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2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)最新文献

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Sparsity-driven radar auto-focus imaging under over-wavelength position perturbations 超波长位置扰动下稀疏驱动雷达自动聚焦成像
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569657
Dehong Liu
{"title":"Sparsity-driven radar auto-focus imaging under over-wavelength position perturbations","authors":"Dehong Liu","doi":"10.1109/SAM.2016.7569657","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569657","url":null,"abstract":"We consider a 2D imaging problem where a perturbed mono-static radar is used to detect localized targets situated in a region of interest. In order to deal with position-induced out-of-focus, we proposed a sparsity-driven auto-focus imaging approach in which each radar measurement is modeled as a superposition of weighted and delayed target signatures scattered from the corresponding target phase centers. We iteratively exploit the position-related delays and the target signatures by analyzing data coherence, and consequently form an adaptive projection matrix of the radar measurements. By imposing sparsity on the scattering weights, a sparse image and a dense image, without and with the target signatures respectively, are reconstructed. Compared to existing auto-focus methods, our approach significantly improves radar focus performance in imaging localized targets, even under position perturbations up to 10 wavelengths of the radar central frequency. We validate our algorithm with simulated noisy data.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121450442","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
Performance bounds for coupled models 耦合模型的性能边界
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569651
Chengfang Ren, Rodrigo Cabral Farias, P. Amblard, P. Comon
{"title":"Performance bounds for coupled models","authors":"Chengfang Ren, Rodrigo Cabral Farias, P. Amblard, P. Comon","doi":"10.1109/SAM.2016.7569651","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569651","url":null,"abstract":"Two models are called “coupled” when a non empty set of the underlying parameters are related through a differentiable implicit function. The goal is to estimate the parameters of both models by merging all datasets, that is, by processing them jointly. In this context, we show that the parameter estimation accuracy under a general class of dataset distributions always improves when compared to an equivalent uncoupled model. We eventually illustrate our results with the fusion of multiple tensor data.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127967016","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: 7
A method to solve the permutation problem in blind source deconvolution for audio signals based on phase linearity estimation 一种基于相位线性估计的音频信号盲源反卷积中置换问题的解决方法
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569643
Hidekazu Fukai
{"title":"A method to solve the permutation problem in blind source deconvolution for audio signals based on phase linearity estimation","authors":"Hidekazu Fukai","doi":"10.1109/SAM.2016.7569643","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569643","url":null,"abstract":"One approach to solve the blind source deconvolution (BSD) is to transform the observations into the frequency domain and apply common blind source separation (BSS) in each frequency bin. This approach is called frequency-domain blind source separation (FD-BSS). Generally FD-BSS has a problem with indeterminacy of the permutation of the separated signals in each frequency bin. Furthermore, even if the permutation problem is solved, we cannot avoid the degradation of quality of the estimated signals because of noise or statistical error. In this paper, we describe a new approach for BSS that utilizes the phase linearity not only to solve the permutation problem but also to tune each value of the elements of the separating matrices. To effectively detect multi- and ambiguous linearity, we propose the use of the Hough transform. To improve the signal-to-noise ratio (SNR), we propose not to persist in the independence, but to adopt the constraints of phase linearity. Simulation results for audio sources show the improvement of SNR with the proposed method.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036118","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
Finite sample analysis of covariance compression using structured samplers 用结构化采样器进行协方差压缩的有限样本分析
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569732
Heng Qiao, P. Pal
{"title":"Finite sample analysis of covariance compression using structured samplers","authors":"Heng Qiao, P. Pal","doi":"10.1109/SAM.2016.7569732","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569732","url":null,"abstract":"This paper considers the problem of compressively sampling wide sense stationary random vectors with low rank Toeplitz structured covariance matrix. Using the celebrated Caratheodory's theorem, Toeplitz structured covariance matrix recovery can be cast as line spectrum estimation problem. In this paper, we utilize this connection to establish theoretical guarantees under which low rank Toeplitz covariance matrices can be compressively sketched and reconstructed from a finite number of compressed samples. Using a newly proposed structured sampler, namely the Generalized Nested Sampler (GNS), we show that stable estimation of original N × N Toeplitz covariance matrix of rank r can be obtained from a compressed sketch of size O(√r) × O(√r) using an atomic norm minimization framework.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132717391","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
Modified maximum likelihood estimator 改进的极大似然估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569748
David C. de Souza, C. Cavalcante, R. Vigelis
{"title":"Modified maximum likelihood estimator","authors":"David C. de Souza, C. Cavalcante, R. Vigelis","doi":"10.1109/SAM.2016.7569748","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569748","url":null,"abstract":"In this paper, we present a modified maximum likelihood estimation method, which is suitable to be used with φ-families rather than exponential families. An indicative result of the efficacy of this method is established. We perform numerical experiments to illustrate the accuracy of this method for estimating the dispersion parameter σ in φ-Gaussians.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"105 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133000876","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
Adaptive self-interference cancelation in LTE-A carrier aggregation FDD direct-conversion transceivers LTE-A载波聚合FDD直接转换收发器中的自适应自干扰消除
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569758
A. Gebhard, R. S. Kanumalli, B. Neurauter, M. Huemer
{"title":"Adaptive self-interference cancelation in LTE-A carrier aggregation FDD direct-conversion transceivers","authors":"A. Gebhard, R. S. Kanumalli, B. Neurauter, M. Huemer","doi":"10.1109/SAM.2016.7569758","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569758","url":null,"abstract":"Modern frequency division duplex radio frequency transceivers experience transmitter-to-receiver leakage due to the limited isolation of the duplexer. In Long Term Evolution-Advanced (LTE-A) carrier aggregation receivers the coupling between the local oscillators creates harmonics on the chip which can lead to the downconversion of this leakage signal to the receive (Rx) baseband. Thereby, this so-called modulated spur interference reduces the signal-to-noise ratio of the Rx signal. In this paper, the modulated spur interference is modeled and several adaptive algorithms are compared regarding their convergence and cancelation performance. To maximize the data throughput, the adaptive filter is required to converge within the short time period of one orthogonal frequency-division multiplexing (OFDM) symbol. Out of the investigated concepts the proposed variable step-size least-mean-square algorithm turns out to be the most favorable choice. It satisfies the required constraints of convergence time and cancelation performance, and it features a reasonable low complexity.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125623390","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: 20
On one-bit quantized ZF precoding for the multiuser massive MIMO downlink 多用户海量MIMO下行链路的1位量化ZF预编码
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569670
A. K. Saxena, I. Fijalkow, A. L. Swindlehurst
{"title":"On one-bit quantized ZF precoding for the multiuser massive MIMO downlink","authors":"A. K. Saxena, I. Fijalkow, A. L. Swindlehurst","doi":"10.1109/SAM.2016.7569670","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569670","url":null,"abstract":"We study low complexity precoding for a downlink massive MIMO multiuser system assuming a base station that employs one-bit digital-to-analog converters (DACs) in order to mitigate power usage. The use of one-bit DACs is equivalent to constraining the transmit signal to be drawn from a QPSK alphabet. While the precoding problem can be formulated using a standard maximum likelihood (ML) encoder, the implementation cost is prohibitive for massive numbers of antennas, even if a sphere encoding approach is used. Instead, we study the performance of a one-bit quantized zero-forcing precoder, and we show that it asymptotically provides the desired downlink vector with low complexity. Simulations show that the quantized ZF precoder can actually outperform the ML encoder for low to moderate signal-to-noise ratios.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129147560","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: 44
Performance analysis of a persymmetric adaptive matched filter 一种超对称自适应匹配滤波器性能分析
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569617
Jun Liu, Hongbin Li, B. Himed
{"title":"Performance analysis of a persymmetric adaptive matched filter","authors":"Jun Liu, Hongbin Li, B. Himed","doi":"10.1109/SAM.2016.7569617","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569617","url":null,"abstract":"We examine the adaptive detection problem in the presence of colored noise with an unknown covariance matrix, by exploiting a persymmetric structure in the received signal. The persymmetric adaptive matched filter (PS-AMF) is used to address this problem, which can significantly alleviate the requirement of secondary data. In this paper, finite-sum expressions for the probability of false alarm of the PS-AMF are derived, which are more convenient to use in calculating the detection threshold. Moreover, the detection probabilities of the PS-AMF are derived. These theoretical results are all confirmed using Monte Carlo simulations.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128602027","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
Low-rank robust adaptive beamforming techniques using joint iterative optimization 基于联合迭代优化的低秩鲁棒自适应波束形成技术
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569614
H. Ruan, R. D. Lamare
{"title":"Low-rank robust adaptive beamforming techniques using joint iterative optimization","authors":"H. Ruan, R. D. Lamare","doi":"10.1109/SAM.2016.7569614","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569614","url":null,"abstract":"This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. At first, we introduce an orthogonal Krylov subspace projection mismatch estimation (OKSPME) method, in which a general linear equation is considered in large dimensions which aims to solve for the steering vector mismatch with known information, then we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace. An adaptive algorithm based on stochastic gradient and joint iterative optimization (JIO) dimensionality reduction technique is devised for beamforming large sensor arrays with low complexity. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) among all the compared RAB methods.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116845178","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
Joint analysis of multiple datasets by cross-cumulant tensor (block) diagonalization 交叉累积张量(块)对角化的多数据集联合分析
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569736
D. Lahat, C. Jutten
{"title":"Joint analysis of multiple datasets by cross-cumulant tensor (block) diagonalization","authors":"D. Lahat, C. Jutten","doi":"10.1109/SAM.2016.7569736","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569736","url":null,"abstract":"In this paper, we propose approximate diagonalization of a cross-cumulant tensor as a means to achieve independent component analysis (ICA) in several linked datasets. This approach generalizes existing cumulant-based independent vector analysis (IVA). It leads to uniqueness, identifiability and resilience to noise that exceed those in the literature, in certain scenarios. The proposed method can achieve blind identification of underdetermined mixtures when single-dataset cumulant-based methods that use the same order of statistics fall short. In addition, it is possible to analyse more than two datasets in a single tensor factorization. The proposed approach readily extends to independent subspace analysis (ISA), by tensor block-diagonalization. The proposed approach can be used as-is or as an ingredient in various data fusion frameworks, using coupled decompositions. The core idea can be used to generalize existing ICA methods from one dataset to an ensemble.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115618780","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: 4
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