Abstract Increasing the spectrum and time utilization rate is the goal of the next wireless communication networks. This work studies the outage performance of the reconfigurable intelligent surface (RIS)-aided integrated satellite duplex unmanned-aerial-vehicle relay terrestrial networks. Especially, the RIS is installed in the tall building to enhance the communication. To further increase the time utilization rate, the duplex unmanned aerial vehicle is utilized to enhance the time utilization efficiency. However, owing to the practical reasons, the imperfect hardware and co-channel interference are further researched in this paper. Particularly, the accurate expression for the outage probability (OP) is gotten to confirm the effects of RIS parameters, channel parameters and imperfect hardware on the considered network. To gain more insights of the OP at high signal-to-noise ratios, the asymptotic analysis for the OP is derived. Finally, some Monte Carlo simulations are provided to verify the rightness of the theoretical analysis. The simulations indicate that the OP is mainly judged by the satellite transmission link. The results also indicate that although RIS can enhance the system performance, the system performance is not decided by RIS.
{"title":"Ris-aided integrated satellite duplex UAV relay terrestrial networks with imperfect hardware and co-channel interference","authors":"Jiu Sun, Kefeng Guo, Feng Zhou, Xueling Wang, Mingfu Zhu","doi":"10.1186/s13634-023-01067-2","DOIUrl":"https://doi.org/10.1186/s13634-023-01067-2","url":null,"abstract":"Abstract Increasing the spectrum and time utilization rate is the goal of the next wireless communication networks. This work studies the outage performance of the reconfigurable intelligent surface (RIS)-aided integrated satellite duplex unmanned-aerial-vehicle relay terrestrial networks. Especially, the RIS is installed in the tall building to enhance the communication. To further increase the time utilization rate, the duplex unmanned aerial vehicle is utilized to enhance the time utilization efficiency. However, owing to the practical reasons, the imperfect hardware and co-channel interference are further researched in this paper. Particularly, the accurate expression for the outage probability (OP) is gotten to confirm the effects of RIS parameters, channel parameters and imperfect hardware on the considered network. To gain more insights of the OP at high signal-to-noise ratios, the asymptotic analysis for the OP is derived. Finally, some Monte Carlo simulations are provided to verify the rightness of the theoretical analysis. The simulations indicate that the OP is mainly judged by the satellite transmission link. The results also indicate that although RIS can enhance the system performance, the system performance is not decided by RIS.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1186/s13634-023-01066-3
Chuanyun Ouyang, Liming Cai, Bin Liu, Tianxiang Zhang
Abstract Background The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion. Purpose This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality. Results After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength. Conclusion The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.
{"title":"An improved wavelet threshold denoising approach for surface electromyography signal","authors":"Chuanyun Ouyang, Liming Cai, Bin Liu, Tianxiang Zhang","doi":"10.1186/s13634-023-01066-3","DOIUrl":"https://doi.org/10.1186/s13634-023-01066-3","url":null,"abstract":"Abstract Background The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion. Purpose This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality. Results After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength. Conclusion The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"97 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135170214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1186/s13634-023-01065-4
Xiaobing Zhang, Binjie Lu, Liang Qiao
Abstract The analysis of underwater discharge signals is of great significance for its application. Wavelet-based de-noising and analysis technology is an effective means to study underwater discharge signals. The selection of wavelets is the key to the accuracy of wavelet analysis. A scheme of wavelet selection is provided in this paper. Based on the signal characteristics and actual noise, the reference target signal and noisy signal are constructed to ensure the accuracy of wavelet performance evaluation. Cross-correlation coefficient, root mean square error, signal-to-noise ratio, and smoothness are chosen as evaluation indexes and fused by the coefficient of variation method. The selected optimal wavelet is used to process the underwater wire-guided discharge signals. The results show that the scheme is feasible and practical.
{"title":"A wavelet selection scheme in underwater discharge signal analysis","authors":"Xiaobing Zhang, Binjie Lu, Liang Qiao","doi":"10.1186/s13634-023-01065-4","DOIUrl":"https://doi.org/10.1186/s13634-023-01065-4","url":null,"abstract":"Abstract The analysis of underwater discharge signals is of great significance for its application. Wavelet-based de-noising and analysis technology is an effective means to study underwater discharge signals. The selection of wavelets is the key to the accuracy of wavelet analysis. A scheme of wavelet selection is provided in this paper. Based on the signal characteristics and actual noise, the reference target signal and noisy signal are constructed to ensure the accuracy of wavelet performance evaluation. Cross-correlation coefficient, root mean square error, signal-to-noise ratio, and smoothness are chosen as evaluation indexes and fused by the coefficient of variation method. The selected optimal wavelet is used to process the underwater wire-guided discharge signals. The results show that the scheme is feasible and practical.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"37 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135267518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1186/s13634-023-01060-9
Weidong Chen
Abstract In this paper, fast algorithms for the extrapolation of band-limited signals are presented by the sampling theorem and Fourier series in the case of over sampling. Assume the band-limited signal is known in a finite interval. We update the signal outside the interval by the Shannon sampling theorem in the case of over sampling. Then we obtain a fast algorithm in the form of Fourier series instead of the Fourier transform in the Papoulis–Gerchberg algorithm. Gibbs phenomena is analyzed in the method. An algorithm is presented to control the Gibbs phenomena, and some examples are given in the experimental results.
{"title":"Fast algorithms for band-limited extrapolation by over sampling and Fourier series","authors":"Weidong Chen","doi":"10.1186/s13634-023-01060-9","DOIUrl":"https://doi.org/10.1186/s13634-023-01060-9","url":null,"abstract":"Abstract In this paper, fast algorithms for the extrapolation of band-limited signals are presented by the sampling theorem and Fourier series in the case of over sampling. Assume the band-limited signal is known in a finite interval. We update the signal outside the interval by the Shannon sampling theorem in the case of over sampling. Then we obtain a fast algorithm in the form of Fourier series instead of the Fourier transform in the Papoulis–Gerchberg algorithm. Gibbs phenomena is analyzed in the method. An algorithm is presented to control the Gibbs phenomena, and some examples are given in the experimental results.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135273750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Impulse noise (IN) widely exists in many communication systems, which seriously affects the performance of OFDM communication systems. A joint channel and IN estimation method based on all subcarriers is designed. This method uses a sparse Bayesian learning (SBL) algorithm incorporating forward–backward Kalman filter (FB-Kalman) to tackle the problem of joint channel and IN estimation and data detection for OFDM systems. Firstly, the channel impulse response and IN are regarded as unknown sparse vectors, and a SBL framework using all subcarriers is proposed to estimate the unknown vector. The SBL theory is used based on the prior distribution of variables, and then the forward–backward joint system is established, which applies the data detection simultaneously. We also propose the FB-Kalman implementation algorithm by using the expectation maximization updates. Explicit expressions of mean and covariance matrix of the posterior distribution are derived in the E-step. Simulation results show that the proposed algorithm improves the normalized mean square error and bit error rate performance of OFDM system in the presence of IN communication environment.
{"title":"Joint channel and impulse noise estimation based on compressed sensing and Kalman filter for OFDM system","authors":"Yiting Zhao, Youming Li, Shoudong Shi, Jianding Yu","doi":"10.1186/s13634-023-01064-5","DOIUrl":"https://doi.org/10.1186/s13634-023-01064-5","url":null,"abstract":"Abstract Impulse noise (IN) widely exists in many communication systems, which seriously affects the performance of OFDM communication systems. A joint channel and IN estimation method based on all subcarriers is designed. This method uses a sparse Bayesian learning (SBL) algorithm incorporating forward–backward Kalman filter (FB-Kalman) to tackle the problem of joint channel and IN estimation and data detection for OFDM systems. Firstly, the channel impulse response and IN are regarded as unknown sparse vectors, and a SBL framework using all subcarriers is proposed to estimate the unknown vector. The SBL theory is used based on the prior distribution of variables, and then the forward–backward joint system is established, which applies the data detection simultaneously. We also propose the FB-Kalman implementation algorithm by using the expectation maximization updates. Explicit expressions of mean and covariance matrix of the posterior distribution are derived in the E-step. Simulation results show that the proposed algorithm improves the normalized mean square error and bit error rate performance of OFDM system in the presence of IN communication environment.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"70 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135512057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1186/s13634-023-01061-8
Bin Wang, Ziyan Jiang, Yanjing Sun, Yan Chen
Abstract In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.
{"title":"An intelligent signal processing method against impulsive noise interference in AIoT","authors":"Bin Wang, Ziyan Jiang, Yanjing Sun, Yan Chen","doi":"10.1186/s13634-023-01061-8","DOIUrl":"https://doi.org/10.1186/s13634-023-01061-8","url":null,"abstract":"Abstract In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1186/s13634-023-01068-1
Wei-guo Zhang, Qing Zhu, Lin-Lin Gu, Hui-Jie Lin
Abstract As a basic task in energy consumption monitoring system, load forecasting has great effects on system operation safety, generation costs and economic benefits. In this paper, a long-term load forecasting algorithm using data dimension expansion and deep feature extraction is proposed. First, the outliers of the meteorological measurements are removed by median filter method, and then the time information is encoded to form the fingerprint of the training data. Next, the full connected network (FCN) is used to expand the dimensions of the fingerprint, and the convolutional neural network (CNN) is used to extract the deep features which can obtain better feature representation. Finally, the FCN, the CNN and regression learning model are combined for jointly offline training. The optimal parameters of these network can be obtained under global solution. Experimental results show that the proposed algorithm has better load forecasting performance than existing methods.
{"title":"A deep learning-based load forecasting algorithm for energy consumption monitoring system using dimension expansion","authors":"Wei-guo Zhang, Qing Zhu, Lin-Lin Gu, Hui-Jie Lin","doi":"10.1186/s13634-023-01068-1","DOIUrl":"https://doi.org/10.1186/s13634-023-01068-1","url":null,"abstract":"Abstract As a basic task in energy consumption monitoring system, load forecasting has great effects on system operation safety, generation costs and economic benefits. In this paper, a long-term load forecasting algorithm using data dimension expansion and deep feature extraction is proposed. First, the outliers of the meteorological measurements are removed by median filter method, and then the time information is encoded to form the fingerprint of the training data. Next, the full connected network (FCN) is used to expand the dimensions of the fingerprint, and the convolutional neural network (CNN) is used to extract the deep features which can obtain better feature representation. Finally, the FCN, the CNN and regression learning model are combined for jointly offline training. The optimal parameters of these network can be obtained under global solution. Experimental results show that the proposed algorithm has better load forecasting performance than existing methods.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1186/s13634-023-01069-0
Anju Jose Tom, Laura Toni, Thomas Maugey
Abstract This work proposes an end-to-end user-centric sampling method aimed at selecting the images from an image collection that are able to maximize the information perceived by a given user. As main contributions, we first introduce novel metrics that assess the amount of perceived information retained by the user when experiencing a set of images. Given the actual information present in a set of images, which is the volume spanned by the set in the corresponding latent space, we show how to take into account the user’s preferences in such a volume calculation to build a user-centric metric for the perceived information. Finally, we propose a sampling strategy seeking the minimum set of images that maximize the information perceived by a given user. Experiments using the coco dataset show the ability of the proposed approach to accurately integrate user preference while keeping a reasonable diversity in the sampled image set.
{"title":"Image embedding and user multi-preference modeling for data collection sampling","authors":"Anju Jose Tom, Laura Toni, Thomas Maugey","doi":"10.1186/s13634-023-01069-0","DOIUrl":"https://doi.org/10.1186/s13634-023-01069-0","url":null,"abstract":"Abstract This work proposes an end-to-end user-centric sampling method aimed at selecting the images from an image collection that are able to maximize the information perceived by a given user. As main contributions, we first introduce novel metrics that assess the amount of perceived information retained by the user when experiencing a set of images. Given the actual information present in a set of images, which is the volume spanned by the set in the corresponding latent space, we show how to take into account the user’s preferences in such a volume calculation to build a user-centric metric for the perceived information. Finally, we propose a sampling strategy seeking the minimum set of images that maximize the information perceived by a given user. Experiments using the coco dataset show the ability of the proposed approach to accurately integrate user preference while keeping a reasonable diversity in the sampled image set.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1186/s13634-023-01063-6
Yan Gao, Haowei Wu, Haiqian Liao, Xu Chen, Shuai Yang, Heng Song
Abstract The manuscript proposes a fault diagnosis method based on graph neural network (GNN) with one-shot learning to effectively diagnose rolling bearings under variable operating conditions. In this proposed method, the convolutional neural network is utilized for feature extraction, reducing loss in the process. Subsequently, GNN applies an adjacency matrix to generate codes for one-shot learning. Experimental verification is conducted using open data from Case Western Reserve University Rolling Bearing Data Center, where four different working conditions with six types of typical faults are selected as input signals. The classification accuracy of the proposed method reaches 98.02%. To further validate its effectiveness, traditional single-learning neural networks such as Siamese, Matching Net, Prototypical Net and (Stacked Auto Encoder) SAE are introduced as comparisons. Simulation results that the proposed method outperforms all chosen methods.
提出了一种基于单次学习的图神经网络(GNN)故障诊断方法，对变工况下的滚动轴承进行有效诊断。该方法利用卷积神经网络进行特征提取，减少了特征提取过程中的损失。随后，GNN应用邻接矩阵生成一次性学习的代码。利用凯斯西储大学滚动轴承数据中心的公开数据进行实验验证，选取4种不同工况、6种典型故障类型作为输入信号。该方法的分类准确率达到98.02%。为了进一步验证其有效性，介绍了Siamese、Matching Net、Prototypical Net和(Stacked Auto Encoder) SAE等传统的单学习神经网络进行比较。仿真结果表明，该方法优于所有选择的方法。
{"title":"A fault diagnosis method for rolling bearings based on graph neural network with one-shot learning","authors":"Yan Gao, Haowei Wu, Haiqian Liao, Xu Chen, Shuai Yang, Heng Song","doi":"10.1186/s13634-023-01063-6","DOIUrl":"https://doi.org/10.1186/s13634-023-01063-6","url":null,"abstract":"Abstract The manuscript proposes a fault diagnosis method based on graph neural network (GNN) with one-shot learning to effectively diagnose rolling bearings under variable operating conditions. In this proposed method, the convolutional neural network is utilized for feature extraction, reducing loss in the process. Subsequently, GNN applies an adjacency matrix to generate codes for one-shot learning. Experimental verification is conducted using open data from Case Western Reserve University Rolling Bearing Data Center, where four different working conditions with six types of typical faults are selected as input signals. The classification accuracy of the proposed method reaches 98.02%. To further validate its effectiveness, traditional single-learning neural networks such as Siamese, Matching Net, Prototypical Net and (Stacked Auto Encoder) SAE are introduced as comparisons. Simulation results that the proposed method outperforms all chosen methods.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136063676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1186/s13634-023-01062-7
Shubh Gupta, Adyasha Mohanty, Grace Gao
Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.
{"title":"Urban localization using robust filtering at multiple linearization points","authors":"Shubh Gupta, Adyasha Mohanty, Grace Gao","doi":"10.1186/s13634-023-01062-7","DOIUrl":"https://doi.org/10.1186/s13634-023-01062-7","url":null,"abstract":"Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.","PeriodicalId":49203,"journal":{"name":"Eurasip Journal on Advances in Signal Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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