Pub Date : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553350
Wenpeng Zhang, Yaowen Fu, Yuanyuan Li
Though high resolution time-frequency representations (TFRs) are developed and provide satisfactory results for multicomponent nonstationary signals, extracting multiple ridges from the time-frequency (TF) plot to approximate the instantaneous frequencies (IFs) for intersected components is quite difficult. In this work, the sparse time-frequency-frequency-rate representation (STFFRR) is proposed by using the short-time sparse representation (STSR) with the chirp dictionary. The instantaneous frequency rate (IFRs) and IFs of signal components can be jointly estimated via the STFFRR. As there are permutations between the IF and IFR estimates of signal components at different instants, the local k-means clustering algorithm is applied for component linking. By employing the STFFRR, the intersected components in TF plot can be well separated and robust IF estimation can be obtained. Numerical results validate the effectiveness of the proposed method.
{"title":"Sparse Time-Frequency-Frequency-Rate Representation for Multicomponent Nonstationary Signal Analysis","authors":"Wenpeng Zhang, Yaowen Fu, Yuanyuan Li","doi":"10.23919/EUSIPCO.2018.8553350","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553350","url":null,"abstract":"Though high resolution time-frequency representations (TFRs) are developed and provide satisfactory results for multicomponent nonstationary signals, extracting multiple ridges from the time-frequency (TF) plot to approximate the instantaneous frequencies (IFs) for intersected components is quite difficult. In this work, the sparse time-frequency-frequency-rate representation (STFFRR) is proposed by using the short-time sparse representation (STSR) with the chirp dictionary. The instantaneous frequency rate (IFRs) and IFs of signal components can be jointly estimated via the STFFRR. As there are permutations between the IF and IFR estimates of signal components at different instants, the local k-means clustering algorithm is applied for component linking. By employing the STFFRR, the intersected components in TF plot can be well separated and robust IF estimation can be obtained. Numerical results validate the effectiveness of the proposed method.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121820648","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553402
Rabeeh Karimi Mahabadi, C. Aprile, V. Cevher
Wearable and implantable body sensor network systems are one of the key technologies for continuous monitoring of patient's vital health status such as temperature and blood pressure, and brain activity. Such devices are critical for early detection of emergency conditions of people at risk and offer a wide range of medical facilities and services. Despite continuous advances in the field of wearable and implantable medical devices, it still faces major challenges such as energy-efficient and low-latency reconstruction of signals. This work presents a power-efficient real-time system for recovering neural signals. Such systems are of high interest for implantable medical devices, where reconstruction of neural signals needs to be done in realtime with low energy consumption. We combine a deep network and DCT-Iearning based compressive sensing framework to propose a novel and efficient compression-decompression system for neural signals. We compare our approach with state-of-the-art compressive sensing methods and show that it achieves superior reconstruction performance with significantly less computing time.
{"title":"Real-Time DCT Learning-based Reconstruction of Neural Signals","authors":"Rabeeh Karimi Mahabadi, C. Aprile, V. Cevher","doi":"10.23919/EUSIPCO.2018.8553402","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553402","url":null,"abstract":"Wearable and implantable body sensor network systems are one of the key technologies for continuous monitoring of patient's vital health status such as temperature and blood pressure, and brain activity. Such devices are critical for early detection of emergency conditions of people at risk and offer a wide range of medical facilities and services. Despite continuous advances in the field of wearable and implantable medical devices, it still faces major challenges such as energy-efficient and low-latency reconstruction of signals. This work presents a power-efficient real-time system for recovering neural signals. Such systems are of high interest for implantable medical devices, where reconstruction of neural signals needs to be done in realtime with low energy consumption. We combine a deep network and DCT-Iearning based compressive sensing framework to propose a novel and efficient compression-decompression system for neural signals. We compare our approach with state-of-the-art compressive sensing methods and show that it achieves superior reconstruction performance with significantly less computing time.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125221807","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553125
Xi Li, F. Guo, Le Yang, K. C. Ho
This paper presents a new algebraic solution for source localization using time difference of arrival (TDOA) measurements in the large equal radius (LER) scenario when the known sensor positions have random errors. The proposed method utilizes the LER condition to directly approximate the true TDOAs so that the originally nonlinear TDOA equations can be recast into ones linearly related to the source position. This enables the use of the closed-form weighted least squares (WLS) technique for source localization and makes the proposed method have lower complexity than the existing technique. The approximate efficiency of the new algorithm is established analytically under strong LER condition. The associated approximation bias is also derived and it is shown numerically to be greater than that of the benchmark technique, especially when LER condition is weak. However, through iterating the proposed method once with bias correction, the proposed method yields comparable localization accuracy with reduced complexity. The theoretical developments are validated by computer simulations.
{"title":"Complexity-Reduced Solution for TDOA Source Localization in Large Equal Radius Scenario with Sensor Position Errors","authors":"Xi Li, F. Guo, Le Yang, K. C. Ho","doi":"10.23919/EUSIPCO.2018.8553125","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553125","url":null,"abstract":"This paper presents a new algebraic solution for source localization using time difference of arrival (TDOA) measurements in the large equal radius (LER) scenario when the known sensor positions have random errors. The proposed method utilizes the LER condition to directly approximate the true TDOAs so that the originally nonlinear TDOA equations can be recast into ones linearly related to the source position. This enables the use of the closed-form weighted least squares (WLS) technique for source localization and makes the proposed method have lower complexity than the existing technique. The approximate efficiency of the new algorithm is established analytically under strong LER condition. The associated approximation bias is also derived and it is shown numerically to be greater than that of the benchmark technique, especially when LER condition is weak. However, through iterating the proposed method once with bias correction, the proposed method yields comparable localization accuracy with reduced complexity. The theoretical developments are validated by computer simulations.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116633173","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553192
Amare Kassaw, Dereje Hailemariam, A. Zoubir
Massive multiple input multiple output (MIMO) is considered as one of the promising technology to significantly improve the spectral efficiency of fifth generation (5G) networks. In this paper, we analyze the performance of uplink massive MIMO systems over a Rician fading channel and imperfect channel state information (CSI) at a base station (BS). Major Rician fading channel parameters including path-loss, shadowing and multipath fading are considered. Minimum mean square error (MMSE) based channel estimation is done at the BS. Assuming a zero-forcing (ZF) detector, a closed-form expression for the uplink achievable rate is derived and expressed as a function of system and propagation parameters. The impact of the system and propagation parameters on the achievable rate are investigated. Numerical results show that, when the Rician K-factor grows, the uplink achievable sum rate improves. Specifically, when both the number of BS antenna and the Rician K-factor become very large, channel estimation becomes more robust and the interference can be average out and thus, uplink sum rate improves sianificantlv,
{"title":"Performance Analysis of Uplink Massive MIMO System Over Rician Fading Channel","authors":"Amare Kassaw, Dereje Hailemariam, A. Zoubir","doi":"10.23919/EUSIPCO.2018.8553192","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553192","url":null,"abstract":"Massive multiple input multiple output (MIMO) is considered as one of the promising technology to significantly improve the spectral efficiency of fifth generation (5G) networks. In this paper, we analyze the performance of uplink massive MIMO systems over a Rician fading channel and imperfect channel state information (CSI) at a base station (BS). Major Rician fading channel parameters including path-loss, shadowing and multipath fading are considered. Minimum mean square error (MMSE) based channel estimation is done at the BS. Assuming a zero-forcing (ZF) detector, a closed-form expression for the uplink achievable rate is derived and expressed as a function of system and propagation parameters. The impact of the system and propagation parameters on the achievable rate are investigated. Numerical results show that, when the Rician K-factor grows, the uplink achievable sum rate improves. Specifically, when both the number of BS antenna and the Rician K-factor become very large, channel estimation becomes more robust and the interference can be average out and thus, uplink sum rate improves sianificantlv,","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116757465","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553595
Qiang Li, Jingran Lin
Recently, transmit beamforming for simultaneous wireless information and power transfer (SWIPT) has received considerable attention. Extensive studies have been done on MISO/MIMO SWIPT beamforming for broadcast channels (BCs) and interfering broadcast channels (IBCs). However, for IBCs the optimal SWIPT beamforming solution is in general not available. In this work, we consider SWIPT beamforming for multiuser MISO IBCs with multi-type receives, including pure information receivers (IRs), pure energy receivers (ERs) and simultaneous information and energy receivers. A power minimization problem with SINR and power transfer constraints on the receivers is considered. This problem is shown to be NP-hard in general. In order to get an efficient SWIPT beamforming solution, the energy-signal-aided SWIPT beamforming scheme is employed at the transmission. We show that with the help of the energy signals, the resultant beamforming problem is no longer NP-hard, and can be optimally solved by semidefinite relaxation (SDR). The key to this is to apply a recently developed low-rank solution result on a class of semidefinite programs (SDPs) to pin down the SDR tightness. Simulation results also demonstrate the efficacy of the energy signals in reducing the transmit power.
{"title":"Optimal SWIPT Beamforming for MISO Interfering Broadcast Channels with Multi - Type Receivers","authors":"Qiang Li, Jingran Lin","doi":"10.23919/EUSIPCO.2018.8553595","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553595","url":null,"abstract":"Recently, transmit beamforming for simultaneous wireless information and power transfer (SWIPT) has received considerable attention. Extensive studies have been done on MISO/MIMO SWIPT beamforming for broadcast channels (BCs) and interfering broadcast channels (IBCs). However, for IBCs the optimal SWIPT beamforming solution is in general not available. In this work, we consider SWIPT beamforming for multiuser MISO IBCs with multi-type receives, including pure information receivers (IRs), pure energy receivers (ERs) and simultaneous information and energy receivers. A power minimization problem with SINR and power transfer constraints on the receivers is considered. This problem is shown to be NP-hard in general. In order to get an efficient SWIPT beamforming solution, the energy-signal-aided SWIPT beamforming scheme is employed at the transmission. We show that with the help of the energy signals, the resultant beamforming problem is no longer NP-hard, and can be optimally solved by semidefinite relaxation (SDR). The key to this is to apply a recently developed low-rank solution result on a class of semidefinite programs (SDPs) to pin down the SDR tightness. Simulation results also demonstrate the efficacy of the energy signals in reducing the transmit power.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"55 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120861373","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553471
A. D. Firoozabadi, H. Durney, I. Soto, Miguel Sanhueza-Olave
Multiple sound source localization is one of the important topic in speech processing. GCC function is used as a traditional algorithm for sound source localization. This function estimates DOA for multiple speakers by calculation the cross-correlation between microphone signals but its accuracy decreases in adverse conditions. The aim of proposed method in this paper is localization of multiple simultaneous speakers in undesirable condition. The proposed method is based on novel 3D nested microphone array in combination with obtained information of Discrete Wavelet Transform (DWT) and subband processing. The proposed 3D nested microphone array prepares the condition for 3D localization and eliminates the spatial aliasing between microphone signals. Also, we propose the DWT for extraction the information of speech signal. Since, the spectral information of speech signal concentrates on low frequencies, we propose a structure of filter bank based on DWT to increase the frequency resolution on low frequencies. The performed evaluation on real and simulated data shows the superiority of our proposed method in comparison with Fullband and subband processing with uniform filters and uniform microphone array.
{"title":"3D Localization of Multiple Simultaneous Speakers with Discrete Wavelet Transform and Proposed 3D Nested Microphone Array","authors":"A. D. Firoozabadi, H. Durney, I. Soto, Miguel Sanhueza-Olave","doi":"10.23919/EUSIPCO.2018.8553471","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553471","url":null,"abstract":"Multiple sound source localization is one of the important topic in speech processing. GCC function is used as a traditional algorithm for sound source localization. This function estimates DOA for multiple speakers by calculation the cross-correlation between microphone signals but its accuracy decreases in adverse conditions. The aim of proposed method in this paper is localization of multiple simultaneous speakers in undesirable condition. The proposed method is based on novel 3D nested microphone array in combination with obtained information of Discrete Wavelet Transform (DWT) and subband processing. The proposed 3D nested microphone array prepares the condition for 3D localization and eliminates the spatial aliasing between microphone signals. Also, we propose the DWT for extraction the information of speech signal. Since, the spectral information of speech signal concentrates on low frequencies, we propose a structure of filter bank based on DWT to increase the frequency resolution on low frequencies. The performed evaluation on real and simulated data shows the superiority of our proposed method in comparison with Fullband and subband processing with uniform filters and uniform microphone array.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121043466","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553312
Héctor Vargas, Y. Fonseca, H. Arguello
Compressive cameras acquire measurements of a scene using random projections instead of sampling at Nyquist rate. Several reconstruction algorithms have been proposed, taking advantage of previous knowledge about the scene. However, some inference tasks require to determine only certain information of the scene without incurring in the high computational reconstruction step. By reducing the computation load related to the reconstruction problem, this paper proposes a computationally efficient object detection approach based on correlation filters and sparse representation that operate over compressive measurements. We consider the problem of object detection in remote sensing scenes with multi-band images, where the pixels are expensive. The correlation filters are designed using explicit knowledge of the target appearance and the target shape to provide a way to recognize the objects from compressive measurements. Numerical experiments show the validity and efficiency of the proposed method in terms of peak-to-side lobe ratio using simulated data.
{"title":"Object Detection on Compressive Measurements using Correlation Filters and Sparse Representation","authors":"Héctor Vargas, Y. Fonseca, H. Arguello","doi":"10.23919/EUSIPCO.2018.8553312","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553312","url":null,"abstract":"Compressive cameras acquire measurements of a scene using random projections instead of sampling at Nyquist rate. Several reconstruction algorithms have been proposed, taking advantage of previous knowledge about the scene. However, some inference tasks require to determine only certain information of the scene without incurring in the high computational reconstruction step. By reducing the computation load related to the reconstruction problem, this paper proposes a computationally efficient object detection approach based on correlation filters and sparse representation that operate over compressive measurements. We consider the problem of object detection in remote sensing scenes with multi-band images, where the pixels are expensive. The correlation filters are designed using explicit knowledge of the target appearance and the target shape to provide a way to recognize the objects from compressive measurements. Numerical experiments show the validity and efficiency of the proposed method in terms of peak-to-side lobe ratio using simulated data.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121115065","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553442
Ozlem Tugfe Demir, T. E. Tuncer
In this paper, we propose a new multi-group multicast beamforming design method for phase shift keying (PSK) modulated signals. Quality of service (QoS)-aware optimization is considered where the aim is to minimize transmission power of multiple-antenna base station under the QoS constraints of single-antenna users. In this paper, we show that symbol-level beamforming scheme proposed in the literature is not an effective design method for multi-group multicasting and modify it using rotated constellation approach in order to reduce transmission power. Proposed method enforces the known interference in a constructive manner such that the received symbol at each user is inside the correct decision region for any set of symbols. Hence, designed beamformers can be utilized throughout a transmission-frame rather than symbol-by-symbol basis. An alternating direction method of multipliers (ADMM) algorithm is presented for the proposed design problem and closed-form update equations are derived for the steps of the ADMM algorithm. Simulation results show that the proposed method decreases the transmission power significantly compared to the conventional and symbol-level beamforming.
{"title":"A New Beamformer Design Method for Multi-Group Multicasting by Enforcing Constructive Interference","authors":"Ozlem Tugfe Demir, T. E. Tuncer","doi":"10.23919/EUSIPCO.2018.8553442","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553442","url":null,"abstract":"In this paper, we propose a new multi-group multicast beamforming design method for phase shift keying (PSK) modulated signals. Quality of service (QoS)-aware optimization is considered where the aim is to minimize transmission power of multiple-antenna base station under the QoS constraints of single-antenna users. In this paper, we show that symbol-level beamforming scheme proposed in the literature is not an effective design method for multi-group multicasting and modify it using rotated constellation approach in order to reduce transmission power. Proposed method enforces the known interference in a constructive manner such that the received symbol at each user is inside the correct decision region for any set of symbols. Hence, designed beamformers can be utilized throughout a transmission-frame rather than symbol-by-symbol basis. An alternating direction method of multipliers (ADMM) algorithm is presented for the proposed design problem and closed-form update equations are derived for the steps of the ADMM algorithm. Simulation results show that the proposed method decreases the transmission power significantly compared to the conventional and symbol-level beamforming.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121117252","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 : 2018-09-01DOI: 10.23919/EUSIPCO.2018.8553245
Miguel Heredia Conde, O. Loffeld
The groundbreaking theory of compressive sensing (CS) enables reconstructing many common classes or real-world signals from a number of samples that is well below that prescribed by the Shannon sampling theorem, which exclusively relates to the bandwidth of the signal. Differently, CS takes profit of the sparsity or compressibility of the signals in an appropriate basis to reconstruct them from few measurements. A large number of algorithms exist for solving the sparse recovery problem, which can be roughly classified in greedy pursuits and l1 minimization algorithms. Chambolle and Pock's (C&P) primal-dual l1minimization algorithm has shown to deliver state-of-the-art results with optimal convergence rate. In this work we present an algorithm for l1 minimization that operates in the null space of the measurement matrix and follows a Nesterov-accelerated gradient descent structure. Restriction to the null space allows the algorithm to operate in a minimal-dimension subspace. A further novelty lies on the fact that the cost function is no longer the l1 norm of the temporal solution, but a weighted sum of its entropy and its l1 norm. The inclusion of the entropy pushes the $l_{1}$ minimization towards a de facto quasi-10 minimization, while the l1 norm term avoids divergence. Our algorithm globally outperforms C&P and other recent approaches for $l_{1}$ minimization in terms of l2reconstruction error, including a different entropy-based method.
{"title":"From L1 Minimization to Entropy Minimization: A Novel Approach for Sparse Signal Recovery in Compressive Sensing","authors":"Miguel Heredia Conde, O. Loffeld","doi":"10.23919/EUSIPCO.2018.8553245","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2018.8553245","url":null,"abstract":"The groundbreaking theory of compressive sensing (CS) enables reconstructing many common classes or real-world signals from a number of samples that is well below that prescribed by the Shannon sampling theorem, which exclusively relates to the bandwidth of the signal. Differently, CS takes profit of the sparsity or compressibility of the signals in an appropriate basis to reconstruct them from few measurements. A large number of algorithms exist for solving the sparse recovery problem, which can be roughly classified in greedy pursuits and l1 minimization algorithms. Chambolle and Pock's (C&P) primal-dual l1minimization algorithm has shown to deliver state-of-the-art results with optimal convergence rate. In this work we present an algorithm for l1 minimization that operates in the null space of the measurement matrix and follows a Nesterov-accelerated gradient descent structure. Restriction to the null space allows the algorithm to operate in a minimal-dimension subspace. A further novelty lies on the fact that the cost function is no longer the l1 norm of the temporal solution, but a weighted sum of its entropy and its l1 norm. The inclusion of the entropy pushes the $l_{1}$ minimization towards a de facto quasi-10 minimization, while the l1 norm term avoids divergence. Our algorithm globally outperforms C&P and other recent approaches for $l_{1}$ minimization in terms of l2reconstruction error, including a different entropy-based method.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125105241","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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