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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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Energy-Efficient distributed amplify-and-forward beamforming for wireless sensor networks 无线传感器网络的高能效分布式放大前向波束形成
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313086
S. Zaidi, Oussama Ben Smida, S. Affes, S. Valaee
{"title":"Energy-Efficient distributed amplify-and-forward beamforming for wireless sensor networks","authors":"S. Zaidi, Oussama Ben Smida, S. Affes, S. Valaee","doi":"10.1109/CAMSAP.2017.8313086","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313086","url":null,"abstract":"In this paper, we consider an amplify-and-forward (AF) beamformer that achieves a dual-hop communication from a source to a receiver in highly-scattered environments, through a wireless sensor network (WSN) comprised of K independent and autonomous nodes. The AF beamforming weights are derived to maximize the received signal-to-noise ratio (SNR) subject to a constraint over the nodes' total transmit power. We verify that the so-obtained SNR-optimal AF beamformer (OB) implementation requires from the small-battery powered WSN nodes a prohibitive power cost. Exploiting the polychromatic structure of scattered channels, we develop a novel polychromatic (i.e., multi-ray) distributed AF beamformer (P-DB) that performs nearly as well as OB while requiring much less power consumption at each node. Furthermore, we prove that the proposed P-DB always outperforms two other benchmarks: the monochromatic (i.e., single-ray) DB (M-DB) which neglects scattering, and the bichromatic (i.e., two-ray) DB (B-DB) which relies on an efficient polychromatic channel approximation by two rays that is valid for small angular spreads (AS).","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"38 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":"121825795","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
Design of optimum sparse array for robust MVDR beamforming against DOA mismatch 抗DOA失配的MVDR波束形成优化稀疏阵列设计
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313065
Xiangrong Wang, M. Amin
{"title":"Design of optimum sparse array for robust MVDR beamforming against DOA mismatch","authors":"Xiangrong Wang, M. Amin","doi":"10.1109/CAMSAP.2017.8313065","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313065","url":null,"abstract":"The adaptive beamforming performance using a given number of antennas can be improved by configuring an optimum sparse array. Existing sparse array designing methods for adaptive beamforming assume prior knowledge of exact source directions-of-arrival (DOAs), which may not be applicable in navigation or other sensing situations. The sensitivity of sparse array configurations against uncertainty of source signal DOAs is examined. We seek the optimum sparse array that is robust to the source angular bias, enabling the output signal-to-noise ratio or signal-to-interference-plus-noise ratio to be minimally degraded. Numerical examples are presented to validate the effectiveness of the configured robust sparse arrays in the presence of arbitrary DOA uncertainties.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"256 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":"121883040","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: 9
Gaussian sum particle flow filter 高斯和粒子流滤波器
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313189
Soumyasundar Pal, M. Coates
{"title":"Gaussian sum particle flow filter","authors":"Soumyasundar Pal, M. Coates","doi":"10.1109/CAMSAP.2017.8313189","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313189","url":null,"abstract":"Particle flow filters provide an approach for state estimation in nonlinear systems. They can outperform many particle filter implementations when the state dimension is high or when the measurements are highly informative. Instead of employing importance sampling, the particles are migrated by numerically solving differential equations that describe a flow from the prior to the posterior at each time step. An analytical solution for the flow equation requires a Gaussian assumption for both the prior and the posterior. Recently Khan et al. [1] devised an approximate flow that could address the case when the prior is represented by a Gaussian Mixture Model (GMM) and the likelihood function is Gaussian. The solution involved inversion of a large matrix which made the computational requirements scale poorly with the state dimension. In this paper, we devise an approximate particle flow filter for the case when both the prior and the likelihood are modeled using Gaussian mixtures. We perform numerical experiments to explore when the proposed method offers advantages compared to existing techniques.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"41 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":"122076551","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: 8
Illuminator of opportunity selection for passive radar 无源雷达机会选择照明器
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313066
Yang Li, Qian He, Rick S. Blum
{"title":"Illuminator of opportunity selection for passive radar","authors":"Yang Li, Qian He, Rick S. Blum","doi":"10.1109/CAMSAP.2017.8313066","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313066","url":null,"abstract":"Passive radar can obtain performance benefits by using multiple receivers and multiple illuminators of opportunity (IOO). However, employing a large number of IOOs is costly in terms of hardware. Thus, it is sometimes necessary to limit the total number of selected IOOs. The IOO selection scheme for maximizing target detection performance is studied in this paper, under the assumption that the number of IOOs that can be selected at each receiver is limited. An IOO selection algorithm based on maximizing the Kullback-Leibler (KL) distance is presented, which requires much lower computational complexity compared with the exhaustive search method and is shown to lead to a detection performance that is close enough to that of the optimal selection.","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":"129849356","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
Multi-Target localization in asynchronous MIMO radars using sparse sensing 基于稀疏感知的异步MIMO雷达多目标定位
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313198
S. Sedighi, R. B. S. Mysore, S. Maleki, B. Ottersten
{"title":"Multi-Target localization in asynchronous MIMO radars using sparse sensing","authors":"S. Sedighi, R. B. S. Mysore, S. Maleki, B. Ottersten","doi":"10.1109/CAMSAP.2017.8313198","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313198","url":null,"abstract":"Multi-target localization, warranted in emerging applications like autonomous driving, requires targets to be perfectly detected in the distributed nodes with accurate range measurements. This implies that high range resolution is crucial in distributed localization in the considered scenario. This work proposes a new framework for multi-target localization, addressing the demand for the high range resolution in automotive applications without increasing the required bandwidth. In particular, it employs sparse stepped frequency waveform and infers the target ranges by exploiting sparsity in target scene. The range measurements are then sent to a fusion center where direction of arrival estimation is undertaken. Numerical results illustrate the impact of range resolution on multi-target localization and the performance improvement arising from the proposed algorithm in such scenarios.","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":"125537406","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: 0
Memory efficient low-rank non-linear subspace tracking 内存高效低秩非线性子空间跟踪
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313099
Fatemeh Sheikholeslami, Dimitris Berberidis, G. Giannakis
{"title":"Memory efficient low-rank non-linear subspace tracking","authors":"Fatemeh Sheikholeslami, Dimitris Berberidis, G. Giannakis","doi":"10.1109/CAMSAP.2017.8313099","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313099","url":null,"abstract":"The task of low-rank subspace tracking is of paramount importance for feature extraction over streaming data. Considering the broad range of applications in which the data fail to adhere to a linear model, the present work proposes a nonlinear subspace tracking algorithm. The proposed algorithm can effectively learn and track an evolving non-linear subspace in an online fashion. The notion of non-linearity is accommodated via exploitation of kernel-induced mappings, whose computational as well as memory requirements, if untreated, will impose scalability issues in large datasets. This issue is addressed by imposing a predefined affordable budget on the number of data vectors to be stored, preventing computational and memory growth of the algorithm, while enabling the tracking of possibly evolving subspaces. Numerical tests corroborate the effectiveness of the proposed algorithm on synthetic as well as real datasets.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"74 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":"116338293","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
Learning-Based pilot precoding and combining for wideband millimeter-wave networks 基于学习的宽带毫米波网络导频预编码与合并
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313146
E. Olfat, H. S. Ghadikolaei, N. N. Moghadam, M. Bengtsson, C. Fischione
{"title":"Learning-Based pilot precoding and combining for wideband millimeter-wave networks","authors":"E. Olfat, H. S. Ghadikolaei, N. N. Moghadam, M. Bengtsson, C. Fischione","doi":"10.1109/CAMSAP.2017.8313146","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313146","url":null,"abstract":"This paper proposes an efficient channel estimation scheme with a minimum number of pilots for a frequency-selective millimeter-wave communication system. We model the dynamics of the channel's second-order statistics by a Markov process and develop a learning framework that finds the optimal precoding and combining vectors for pilot signals, given the channel dynamics. Using these vectors, the transmitter and receiver will sequentially estimate the corresponding angles of departure and arrival, and then refine the pilot precoding and combining vectors to minimize the error of estimating the small-scale fading of all subcarriers. Numerical results demonstrate near-optimality of our approach, compared to the oracle wherein the second-order statistics (not the dynamics) are perfectly known a priori.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"81 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":"125908141","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: 3
Fast and accurate radio interferometric imaging using krylov subspaces 快速和准确的无线电干涉成像使用克雷洛夫子空间
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313147
S. Naghibzadeh, A. V. D. Veen
{"title":"Fast and accurate radio interferometric imaging using krylov subspaces","authors":"S. Naghibzadeh, A. V. D. Veen","doi":"10.1109/CAMSAP.2017.8313147","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313147","url":null,"abstract":"We propose a fast iterative method for image formation in Radio Astronomy (RA). We formulate the image formation problem as a maximum likelihood estimation problem to estimate the image pixel powers via array covariance measurements. We use an iterative solution method based on projections onto Krylov subspaces and exploit the sample covariance error estimate via discrepancy principle as the stopping criterion. We propose to regularize the ill-posed imaging problem based on a Bayesian framework using MVDR beamformed data applied as a right preconditioner to the system matrix. We compare the proposed method with the state-of-the-art sparse sensing methods and show that the proposed method obtains comparably accurate solutions with a significant reduction in computation.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"2007 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":"125977357","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 particle-based approach for topology estimation of gene networks 基于粒子的基因网络拓扑估计方法
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313217
C. Tasdemir, M. Bugallo, P. Djurić
{"title":"A particle-based approach for topology estimation of gene networks","authors":"C. Tasdemir, M. Bugallo, P. Djurić","doi":"10.1109/CAMSAP.2017.8313217","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313217","url":null,"abstract":"In this paper, an iterative particle-based method is proposed for topology estimation of gene networks. Using a particle filter for each gene expression, the connections among genes and the gene expressions are modeled by random measures. The probabilities of the possible topologies are computed using only estimates of gene expressions which allow for proposals of new topologies in an iterative manner. The resampling step of particle filtering eliminates the topologies with smaller weights and improves the results. The algorithm is compared with the Least Absolute Shrinkage and Selection Operator. The simulation results of the proposed method show better performance in capturing the interactions among genes.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"48 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":"130880844","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: 3
Compressed power spectrum, carrier and DOA estimation via PARAFAC decomposition 压缩功率谱，通过PARAFAC分解估计载波和DOA
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313106
Jun Fang, Feiyu Wang, Hongbin Li
{"title":"Compressed power spectrum, carrier and DOA estimation via PARAFAC decomposition","authors":"Jun Fang, Feiyu Wang, Hongbin Li","doi":"10.1109/CAMSAP.2017.8313106","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313106","url":null,"abstract":"This paper considers the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. A new phased-array based sub-Nyquist receiver architecture, which requires only a single delay channel for each sensor output and thus can be implemented simply and efficiently, is proposed. We proposed a CANDECOMP/PARAFAC (CP) decomposition-based method for joint wideband spectrum sensing and DOA estimation through exploiting the cross-correlations between different sensor outputs. Simulation results under different numbers of antennas and signal to noise ratios (SNR) are provided to corroborate our proposed algorithm.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"20 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":"131858146","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: 3
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