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2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)最新文献

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Multipath mitigation using OMP and newton's method for multi-antenna GNSS receivers 基于OMP和牛顿法的多天线GNSS接收机多径缓解
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313162
L. Weiland, Thomas Wiese, W. Utschick
{"title":"Multipath mitigation using OMP and newton's method for multi-antenna GNSS receivers","authors":"L. Weiland, Thomas Wiese, W. Utschick","doi":"10.1109/CAMSAP.2017.8313162","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313162","url":null,"abstract":"We consider multipath mitigation for multi-antenna GNSS receivers using the maximum likelihood (ML) principle and Newton's method (NM). If good initial estimates for the parameters of all multipath components are available, NM is a very effective tool to find the global optimum of the ML cost function and, in particular, NM yields a very accurate estimate for the delay of the line-of-sight path. By introducing a finite grid for the parameters, algorithms from the field of sparse recovery, e.g., the orthogonal matching pursuit (OMP) algorithm, can be used as initialization schemes. In this paper, we propose two extensions of the OMP algorithm that use grid-less refinement steps, which are themselves based on NM applied to marginals of the ML objective. As numerical simulations show, initializing NM using OMP with grid-less refinement steps improves the estimation performance in the high signal-to-noise power ratio (SNR) regime if compared to, e.g., initialization using standard grid-based OMP or the SAGE algorithm.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"23 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":"127368145","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}
引用次数: 2
DOA estimation and beamforming using spatially under-sampled AVS arrays 空间欠采样AVS阵列的DOA估计和波束形成
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313203
N. R. Krishnaprasad, M. Coutiño, S. P. Chepuri, D. F. Comesaña, G. Leus
{"title":"DOA estimation and beamforming using spatially under-sampled AVS arrays","authors":"N. R. Krishnaprasad, M. Coutiño, S. P. Chepuri, D. F. Comesaña, G. Leus","doi":"10.1109/CAMSAP.2017.8313203","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313203","url":null,"abstract":"In this paper, we show the advantages of spatially under-sampled acoustic vector sensor (AVS) arrays over conventional acoustic pressure sensor (APS) arrays for performing direction-of-arrival (DOA) estimation and interference cancellation. We provide insights into the theoretical performance of an under-sampled AVS array with respect to its DOA estimation performance using the Cramér-Rao lower bound (CRLB). We also show that the minimum variance distortionless response (MVDR) beamformer suppresses the grating lobes considerably as compared to the classical (or Bartlett) beamformer leading to unambiguous DOA estimates. Finally, through zero-forcing (ZF) and minimization of maximum side lobe beamformers, the advantages of an under-sampled AVS array for interference cancellation are presented.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"283 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":"127554342","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}
引用次数: 6
Reconstruction of compressively sampled images using a nonlinear Bayesian prior 利用非线性贝叶斯先验重构压缩采样图像
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313077
S. Colonnese, M. Biagi, R. Cusani, G. Scarano
{"title":"Reconstruction of compressively sampled images using a nonlinear Bayesian prior","authors":"S. Colonnese, M. Biagi, R. Cusani, G. Scarano","doi":"10.1109/CAMSAP.2017.8313077","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313077","url":null,"abstract":"This paper presents a procedure for reconstruction of spatially localized images from compressively sampled measurements making use of Bayesian priors. The contribution of this paper is twofold: firstly, we analytically derive the expected value of wavelet domain signal structures conditional to a suitably defined noisy estimate; secondly, we exploit such conditional expectation within a nonlinear estimation stage that is added to an iterative reconstruction algorithm at a very low computational cost. We present numerical results focusing on spatially localized images and assessing the accuracy of the resulting algorithm, which definitely outperforms state-of-the-art competitors in very ill-posed conditions characterized by a low number of measurements. This contribution highlights the strong analogy between compressive sampling reconstruction and blind deconvolution, and paves the way to further work on joint design of image deconvolution/reconstruction from compressively sampled measurements.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"12 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":"121232173","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}
引用次数: 1
A fast model for solving the ECG forward problem based on an evolutionary algorithm 基于进化算法的心电正向问题快速求解模型
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313083
Karim El Houari, A. Kachenoura, L. Albera, S. Bensaid, A. Karfoul, Christelle Boichon-Grivot, M. Rochette, Alfredo I. Hernández
{"title":"A fast model for solving the ECG forward problem based on an evolutionary algorithm","authors":"Karim El Houari, A. Kachenoura, L. Albera, S. Bensaid, A. Karfoul, Christelle Boichon-Grivot, M. Rochette, Alfredo I. Hernández","doi":"10.1109/CAMSAP.2017.8313083","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313083","url":null,"abstract":"The estimation of solutions of the ElectroCardioGraphy (ECG) inverse problem is a key issue for non-invasive diagnosis and therapy of cardiac arrhythmia. A number of methods have been proposed to estimate such solutions, but their quantitative evaluation is not simple due to the lack of reference data. One way to proceed to their evaluation is to solve the forward problem, by generating simulations using a mathematical model representing the initiation and propagation of the cardiac electrical activity through the heart and torso. These models allow for the synthesis of torso ECG potentials corresponding to known, simulated cardiac potential mappings. However, most of the existing cardiac propagation models are too complex for this kind of application, with a significant number of parameters to be tuned, leading to high computational costs. In this paper, we propose a reliable and fast framework for building a cardiac and torso propagation model that generates sufficiently realistic healthy ECGs to perform reference-based evaluations of inverse problem methods. We used a set of tissue-level structures representing the cardiac electrical activity through FitzHugh-Nagumo model and a monodomain formalism for cardiac propagation. Low-resolution 3D Finite Element Method (FEM) representations are performed. An evolutionary algorithm is used to identify the main model parameters that provide the best fit to a real healthy ECG. The obtained preliminary results show that it is possible to generate realistic healthy ECGs using such simplified 3D heart torso models with very low computational costs.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"101 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":"121304553","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}
引用次数: 5
Tensor-Based compressed estimation of frequency-selective mmWave MIMO channels 基于张量的频率选择性毫米波MIMO信道压缩估计
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313186
D. C. Araújo, A. D. Almeida
{"title":"Tensor-Based compressed estimation of frequency-selective mmWave MIMO channels","authors":"D. C. Araújo, A. D. Almeida","doi":"10.1109/CAMSAP.2017.8313186","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313186","url":null,"abstract":"This paper develops a novel channel estimation technique for frequency-selective mmWave MIMO channels using a hybrid analog-digital architecture. By adopting a tensor formalism to model the effective channel, we link the channel estimation problem to the theory of multi-way compressive sensing of sparse tensors via Parallel Factors (PARAFAC) analysis. By leveraging on this link, a joint estimation of the compressed channel bases (spatial transmit, spatial receive and delay) can be obtained by means of an alternating least squares algorithm. Once these bases are estimated, the channel parameters are extracted by solving a simpler compressive sensing (CS) problem for each basis. Some useful bounds on the minimum number of beams and pilot sequence length can be derived from Kruskal's uniqueness conditions for sparse PARAFAC models. Remarkable channel estimation performance is obtained with short pilot sequences and very few beams, as shown in our simulation results.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"116 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":"115767363","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}
引用次数: 10
Soft extrapolation of bandlimited functions 带限函数的软外推
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313182
Dmitry Batenkov, L. Demanet
{"title":"Soft extrapolation of bandlimited functions","authors":"Dmitry Batenkov, L. Demanet","doi":"10.1109/CAMSAP.2017.8313182","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313182","url":null,"abstract":"Soft extrapolation refers to the problem of recovering a function from its samples multiplied by a fast-decaying window — in this note a narrow Gaussian. The question is akin to deconvolution, but leverages smoothness of the function in order to achieve stable recovery over an interval potentially larger than the essential support of the window. In case the function is bandlimited, we provide an error bound for extrapolation by a least-squares polynomial fit of a well-chosen degree: it is (morally) proportional to a fractional power of the perturbation level, which goes from 1 near the available samples, to 0 when the extrapolation distance reaches the characteristic smoothness length scale of the function. This bound is minimax in the sense that no algorithm can yield a meaningfully lower error over the same smoothness class. The result in this note can be put in the context of blind superresolution, where it corresponds to the limit of a single spike corrupted by a compactly-supported blur.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"59 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":"115294941","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}
引用次数: 1
Algorithms for the multi-sensor assignment problem in the δ-generalized labeled multi-Bernoulli filter δ-广义标记多伯努利滤波器中多传感器分配问题的算法
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313114
J. Yu, A. Saucan, M. Coates, M. Rabbat
{"title":"Algorithms for the multi-sensor assignment problem in the δ-generalized labeled multi-Bernoulli filter","authors":"J. Yu, A. Saucan, M. Coates, M. Rabbat","doi":"10.1109/CAMSAP.2017.8313114","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313114","url":null,"abstract":"Previous adaptations of the δ-generalized labeled multi-Bernoulli (δ-GLMB) filter to the multi-sensor case involve the sequential application of the update step for each sensor or Gibbs sampling for multi-sensor data association. The practical usage of the sequential δ-GLMB filter is limited due to the number of hypotheses growing with each additional sensor. Similarly, the Gibbs method requires a large number of samples for each hypothesis. In this paper, in the aim of finding the optimal or near-optimal multi-sensor assignments, we propose two novel methods, the combination and the cross entropy methods. Numerical simulations are conducted to evaluate the proposed multi-assignment methods together with the standard sequential processing method and a stochastic optimization algorithm based on Gibbs sampling. The combination method is able to significantly reduce running time with respect to the sequential method while yielding competitive performance across a wide range of test scenarios.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"24 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":"115454748","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}
引用次数: 2
One-Bit compressive sampling with time-varying thresholds for multiple sinusoids 多正弦波的时变阈值单比特压缩采样
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313172
Christopher Gianelli, Luzhou Xu, Jian Li, P. Stoica
{"title":"One-Bit compressive sampling with time-varying thresholds for multiple sinusoids","authors":"Christopher Gianelli, Luzhou Xu, Jian Li, P. Stoica","doi":"10.1109/CAMSAP.2017.8313172","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313172","url":null,"abstract":"Wide-band spectral sensing is a challenging task that will be required in future cognitive radio and radar applications. Recent research has shown that sampling using only one-bit of amplitude precision can be realized at an extremely high rate [1] in an affordable manner. In this work, one-bit sampling using time-varying thresholds is considered for line spectral estimation. The time-varying thresholds allow for amplitude estimation. A novel one-bit RELAX algorithm is developed for multi-tone parameter estimation. This algorithm is shown to have excellent performance via a numerical example.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 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":"131080729","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}
引用次数: 25
Adaptive noisy importance sampling for stochastic optimization 随机优化中的自适应噪声重要性抽样
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313215
Ö. D. Akyildiz, I. P. Mariño, J. Míguez
{"title":"Adaptive noisy importance sampling for stochastic optimization","authors":"Ö. D. Akyildiz, I. P. Mariño, J. Míguez","doi":"10.1109/CAMSAP.2017.8313215","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313215","url":null,"abstract":"In this work, we introduce an adaptive noisy importance sampler (ANIS) for optimization in an online setting. ANIS is an extension of the family of adaptive importance samplers where the weights are only approximate as they are computed via subsampling of the available data. Allowing errors in the weights enables us to use the algorithm in the so-called large-scale optimization setting, where the cost function consists of the sum of many component functions. ANIS can be used to optimize general cost functions as it does not need any gradient information to update the parameters. We show how the weights of ANIS are related to those of adaptive importance samplers and present some computer simulation results.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"43 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":"127217756","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}
引用次数: 5
Performance comparison of hybrid and digital beamforming with transmitter impairments 具有发射机损伤的混合波束形成与数字波束形成的性能比较
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313084
Kilian Roth, J. Nossek
{"title":"Performance comparison of hybrid and digital beamforming with transmitter impairments","authors":"Kilian Roth, J. Nossek","doi":"10.1109/CAMSAP.2017.8313084","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313084","url":null,"abstract":"Leveraging the available millimeter wave spectrum will be important for 5G. This work compares Hybrid Beam-Forming (HBF) and Digital BeamForming (DBF) with low resolution Analog-to-Digital-Converters, while considering a model for transmitter impairments, in terms of spectral efficiency and energy efficiency for a single-user and multi-user scenario with multipath propagation. We show that in the low Signal to Noise Ratio (SNR) regime, the performance of DBF even with 1–2 bits of resolution outperforms, that of HBF in terms of the spectral efficiency, for both the single-user and multi-user case. Already 3–4 bit effective resolution DBF outperforms HBF for the whole considered SNR range from −30 to 30 dB in terms of spectral efficiency and energy efficiency. For a asymmetric receive power multi-user scenario, this effective resolution is also sufficient to cover receive power differences of 20 dB.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"383 2 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":"114363335","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}
引用次数: 2
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