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2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)最新文献

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An improved RIP-based performance guarantee for sparse signal reconstruction via subspace pursuit 一种改进的基于rip的子空间追踪稀疏信号重构性能保证
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882428
Ling-Hua Chang, Jwo-Yuh Wu
{"title":"An improved RIP-based performance guarantee for sparse signal reconstruction via subspace pursuit","authors":"Ling-Hua Chang, Jwo-Yuh Wu","doi":"10.1109/SAM.2014.6882428","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882428","url":null,"abstract":"Subspace pursuit (SP) is a well-known greedy algorithm capable of reconstructing a sparse signal vector from a set of incomplete measurements. In this paper, by exploiting an approximate orthogonality condition characterized in terms of the achievable angles between two compressed orthogonal sparse vectors, we show that perfect signal recovery in the noiseless case, as well as stable signal recovery in the noisy case, is guaranteed if the sensing matrix satisfies RIP of order 3K with RIC δ3K ≤ 0.2412 . Our work improves the best-known existing results, namely, δ3K <; 0.165 for the noiseless case [3] and δ3K <; 0.139 when noise is present [4]. In addition, for the noisy case we derive a reconstruction error upper bound, which is shown to be smaller as compared to the bound reported in [4].","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129539199","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
Strategies for principal component analysis in wireless sensor networks 无线传感器网络中的主成分分析策略
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882383
Nisrine Ghadban, P. Honeine, C. Francis, F. Mourad, J. Farah
{"title":"Strategies for principal component analysis in wireless sensor networks","authors":"Nisrine Ghadban, P. Honeine, C. Francis, F. Mourad, J. Farah","doi":"10.1109/SAM.2014.6882383","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882383","url":null,"abstract":"This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396022","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
Tracking simplified shapes using a stochastic boundary 使用随机边界跟踪简化形状
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882380
Antonio Zea, F. Faion, M. Baum, U. Hanebeck
{"title":"Tracking simplified shapes using a stochastic boundary","authors":"Antonio Zea, F. Faion, M. Baum, U. Hanebeck","doi":"10.1109/SAM.2014.6882380","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882380","url":null,"abstract":"When tracking extended objects, it is often the case that the shape of the target cannot be fully observed due to issues of visibility, artifacts, or high noise, which can change with time. In these situations, it is a common approach to model targets as simpler shapes instead, such as ellipsoids or cylinders. However, these simplifications cause information loss from the original shape, which could be used to improve the estimation results. In this paper, we propose a way to recover information from these lost details in the form of a stochastic boundary, whose parameters can be dynamically estimated from received measurements. The benefits of this approach are evaluated by tracking an object using noisy, real-life RGBD data.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124804713","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
MMOSPA-based direction-of-arrival estimation for planar antenna arrays 基于mmospa的平面天线阵到达方向估计
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882377
M. Baum, P. Willett, U. Hanebeck
{"title":"MMOSPA-based direction-of-arrival estimation for planar antenna arrays","authors":"M. Baum, P. Willett, U. Hanebeck","doi":"10.1109/SAM.2014.6882377","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882377","url":null,"abstract":"This work is concerned with direction-of-arrival (DOA) estimation of narrowband signals from multiple targets using a planar antenna array. We illustrate the shortcomings of Maximum Likelihood (ML), Maximum a Posteriori (MAP), and Minimum Mean Squared Error (MMSE) estimation, issues that can be attributed to the symmetry in the likelihood function that must exist when there is no information about labeling of targets. We proffer the recently introduced concept of Minimum Mean OSPA (MMOSPA) estimation that is based on the optimal sub-pattern assignment (OSPA) metric for sets and hence inherently incorporates symmetric likelihood functions.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121568839","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
Robust parametric detection in non homogeneous clutter 非均匀杂波鲁棒参数检测
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882445
Toufik Boukaba, A. Zoubir, D. Berkani
{"title":"Robust parametric detection in non homogeneous clutter","authors":"Toufik Boukaba, A. Zoubir, D. Berkani","doi":"10.1109/SAM.2014.6882445","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882445","url":null,"abstract":"In this paper we address the problem of robust adaptive parametric radar detection in non-homogeneous clutter. Interfering targets and clutter edges in secondary cells are regarded as outliers. Classical estimators of parametric models are not sufficiently robust in such situations. We consider stationary segments, where the clutter is modelled as an autoregressive process, we apply a robust filter and we estimate the autoregressive model to construct the parametric detector.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115864740","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
Scalar estimation from unreliable binary observations 根据不可靠的二元观测值进行标量估计
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882361
R. Corey, A. Singer
{"title":"Scalar estimation from unreliable binary observations","authors":"R. Corey, A. Singer","doi":"10.1109/SAM.2014.6882361","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882361","url":null,"abstract":"We consider a scalar signal estimator based on unreliable observations. The proposed architecture forms an estimate using redundant arrays of unreliable binary sensors with detection thresholds that can vary randomly about their nominal values. We analyze the achievable performance of the estimator in terms of mean square error. We also provide approximate expressions for the error of a mean square optimal estimator in terms of the degree of redundancy in the system and the distribution of the random thresholds. We show that calibration and redundancy can compensate for uncertainty in the observations to form a reliable estimate.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443217","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
Near-field source localization: Sparse recovery techniques and grid matching 近场源定位:稀疏恢复技术和网格匹配
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882418
K. Hu, S. P. Chepuri, G. Leus
{"title":"Near-field source localization: Sparse recovery techniques and grid matching","authors":"K. Hu, S. P. Chepuri, G. Leus","doi":"10.1109/SAM.2014.6882418","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882418","url":null,"abstract":"Near-field source localization is a joint direction-of-arrival (DOA) and range estimation problem. Leveraging the sparsity of the spatial spectrum, and gridding along the DOA and range domain, the near-field source localization problem can be casted as a linear sparse regression problem. However, this would result in a very large dictionary. Using the Fresnel-approximation, the DOA and range naturally decouple in the correlation domain. This allows us to solve two inverse problems of a smaller dimension instead of one higher dimensional problem. Furthermore, the sources need not be exactly on the predefined sampling grid. We use a mismatch model to cope with such off-grid sources and present estimators for grid matching.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131927197","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: 22
Directions of arrival estimation by learning sparse dictionaries for sparse spatial spectra 基于学习稀疏字典的稀疏空间光谱到达方向估计
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882420
Cheng-Yu Hung, Jimeng Zheng, M. Kaveh
{"title":"Directions of arrival estimation by learning sparse dictionaries for sparse spatial spectra","authors":"Cheng-Yu Hung, Jimeng Zheng, M. Kaveh","doi":"10.1109/SAM.2014.6882420","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882420","url":null,"abstract":"A major limitation of most methods exploiting sparse signal or spectral models for the purpose of estimating directions-of-arrival stems from the fixed model dictionary that is formed by array response vectors over a discrete search grid of possible directions. In general, the array responses to actual DoAs will most likely not be members of such a dictionary. In this work, the sparse spectral signal model with uncertainty of linearized dictionary parameter mismatch is considered, and the dictionary matrix is reformulated into a multiplication of a fixed base dictionary and a sparse matrix. Based on this double-sparsity model, an alternating dictionary learning-sparse spectral model fitting approach is proposed to reduce the estimation errors of DoAs and their powers. Group-sparsity estimator and Lasso-based Least Squares are utilized in the formulation of the associated optimization problem. The performance of the proposed methods are demonstrated by numerical simulations.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123730940","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
Interference mitigation in GNSS receivers by array signal processing: A software radio approach 阵列信号处理在GNSS接收机中的干扰缓解:一种软件无线电方法
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882355
J. Arribas, P. Closas, C. Fernández-Prades
{"title":"Interference mitigation in GNSS receivers by array signal processing: A software radio approach","authors":"J. Arribas, P. Closas, C. Fernández-Prades","doi":"10.1109/SAM.2014.6882355","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882355","url":null,"abstract":"The fact that most of high-accuracy positioning and distributed timing services, including safety-critical operations, rely on Global Navigation Satellite Systems (GNSS) has raised the concern of possible denial-of-service situations. Complementary to time- and frequency-domain mitigation techniques, it is well known that antenna-array based receivers can benefit from spatial domain processing and effectively mitigate unintentional and intentional Radio Frequency Interferences (RFIs). In this work, we propose a software-based GNSS receiver architecture that implements a real-time, array-based signal acquisition algorithm based on the Generalized Likelihood Ratio Test, combined with a null-steering spatial filter for signal tracking, showing its RFI rejection capabilities.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125224226","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
Sparse delay-Doppler image reconstruction under off-grid problem 离网条件下稀疏延迟多普勒图像重建
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2014-06-22 DOI: 10.1109/SAM.2014.6882429
Oguzhan Teke, A. Gürbüz, O. Arikan
{"title":"Sparse delay-Doppler image reconstruction under off-grid problem","authors":"Oguzhan Teke, A. Gürbüz, O. Arikan","doi":"10.1109/SAM.2014.6882429","DOIUrl":"https://doi.org/10.1109/SAM.2014.6882429","url":null,"abstract":"Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay-Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay-Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel and OMP based sparse reconstruction technique with parameter perturbation, named as PPOMP, is proposed for robust delay-Doppler radar processing even under the off-grid case. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. A new performance metric based on Kull-back-Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces.","PeriodicalId":141678,"journal":{"name":"2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117343430","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
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