in the graph vertex domain, causing potential failure in handling graphs with hierarchical structures. To address the limitation, this work proposes novel multiscale graph scattering transform (MGST) to achieve hierarchical representations along both graph vertex and spectral domains. With recursive partitioning a graph structure, we yield multiple subgraphs at various scales and then perform scattering frequency decomposition on each subgraph. MGST finally obtains a series of representations and each of them corresponds to a specific graph vertex-spectral subband, achieving multiresolution along both graph vertex and spectral domains. In the experiments, we validate the superior empirical performances of MGST and visualize each graph vertex-spectral subband.
{"title":"Multiscale Graph Scattering Transform","authors":"Genjia Liu, Maosen Li, Siheng Chen","doi":"10.23919/eusipco55093.2022.9909669","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909669","url":null,"abstract":"Graph scattering transform (GST) is mathematically-designed graph convolutional model that iteratively applies graph filter banks to achieve comprehensive feature extraction from graph signals. While GST performs excessive decomposition of graph signals in the graph spectral domain, it does not explicitly achieve multiresolution in the graph vertex domain, causing potential failure in handling graphs with hierarchical structures. To address the limitation, this work proposes novel multiscale graph scattering transform (MGST) to achieve hierarchical representations along both graph vertex and spectral domains. With recursive partitioning a graph structure, we yield multiple subgraphs at various scales and then perform scattering frequency decomposition on each subgraph. MGST finally obtains a series of representations and each of them corresponds to a specific graph vertex-spectral subband, achieving multiresolution along both graph vertex and spectral domains. In the experiments, we validate the superior empirical performances of MGST and visualize each graph vertex-spectral subband.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114994261","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909828
Albert Podusenko, B. V. Erp, Dmitry V. Bagaev, Ismail Senöz, B. Vries
The switching autoregressive model is a flexible model for signals generated by non-stationary processes. Unfortunately, evaluation of the exact posterior distributions of the latent variables for a switching autoregressive model is analytically intractable, and this limits the applicability of switching autoregressive models in practical signal processing tasks. In this paper we present a message passing-based approach for computing approximate posterior distributions in the switching autoregressive model. Our solution tracks approximate posterior distributions in a modular way and easily extends to more complicated model variations. The proposed message passing algorithm is verified and validated on synthetic and acoustic data sets respectively.
{"title":"Message Passing-based Inference in Switching Autoregressive Models","authors":"Albert Podusenko, B. V. Erp, Dmitry V. Bagaev, Ismail Senöz, B. Vries","doi":"10.23919/eusipco55093.2022.9909828","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909828","url":null,"abstract":"The switching autoregressive model is a flexible model for signals generated by non-stationary processes. Unfortunately, evaluation of the exact posterior distributions of the latent variables for a switching autoregressive model is analytically intractable, and this limits the applicability of switching autoregressive models in practical signal processing tasks. In this paper we present a message passing-based approach for computing approximate posterior distributions in the switching autoregressive model. Our solution tracks approximate posterior distributions in a modular way and easily extends to more complicated model variations. The proposed message passing algorithm is verified and validated on synthetic and acoustic data sets respectively.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116303850","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909641
Tanuj Hasija, Tim Marrinan
In many applications, components correlated across multiple data sets represent meaningful patterns and commonalities. Estimates of these patterns can be improved when the number of correlated components is known, but since data exploration often occurs in an unsupervised setting, the number of correlated components is generally not known. In this paper, we derive a generalized likelihood ratio test (GLRT) for estimating the number of components correlated across multiple data sets. In particular, we are concerned with the scenario where the number of available samples is small. As a result of the small sample support, correlation coefficients and other summary statistics are significantly overestimated by traditional methods. The proposed test combines linear dimensionality reduction with a GLRT based on a measure of multiset correlation referred as the generalized variance cost function (mCCA-GENVAR). By jointly estimating the rank of the dimensionality reduction and the number of correlated components, we are able to provide high-accuracy estimates in the challenging sample-poor setting. These advantages are illustrated in numerical experiments that compare and contrast the proposed method with existing techniques.
{"title":"A GLRT for estimating the number of correlated components in sample-poor mCCA","authors":"Tanuj Hasija, Tim Marrinan","doi":"10.23919/eusipco55093.2022.9909641","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909641","url":null,"abstract":"In many applications, components correlated across multiple data sets represent meaningful patterns and commonalities. Estimates of these patterns can be improved when the number of correlated components is known, but since data exploration often occurs in an unsupervised setting, the number of correlated components is generally not known. In this paper, we derive a generalized likelihood ratio test (GLRT) for estimating the number of components correlated across multiple data sets. In particular, we are concerned with the scenario where the number of available samples is small. As a result of the small sample support, correlation coefficients and other summary statistics are significantly overestimated by traditional methods. The proposed test combines linear dimensionality reduction with a GLRT based on a measure of multiset correlation referred as the generalized variance cost function (mCCA-GENVAR). By jointly estimating the rank of the dimensionality reduction and the number of correlated components, we are able to provide high-accuracy estimates in the challenging sample-poor setting. These advantages are illustrated in numerical experiments that compare and contrast the proposed method with existing techniques.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114984944","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909816
M. Thomas, Fillatre Lionel, Deruaz-Pepin Laurent
Vessel noise classification is generally considered as a challenging task due to its need for robustness and reliability. Thus, classification in this domain mainly relied on expert feature. Raw waveform architectures have been historically avoided, despite their performances in other domains. This paper proposes a Learning-based Scattering Transform (LST) that efficiently learns temporal dependencies within cyclostationary signals, such as vessel noises. The LST is implememented as a Convolutional Neural Network (CNN) with short filters whose structure mimics a multiscale signal decomposition. By this way, the architecture of our neural network is intrinsically explainable. Numerical simulations compare our method to an other explainable model and classic convolutional neural networks.
{"title":"Learning-Based Scattering Transform for Explainable Classification","authors":"M. Thomas, Fillatre Lionel, Deruaz-Pepin Laurent","doi":"10.23919/eusipco55093.2022.9909816","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909816","url":null,"abstract":"Vessel noise classification is generally considered as a challenging task due to its need for robustness and reliability. Thus, classification in this domain mainly relied on expert feature. Raw waveform architectures have been historically avoided, despite their performances in other domains. This paper proposes a Learning-based Scattering Transform (LST) that efficiently learns temporal dependencies within cyclostationary signals, such as vessel noises. The LST is implememented as a Convolutional Neural Network (CNN) with short filters whose structure mimics a multiscale signal decomposition. By this way, the architecture of our neural network is intrinsically explainable. Numerical simulations compare our method to an other explainable model and classic convolutional neural networks.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064864","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909919
Matthias Pollach, Felix Schiegg, Matthias Ludwig, A. Bette, Alois Knoll
This work proposes an automated semantic segmen-tation approach for high resolution scanning electron microscope images, which enables the detection of hardware Trojans and counterfeit integrated circuits. We evaluate state of the art segmentation approaches and leverage expert domain knowledge to propose a neural network architecture tailored for our use case. We further address the challenge of the limited availability of training images and evaluate which pre-trained encoder can be leveraged most effectively for the given use case. The proposed segmentation network uses expert domain knowledge to account for the importance of separating technology features on a fine-grain level by introducing a separate boundary stream. The test results compare our network to a baseline approach and to two state-of-the-art segmentation networks.
{"title":"Boundary Enhanced Semantic Segmentation for High Resolution Electron Microscope Images","authors":"Matthias Pollach, Felix Schiegg, Matthias Ludwig, A. Bette, Alois Knoll","doi":"10.23919/eusipco55093.2022.9909919","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909919","url":null,"abstract":"This work proposes an automated semantic segmen-tation approach for high resolution scanning electron microscope images, which enables the detection of hardware Trojans and counterfeit integrated circuits. We evaluate state of the art segmentation approaches and leverage expert domain knowledge to propose a neural network architecture tailored for our use case. We further address the challenge of the limited availability of training images and evaluate which pre-trained encoder can be leveraged most effectively for the given use case. The proposed segmentation network uses expert domain knowledge to account for the importance of separating technology features on a fine-grain level by introducing a separate boundary stream. The test results compare our network to a baseline approach and to two state-of-the-art segmentation networks.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121975224","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 : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909678
Erik Tegler, Martin Larsson, M. Oskarsson, Kalle Åström
Recent advances in simultaneous estimation of both receiver and sender positions in ad-hoc sensor networks have made it possible to automatically calibrate node positions - a prerequisite for many applications. In man-made environments there are often large planar reflective surfaces that give significant reverberations. In this paper, we study geometric problems of receiver-sender node calibration in the presence of such reflective planes. We establish a rank-1 factorization problem that can be used to simplify the estimation. We also show how to estimate offsets, in the Time difference of arrival case, using only the rank constraint. Finally, we present a new solver for the minimal cases of sender-receiver position estimation. These contributions result in a powerful stratified approach for the node calibration problem, given a reflective plane. The methods are verified with both synthetic and real data.
{"title":"Sensor node calibration in presence of a dominant reflective plane","authors":"Erik Tegler, Martin Larsson, M. Oskarsson, Kalle Åström","doi":"10.23919/eusipco55093.2022.9909678","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909678","url":null,"abstract":"Recent advances in simultaneous estimation of both receiver and sender positions in ad-hoc sensor networks have made it possible to automatically calibrate node positions - a prerequisite for many applications. In man-made environments there are often large planar reflective surfaces that give significant reverberations. In this paper, we study geometric problems of receiver-sender node calibration in the presence of such reflective planes. We establish a rank-1 factorization problem that can be used to simplify the estimation. We also show how to estimate offsets, in the Time difference of arrival case, using only the rank constraint. Finally, we present a new solver for the minimal cases of sender-receiver position estimation. These contributions result in a powerful stratified approach for the node calibration problem, given a reflective plane. The methods are verified with both synthetic and real data.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434220","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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