Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313125
M. Coutiño, S. P. Chepuri, G. Leus
In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub-spaces, we show how deterministic samplers can be designed under a Neyman-Pearson-like setting when the generalized likelihood ratio test is used. For a less stringent case than the worst case design, we introduce a submodular cost that obtains comparable results with its convex counterpart, while having a linear time heuristic for its near optimal maximization.
{"title":"Sparse sensing for composite matched subspace detection","authors":"M. Coutiño, S. P. Chepuri, G. Leus","doi":"10.1109/CAMSAP.2017.8313125","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313125","url":null,"abstract":"In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub-spaces, we show how deterministic samplers can be designed under a Neyman-Pearson-like setting when the generalized likelihood ratio test is used. For a less stringent case than the worst case design, we introduce a submodular cost that obtains comparable results with its convex counterpart, while having a linear time heuristic for its near optimal maximization.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115819225","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313138
Shuai Zhang, Yingshuai Hao, Meng Wang, J. Chow
This paper studies the low-rank matrix completion problem by exploiting the temporal correlations in the data. Connecting low-rank matrices with dynamical systems such as power systems, we propose a new model, termed multi-channel low-rank Hankel matrices, to characterize the intrinsic low-dimensional structures in a collection of time series. An accelerated multi-channel fast iterative hard thresholding (AM-FIHT) with a linear convergence rate is proposed to recover the missing points. The required number of observed entries for successful recovery is significantly reduced from conventional low-rank completion methods. Numerical experiments are carried out on recorded PMU data to verify the proposed method.
{"title":"Multi-Channel missing data recovery by exploiting the low-rank hankel structures","authors":"Shuai Zhang, Yingshuai Hao, Meng Wang, J. Chow","doi":"10.1109/CAMSAP.2017.8313138","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313138","url":null,"abstract":"This paper studies the low-rank matrix completion problem by exploiting the temporal correlations in the data. Connecting low-rank matrices with dynamical systems such as power systems, we propose a new model, termed multi-channel low-rank Hankel matrices, to characterize the intrinsic low-dimensional structures in a collection of time series. An accelerated multi-channel fast iterative hard thresholding (AM-FIHT) with a linear convergence rate is proposed to recover the missing points. The required number of observed entries for successful recovery is significantly reduced from conventional low-rank completion methods. Numerical experiments are carried out on recorded PMU data to verify the proposed method.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"99 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976463","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313095
Liming Wang, Yuejie Chi
Poisson noise is ubiquitously encountered in applications including medical and photon-limited imaging. We consider the problem of recovering and tracking the underlying Poisson rate, where the rate vector is assumed to lie in an unknown low-dimensional subspace, with possibly missing entries. A stochastic approximation (SA) algorithm is proposed to solve the problem. This algorithm alternates between two steps. It sequentially identifies the underlying subspace, and recovers coefficients associated with the subspace. The SA algorithm is then modified to obtain a memory-efficient algorithm without storing all historic data. Two theoretical performance guarantees are establish regarding the convergence of SA algorithm. Numerical experiments are provided to demonstrate the proposed algorithms for Poisson video. The memory-limited SA algorithm is shown to empirically yield similar performances to the original SA algorithm.
{"title":"Memory-Limited stochastic approximation for poisson subspace tracking","authors":"Liming Wang, Yuejie Chi","doi":"10.1109/CAMSAP.2017.8313095","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313095","url":null,"abstract":"Poisson noise is ubiquitously encountered in applications including medical and photon-limited imaging. We consider the problem of recovering and tracking the underlying Poisson rate, where the rate vector is assumed to lie in an unknown low-dimensional subspace, with possibly missing entries. A stochastic approximation (SA) algorithm is proposed to solve the problem. This algorithm alternates between two steps. It sequentially identifies the underlying subspace, and recovers coefficients associated with the subspace. The SA algorithm is then modified to obtain a memory-efficient algorithm without storing all historic data. Two theoretical performance guarantees are establish regarding the convergence of SA algorithm. Numerical experiments are provided to demonstrate the proposed algorithms for Poisson video. The memory-limited SA algorithm is shown to empirically yield similar performances to the original SA algorithm.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127217094","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313116
F. Vincent, O. Besson, É. Chaumette
Adaptive beamforming is a central processing stage in many sensor array applications. Minimum Power Distortionless Response is one of the most popular technique, but suffers from strong degradation when the sample covariance matrix is ill-conditioned due to small sample support. Many robust beamformers have been designed to circumvent this drawback, such as diagonal loading or reduced rank techniques, to cite a few. In this communication we present a new robust beamformer, based on bias analysis of the sample covariance matrix eigenvectors. This beamformer can be viewed as a bias-compensated reduced rank beamformer. This beamformer is shown to have a better behaviour than a principal component beamformer in the case of a weak signal of interest.
{"title":"Bias-Compensated MPDR beamformer for small number of samples","authors":"F. Vincent, O. Besson, É. Chaumette","doi":"10.1109/CAMSAP.2017.8313116","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313116","url":null,"abstract":"Adaptive beamforming is a central processing stage in many sensor array applications. Minimum Power Distortionless Response is one of the most popular technique, but suffers from strong degradation when the sample covariance matrix is ill-conditioned due to small sample support. Many robust beamformers have been designed to circumvent this drawback, such as diagonal loading or reduced rank techniques, to cite a few. In this communication we present a new robust beamformer, based on bias analysis of the sample covariance matrix eigenvectors. This beamformer can be viewed as a bias-compensated reduced rank beamformer. This beamformer is shown to have a better behaviour than a principal component beamformer in the case of a weak signal of interest.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127292487","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313091
Yunmei Shi, Aritra Konar, N. Sidiropoulos, X. Mao, Yongtan Liu
We consider an outage based approach for transmit beamforming where the downlink channels are modeled as random vectors drawn from an unknown distribution. Our problem model is applicable to both point-to-point transmit beamforming as well as single-group multicasting. Given the lack of channel information, we equivalently reformulate our problem as a stochastic optimization (SO) problem with a discontinuous and non-convex cost function. We design two judicious smooth approximations of the said function, which are amenable to stochastic gradient type methods. Using these, we compute approximate online solutions via streaming first-order methods (FOMs) based on intermittent, delayed, or peer feedback. Simulation results for massive MIMO systems demonstrate the effective performance of our methods.
{"title":"Transmit beamforming for minimum outage via stochastic approximation","authors":"Yunmei Shi, Aritra Konar, N. Sidiropoulos, X. Mao, Yongtan Liu","doi":"10.1109/CAMSAP.2017.8313091","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313091","url":null,"abstract":"We consider an outage based approach for transmit beamforming where the downlink channels are modeled as random vectors drawn from an unknown distribution. Our problem model is applicable to both point-to-point transmit beamforming as well as single-group multicasting. Given the lack of channel information, we equivalently reformulate our problem as a stochastic optimization (SO) problem with a discontinuous and non-convex cost function. We design two judicious smooth approximations of the said function, which are amenable to stochastic gradient type methods. Using these, we compute approximate online solutions via streaming first-order methods (FOMs) based on intermittent, delayed, or peer feedback. Simulation results for massive MIMO systems demonstrate the effective performance of our methods.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125012460","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313108
Hiroki Iimori, Răzvan-Andrei Stoica, G. Abreu
It is well known that distortion in wireless transmit signals occurs due to the non-linearity of power amplifiers. The typical cost of wireless hardware and the relatively large distances between devices allowed for such distortion to be thus far widely neglected in the wireless literature. However, recent paradigm shifting trends point to high-density networks of extreme low-cost devices. In this article we therefore target the problem of non-linear distortion in the transmit wireless signals, which is known to be adequately modelled by non-linear noise with power proportional to the energy of transmit symbols. Specifically, we propose a probabilistic constellation shaping technique in which the discrete Maxwell-Boltzmann (MB) distribution is employed to build the optimization problem for the maximization of the mutual information between transmit and receive signals, which is efficiently achieved via a golden section method. The approach is validated via simulated comparisons against systems employing conventional constellations.
{"title":"Constellation shaping for rate maximization in AWGN channels with non-linear distortion","authors":"Hiroki Iimori, Răzvan-Andrei Stoica, G. Abreu","doi":"10.1109/CAMSAP.2017.8313108","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313108","url":null,"abstract":"It is well known that distortion in wireless transmit signals occurs due to the non-linearity of power amplifiers. The typical cost of wireless hardware and the relatively large distances between devices allowed for such distortion to be thus far widely neglected in the wireless literature. However, recent paradigm shifting trends point to high-density networks of extreme low-cost devices. In this article we therefore target the problem of non-linear distortion in the transmit wireless signals, which is known to be adequately modelled by non-linear noise with power proportional to the energy of transmit symbols. Specifically, we propose a probabilistic constellation shaping technique in which the discrete Maxwell-Boltzmann (MB) distribution is employed to build the optimization problem for the maximization of the mutual information between transmit and receive signals, which is efficiently achieved via a golden section method. The approach is validated via simulated comparisons against systems employing conventional constellations.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123322069","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313175
Martin Gölz, Michael Fauss, A. Zoubir
A new algorithm is presented that combines the bootstrap and the generalized sequential probability ratio test. The latter replaces all unknown parameters with suitable estimates so that the test statistic is subject to uncertainty. The question of how to choose the decision thresholds for the generalized sequential probability ratio test such that it fulfills given constraints on the error probabilities is still open. We propose to address this problem not by adjusting the thresholds, but by bootstrapping the estimates of the unknown parameters and constructing confidence intervals for the test statistic. The stopping rule of the test is then defined in terms of this confidence interval instead of the test statistic itself. The proposed procedure is reliable and admits the beneficial properties of sequential tests in terms of the expected number of samples. It can hence be useful for applications where making observations is expensive or time critical, as is often the case in Internet-of-Things, data analytics or wireless communications.
{"title":"A bootstrapped sequential probability ratio test for signal processing applications","authors":"Martin Gölz, Michael Fauss, A. Zoubir","doi":"10.1109/CAMSAP.2017.8313175","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313175","url":null,"abstract":"A new algorithm is presented that combines the bootstrap and the generalized sequential probability ratio test. The latter replaces all unknown parameters with suitable estimates so that the test statistic is subject to uncertainty. The question of how to choose the decision thresholds for the generalized sequential probability ratio test such that it fulfills given constraints on the error probabilities is still open. We propose to address this problem not by adjusting the thresholds, but by bootstrapping the estimates of the unknown parameters and constructing confidence intervals for the test statistic. The stopping rule of the test is then defined in terms of this confidence interval instead of the test statistic itself. The proposed procedure is reliable and admits the beneficial properties of sequential tests in terms of the expected number of samples. It can hence be useful for applications where making observations is expensive or time critical, as is often the case in Internet-of-Things, data analytics or wireless communications.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126778607","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313087
Sandra Bender, Meik Dörpinghaus, G. Fettweis
We analyze the spectral efficiency of a 1-bit quantized multiple-input multiple-output channel with oversampling in time, where 1-bit quantization could become a key component to achieve the energy efficiency that is required for future communication systems. Applying adapted signaling schemes and appropriate power allocation algorithms, we derive lower bounds on the spectral efficiency based on results for the single-input single-output case. We show the potential gain compared to previous results without oversampling.
{"title":"On the achievable rate of multi-antenna receivers with oversampled 1-bit quantization","authors":"Sandra Bender, Meik Dörpinghaus, G. Fettweis","doi":"10.1109/CAMSAP.2017.8313087","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313087","url":null,"abstract":"We analyze the spectral efficiency of a 1-bit quantized multiple-input multiple-output channel with oversampling in time, where 1-bit quantization could become a key component to achieve the energy efficiency that is required for future communication systems. Applying adapted signaling schemes and appropriate power allocation algorithms, we derive lower bounds on the spectral efficiency based on results for the single-input single-output case. We show the potential gain compared to previous results without oversampling.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115013575","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313168
Miguel Angel Ribot, J. Cabeza, P. Closas, C. Botteron, P. Farine
This paper characterizes the estimation performance of synthetic aperture (SA) techniques in the context of moving GNSS receivers. Under the assumption of a stationary channel, SA techniques transform a single antenna into a virtual array. We first introduce a model for the GNSS signal received by a single moving antenna. Leveraging this model, SA processing enables direction-of-arrival (DOA) and beamforming on a single antenna. The model does not make use of the narrowband assumption, which makes it suitable for relatively large trajectories. In addition, it includes the effects of the polarization mismatch between the received signal and the receiving antenna. Then, the proposed model is used to derive the Cramér-Rao lower bound (CRB) for the joint estimation of the received signal amplitudes, synchronization and DOA parameters. We compute the CRB for two different antenna motions, with results depending on the antenna trajectory as well as on the scenario geometry. Results highlight how SA processing profits from spatial and polarization diversities, pointing out its potential for DOA estimation and beamforming applications in moving GNSS platforms, such as unmanned air vehicles or smartphones.
{"title":"Estimation bounds for GNSS synthetic aperture techniques","authors":"Miguel Angel Ribot, J. Cabeza, P. Closas, C. Botteron, P. Farine","doi":"10.1109/CAMSAP.2017.8313168","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313168","url":null,"abstract":"This paper characterizes the estimation performance of synthetic aperture (SA) techniques in the context of moving GNSS receivers. Under the assumption of a stationary channel, SA techniques transform a single antenna into a virtual array. We first introduce a model for the GNSS signal received by a single moving antenna. Leveraging this model, SA processing enables direction-of-arrival (DOA) and beamforming on a single antenna. The model does not make use of the narrowband assumption, which makes it suitable for relatively large trajectories. In addition, it includes the effects of the polarization mismatch between the received signal and the receiving antenna. Then, the proposed model is used to derive the Cramér-Rao lower bound (CRB) for the joint estimation of the received signal amplitudes, synchronization and DOA parameters. We compute the CRB for two different antenna motions, with results depending on the antenna trajectory as well as on the scenario geometry. Results highlight how SA processing profits from spatial and polarization diversities, pointing out its potential for DOA estimation and beamforming applications in moving GNSS platforms, such as unmanned air vehicles or smartphones.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115185629","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 : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313096
R. K. Miranda, J. Costa, G. D. Galdo, F. Roemer
For the next generation communications, a high data-rate scenario is expected not only due to the increasing amount of mobile subscribers, but also due to the impact of technologies such as the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) and Virtual Reality (VR). One of the key technologies to allow for a better exploitation of the scarce spectrum is the incorporation of antenna arrays into communication devices. In that sense, beamforming is an array processing tool that provides spatial separation of multiple sources sharing the same spectrum band. In this work, we propose a framework composed of a bank of frequency invariant beamformers (FIB) and an adaptive parallel factor analysis (PARAFAC) decomposition instead of the state-of-the art independent component analysis (ICA). The original PARAFAC adaptation is modified for scenarios where the signals are time-correlated (non-white) and the a pseudo-inversion step is added for an increased accuracy. Our proposed framework outperforms the state-of-the-art methods in terms of accuracy and convergence.
{"title":"Broadband beamforming via frequency invariance transformation and PARAFAC decomposition","authors":"R. K. Miranda, J. Costa, G. D. Galdo, F. Roemer","doi":"10.1109/CAMSAP.2017.8313096","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313096","url":null,"abstract":"For the next generation communications, a high data-rate scenario is expected not only due to the increasing amount of mobile subscribers, but also due to the impact of technologies such as the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) and Virtual Reality (VR). One of the key technologies to allow for a better exploitation of the scarce spectrum is the incorporation of antenna arrays into communication devices. In that sense, beamforming is an array processing tool that provides spatial separation of multiple sources sharing the same spectrum band. In this work, we propose a framework composed of a bank of frequency invariant beamformers (FIB) and an adaptive parallel factor analysis (PARAFAC) decomposition instead of the state-of-the art independent component analysis (ICA). The original PARAFAC adaptation is modified for scenarios where the signals are time-correlated (non-white) and the a pseudo-inversion step is added for an increased accuracy. Our proposed framework outperforms the state-of-the-art methods in terms of accuracy and convergence.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128578759","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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