Pub Date : 2020-11-02DOI: 10.1049/iet-spr.2020.0233
Sowjanya Modalavalasa, U. K. Sahoo, A. Sahoo
: The traditional least-squares based diffusion least mean squares is not robust against outliers present in either desired data or input data. The diffusion minimum generalised rank (GR) norm algorithm proposed in the earlier works of the authors was able to effectively estimate the parameter of interest in presence of outliers in both desired and input data. However, this manuscript deals with the robust distributed estimation over distributed networks exploiting sparsity underlying in the system model. The proposed algorithm is based on both GR norm and compressive sensing, where GR norm ensures robustness against outliers in input as well as desired data. The techniques from compressive sensing endow the network with adaptive learning of the sparse structure form the incoming data in real-time and it also enables tracking of the sparsity variations of the system model. The mean and mean square convergence of the proposed algorithm are analysed and the conditions under which the proposed algorithm outperforms the unregularised diffusion GR norm algorithm are also investigated. The proposed algorithms are validated for three different applications namely distributed parameter estimation, tracking and distributed power spectrum estimation.
{"title":"Sparse distributed learning based on diffusion minimum generalised rank norm","authors":"Sowjanya Modalavalasa, U. K. Sahoo, A. Sahoo","doi":"10.1049/iet-spr.2020.0233","DOIUrl":"https://doi.org/10.1049/iet-spr.2020.0233","url":null,"abstract":": The traditional least-squares based diffusion least mean squares is not robust against outliers present in either desired data or input data. The diffusion minimum generalised rank (GR) norm algorithm proposed in the earlier works of the authors was able to effectively estimate the parameter of interest in presence of outliers in both desired and input data. However, this manuscript deals with the robust distributed estimation over distributed networks exploiting sparsity underlying in the system model. The proposed algorithm is based on both GR norm and compressive sensing, where GR norm ensures robustness against outliers in input as well as desired data. The techniques from compressive sensing endow the network with adaptive learning of the sparse structure form the incoming data in real-time and it also enables tracking of the sparsity variations of the system model. The mean and mean square convergence of the proposed algorithm are analysed and the conditions under which the proposed algorithm outperforms the unregularised diffusion GR norm algorithm are also investigated. The proposed algorithms are validated for three different applications namely distributed parameter estimation, tracking and distributed power spectrum estimation.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116399045","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 : 2020-10-29DOI: 10.1049/iet-spr.2020.0229
Jin He, Linna Li, Ting Shu
: In sensor array processing literature, near-field bearing and range estimation algorithms generally use spherical wavefront to model only array sensors’ phase response (sometimes with Fresnel approximation) and assume equality in amplitude response. Ignoring the range dependent amplitudes, though facilitating the algorithmic development, will cause systematic estimation errors due to model mismatch. By taking the spherical wavefront amplitude into account, a new bearing and range estimation algorithm for locating multiple near-field sinusoid sources is presented. With the estimation of the near- field sensor array's response vector, closed-form formulas for bearing and range estimates are derived from its magnitude, or phase, or both. The problem of estimation ambiguity is discussed as well. Cramér-Rao bound is also derived to serve as a benchmark for performance study.
{"title":"Bearing and range estimation with an exact source-sensor spatial model","authors":"Jin He, Linna Li, Ting Shu","doi":"10.1049/iet-spr.2020.0229","DOIUrl":"https://doi.org/10.1049/iet-spr.2020.0229","url":null,"abstract":": In sensor array processing literature, near-field bearing and range estimation algorithms generally use spherical wavefront to model only array sensors’ phase response (sometimes with Fresnel approximation) and assume equality in amplitude response. Ignoring the range dependent amplitudes, though facilitating the algorithmic development, will cause systematic estimation errors due to model mismatch. By taking the spherical wavefront amplitude into account, a new bearing and range estimation algorithm for locating multiple near-field sinusoid sources is presented. With the estimation of the near- field sensor array's response vector, closed-form formulas for bearing and range estimates are derived from its magnitude, or phase, or both. The problem of estimation ambiguity is discussed as well. Cramér-Rao bound is also derived to serve as a benchmark for performance study.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957596","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 : 2020-10-29DOI: 10.1049/iet-spr.2019.0547
Ning Wang, Yinya Li, Jinliang Cong, A. Sheng
: This paper investigates the distributed state estimation for a class of linear time-varying systems with intermittent observations in sensor networks. Unlike the existing studies in distributed state estimation, this work considers the scenario where the cross-covariances between different sensors are unavailable and the measurements for state estimation encounter intermittent observations and/or random losses. For this practical scenario, a new sequential covariance intersection-based Kalman consensus filer (SCIKCF) is then developed. We show that, with the proposed SCIKCF, each sensor can achieve consensus estimates regardless of the order of fusion. Furthermore, the stability of the SCIKCF as well as the boundedness of the estimation error and the corresponding error covariances are analysed. Finally, three examples are performed to verify the effectiveness of the proposed SCIKCF.
{"title":"Sequential covariance intersection-based Kalman consensus filter with intermittent observations","authors":"Ning Wang, Yinya Li, Jinliang Cong, A. Sheng","doi":"10.1049/iet-spr.2019.0547","DOIUrl":"https://doi.org/10.1049/iet-spr.2019.0547","url":null,"abstract":": This paper investigates the distributed state estimation for a class of linear time-varying systems with intermittent observations in sensor networks. Unlike the existing studies in distributed state estimation, this work considers the scenario where the cross-covariances between different sensors are unavailable and the measurements for state estimation encounter intermittent observations and/or random losses. For this practical scenario, a new sequential covariance intersection-based Kalman consensus filer (SCIKCF) is then developed. We show that, with the proposed SCIKCF, each sensor can achieve consensus estimates regardless of the order of fusion. Furthermore, the stability of the SCIKCF as well as the boundedness of the estimation error and the corresponding error covariances are analysed. Finally, three examples are performed to verify the effectiveness of the proposed SCIKCF.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022685","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 : 2020-10-06DOI: 10.1049/iet-spr.2020.0095
A. C. Rosell, Jorge Cogo, J. Areta, J. P. Pascual
: A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.
提出了一种估计气象雷达平均多普勒速度和谱宽的深度学习方法。它可以在有或没有地面杂波的情况下运行。该方法使用一个具有两个分支的深度神经网络，一个用于速度估计，另一个用于谱宽估计。分析了不同的网络结构，并根据其验证性能选择了一种网络结构，同时考虑了串行和并行实现。训练是使用涵盖广泛可能场景的合成数据进行的。蒙特卡罗实现用于评估所提出的方法在不同天气条件下的性能。结果与无地杂波信号的脉冲对处理(PPP)和有地杂波信号的高斯模型自适应处理(GMAP)两种标准方法进行了比较。将所提出的算法与GMAP进行比较，获得更好的估计，并与PPP进行比较。该性能还使用位于阿根廷San Carlos de Bariloche的c波段雷达RMA-12的真实天气数据进行了验证。经过训练后，所提出的方法需要适度的计算负荷，并且具有一次处理所有数据的优点，使其成为实时实现的良好候选。
{"title":"Doppler processing in weather radar using deep learning","authors":"A. C. Rosell, Jorge Cogo, J. Areta, J. P. Pascual","doi":"10.1049/iet-spr.2020.0095","DOIUrl":"https://doi.org/10.1049/iet-spr.2020.0095","url":null,"abstract":": A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126281660","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 : 2020-10-06DOI: 10.1049/iet-spr.2019.0131
G. Deekshitha, L. Mary
: Spoken Term Detection (STD) is the process of locating the occurrences of spoken queries in a given speech database. Generally, two methods are adopted for STD: an ASR based sequence matching and ASR-free, feature-based template matching. If a well-performing ASR is available, the former STD method is accurate. However, to build an ASR with consistent performance, several hours of labelled corpora is required. Template matching methods work well for small or chopped utterances. However, in practice, the volume of the search database can be huge, containing sentences of varying lengths. Hence time complexity of template matching techniques will be high, which makes them impractical for realistic search applications. In this work, a two-stage STD system is proposed, which combines the ASR-based phoneme sequence matching in the first stage and feature sequence template matching of selected locations in the second stage. The time complexity of the second stage is reduced by performing DTW-based template matching only at probable query locations identified by the first stage. ‘Split and match’ approach helps to reduce the false-positives in case of longer query words. Effectiveness of the proposed method is demonstrated using English and Malayalam datasets.
{"title":"Two-stage spoken term detection system for under-resourced languages","authors":"G. Deekshitha, L. Mary","doi":"10.1049/iet-spr.2019.0131","DOIUrl":"https://doi.org/10.1049/iet-spr.2019.0131","url":null,"abstract":": Spoken Term Detection (STD) is the process of locating the occurrences of spoken queries in a given speech database. Generally, two methods are adopted for STD: an ASR based sequence matching and ASR-free, feature-based template matching. If a well-performing ASR is available, the former STD method is accurate. However, to build an ASR with consistent performance, several hours of labelled corpora is required. Template matching methods work well for small or chopped utterances. However, in practice, the volume of the search database can be huge, containing sentences of varying lengths. Hence time complexity of template matching techniques will be high, which makes them impractical for realistic search applications. In this work, a two-stage STD system is proposed, which combines the ASR-based phoneme sequence matching in the first stage and feature sequence template matching of selected locations in the second stage. The time complexity of the second stage is reduced by performing DTW-based template matching only at probable query locations identified by the first stage. ‘Split and match’ approach helps to reduce the false-positives in case of longer query words. Effectiveness of the proposed method is demonstrated using English and Malayalam datasets.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125865162","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 : 2020-10-06DOI: 10.1049/iet-spr.2020.0201
Hyung-Rae Park, Jian Li
: This study addresses the problem of direction-of-arrival (DOA) estimation of coherent signals via sparse parameter estimation. Since many sparse methods provide good performances regardless of signal correlations and array geometry, they can be considered as candidates for DOA estimation of coherent signals impinging on a sensor array with arbitrary geometry. However, their straightforward applications require high computational loads especially for two-dimensional (2D) DOA estimation. Two efficient methods based on sparse parameter estimation are herein presented; one is a combined approach of sparse estimation and the RELAX algorithm extended for 2D DOA estimation and the other relies on the adaptive 2D grid refinement and power update control. Numerical simulations are performed to demonstrate the efficiency of the proposed methods using a uniform circular array for both 1D and 2D DOA estimation cases. It is shown that sparse asymptotic minimum variance (SAMV)-RELAX, a combined approach of SAMV and RELAX, outperforms SAMV and multi-stage SAMV in 2D scenarios without suffering from plateau effects for off-grid signals and that its computational load is significantly lower than those of SAMV and multi-stage SAMV. In addition, SAMV-RELAX does not require the difficult selection of grid parameters for fine DOA estimation unlike the multi-stage approach.
{"title":"Efficient sparse parameter estimation based methods for two-dimensional DOA estimation of coherent signals","authors":"Hyung-Rae Park, Jian Li","doi":"10.1049/iet-spr.2020.0201","DOIUrl":"https://doi.org/10.1049/iet-spr.2020.0201","url":null,"abstract":": This study addresses the problem of direction-of-arrival (DOA) estimation of coherent signals via sparse parameter estimation. Since many sparse methods provide good performances regardless of signal correlations and array geometry, they can be considered as candidates for DOA estimation of coherent signals impinging on a sensor array with arbitrary geometry. However, their straightforward applications require high computational loads especially for two-dimensional (2D) DOA estimation. Two efficient methods based on sparse parameter estimation are herein presented; one is a combined approach of sparse estimation and the RELAX algorithm extended for 2D DOA estimation and the other relies on the adaptive 2D grid refinement and power update control. Numerical simulations are performed to demonstrate the efficiency of the proposed methods using a uniform circular array for both 1D and 2D DOA estimation cases. It is shown that sparse asymptotic minimum variance (SAMV)-RELAX, a combined approach of SAMV and RELAX, outperforms SAMV and multi-stage SAMV in 2D scenarios without suffering from plateau effects for off-grid signals and that its computational load is significantly lower than those of SAMV and multi-stage SAMV. In addition, SAMV-RELAX does not require the difficult selection of grid parameters for fine DOA estimation unlike the multi-stage approach.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115190091","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 : 2020-10-06DOI: 10.1049/iet-spr.2020.0067
Ziheng Zhou, X. Luan, Shuping He, Fei Liu
: To solve the problem of high-order moment Gaussian distribution (HGD) noise in state estimation, a fusion filter for Markov jump linear systems (MJLSs) with high-order moment information obtained from sensor data is designed. To obtain high-order moment information, the multi-sensor MJLS is converted to a single-mode system composed of high-order moment components by using a cumulant generating function. Next, a filter design based on Bayesian theory is established to achieve state estimation with a high-order moment information form according to the transformed single-mode deterministic system. Subsequently, a high-order moment fusion technique based on entropy theory is proposed to obtain a more accurate estimation result of the state by using the high-order moment information obtained from various sensors. Comparing the first- and second-order moment information obtained by traditional Gaussian distribution, the HGD introduces higher-order moment information and makes the fusion process more reasonable. In this way, a more precise and reasonable performance of the state estimation is achieved, depending on the sensor fusion technique. To confirm the effectiveness and advantages of the proposed method, a numerical simulation example is provided with various fusion methods. Thus, the performance of the proposed fusion filter design is verified.
{"title":"High-order moment multi-sensor fusion filter design of Markov jump linear systems","authors":"Ziheng Zhou, X. Luan, Shuping He, Fei Liu","doi":"10.1049/iet-spr.2020.0067","DOIUrl":"https://doi.org/10.1049/iet-spr.2020.0067","url":null,"abstract":": To solve the problem of high-order moment Gaussian distribution (HGD) noise in state estimation, a fusion filter for Markov jump linear systems (MJLSs) with high-order moment information obtained from sensor data is designed. To obtain high-order moment information, the multi-sensor MJLS is converted to a single-mode system composed of high-order moment components by using a cumulant generating function. Next, a filter design based on Bayesian theory is established to achieve state estimation with a high-order moment information form according to the transformed single-mode deterministic system. Subsequently, a high-order moment fusion technique based on entropy theory is proposed to obtain a more accurate estimation result of the state by using the high-order moment information obtained from various sensors. Comparing the first- and second-order moment information obtained by traditional Gaussian distribution, the HGD introduces higher-order moment information and makes the fusion process more reasonable. In this way, a more precise and reasonable performance of the state estimation is achieved, depending on the sensor fusion technique. To confirm the effectiveness and advantages of the proposed method, a numerical simulation example is provided with various fusion methods. Thus, the performance of the proposed fusion filter design is verified.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115313357","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}
A finite sequence of equidistant samples (a sample train) of a periodic signal can be identified with a point in a multi-dimensional space. Such a point depends on the sampled signal, the sampling period, and the starting time of the sequence. If the starting time varies, then the corresponding point moves along a closed curve. We prove that such a curve, i.e., the set of all sample trains of a given length, determines the period of the sampled signal, provided that the sampling period is known. This is true even if the trains are short, and if the samples comprising trains are taken at a sub-Nyquist rate. The presented result is proved with a help of the theory of rotation numbers developed by Poincar'e. We also prove that the curve of sample trains determines the sampled signal up to a time shift, provided that the ratio of the sampling period to the period of the signal is irrational. Eventually, we give an example which shows that the assumption on incommensurability of the periods cannot be dropped.
{"title":"Period and signal reconstruction from the curve of trains of samples","authors":"M. Rupniewski","doi":"10.1049/sil2.12086","DOIUrl":"https://doi.org/10.1049/sil2.12086","url":null,"abstract":"A finite sequence of equidistant samples (a sample train) of a periodic signal can be identified with a point in a multi-dimensional space. Such a point depends on the sampled signal, the sampling period, and the starting time of the sequence. If the starting time varies, then the corresponding point moves along a closed curve. We prove that such a curve, i.e., the set of all sample trains of a given length, determines the period of the sampled signal, provided that the sampling period is known. This is true even if the trains are short, and if the samples comprising trains are taken at a sub-Nyquist rate. The presented result is proved with a help of the theory of rotation numbers developed by Poincar'e. We also prove that the curve of sample trains determines the sampled signal up to a time shift, provided that the ratio of the sampling period to the period of the signal is irrational. Eventually, we give an example which shows that the assumption on incommensurability of the periods cannot be dropped.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115742595","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 : 2020-07-30DOI: 10.1049/iet-spr.2020.0104
S. Chatterjee, R. Thakur, R. Yadav, Lalita Gupta, D. Raghuvanshi
An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power-line interference removal, DLSR and EWT perform well. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.
{"title":"Review of noise removal techniques in ECG signals","authors":"S. Chatterjee, R. Thakur, R. Yadav, Lalita Gupta, D. Raghuvanshi","doi":"10.1049/iet-spr.2020.0104","DOIUrl":"https://doi.org/10.1049/iet-spr.2020.0104","url":null,"abstract":"An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power-line interference removal, DLSR and EWT perform well. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131319721","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 : 2020-04-01DOI: 10.1049/iet-spr.2019.0343
Chee-Hyun Park, Joon‐Hyuk Chang
Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper-tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection.
{"title":"Robust range estimation algorithm based on hyper-tangent loss function","authors":"Chee-Hyun Park, Joon‐Hyuk Chang","doi":"10.1049/iet-spr.2019.0343","DOIUrl":"https://doi.org/10.1049/iet-spr.2019.0343","url":null,"abstract":"Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper-tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125908860","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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