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Linear Distributed Algorithms For Localization In Mobile Networks 移动网络中线性分布的定位算法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450766
S. Safavi, U. Khan, S. Kar, José M. F. Moura
{"title":"Linear Distributed Algorithms For Localization In Mobile Networks","authors":"S. Safavi, U. Khan, S. Kar, José M. F. Moura","doi":"10.1109/SSP.2018.8450766","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450766","url":null,"abstract":"This paper studies the problem of distributed self-localization in noisy networks of mobile agents. Agent mobility is captured by means of a stochastic motion model and the goal of each agent is to dynamically track its (location) state using noisy inter-agent relative distance measurements and communication with a subset of neighboring agents. The Bayesian tracking formulation thus obtained is highly non-standard, in that the distance measurements relate to the location in a non-linear way; and in a mobile setting, it is not clear how connectivity can be maintained for the localization process to provide unambiguous location results. To make the collaborative filtering problem tractable, the paper first presents a barycentric-coordinate based reparametrization of the state-space model; the transformed formulation leads to a bilinear state-space. Under mild network connectivity assumptions, specifically, the inter-agent communication network stays connected on an average, and a structural convexity condition, specifically, infinitely often the agents lie in the convex hull of a set of $m+1$ neighboring agents, where m denotes the dimension of the space, a distributed filtering scheme is proposed that enables each agent to track its location with bounded mean-squared error as long as there is at least one anchor in the network (agent with known location). Simulations are presented to illustrate the efficacy of the proposed distributed filtering procedure and the theoretical results.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114458924","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
A Multitaper Test For The Detection of Non-Stationary Processes Using Canonical Correlation Analysis 用典型相关分析检测非平稳过程的多锥度检验
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450806
F. A. Marshall, G. Takahara, D. Thomson
{"title":"A Multitaper Test For The Detection of Non-Stationary Processes Using Canonical Correlation Analysis","authors":"F. A. Marshall, G. Takahara, D. Thomson","doi":"10.1109/SSP.2018.8450806","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450806","url":null,"abstract":"A new test has been designed for detecting the presence of non-stationary component processes in a time series. The test statistic is derived from the canonical correlations between two sets of eigencoefficients which are offset in frequency. The correlation coefficient which defines the test statistic has lower estimation variance than the multitaper, linear spectral-correlation coefficient, and it reveals important evidence of non-stationarity which is missed in the detector of the latter correlation coefficient.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122180116","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
An Efficient Greedy Algorithm for finding the Nearest Simultaneous Diagonalizable Family 寻找最近的可同时对角化族的一种高效贪婪算法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450802
Riku Akema, M. Yamagishi, I. Yamada
{"title":"An Efficient Greedy Algorithm for finding the Nearest Simultaneous Diagonalizable Family","authors":"Riku Akema, M. Yamagishi, I. Yamada","doi":"10.1109/SSP.2018.8450802","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450802","url":null,"abstract":"Diagonalization of given multiple squared matrices by a common similarity transformation is called Simultaneous Diagonalization (SD). The approximate SD problem for finding numerically a similarity matrix which diagonalizes approximately given multiple matrices has been a long standing challenge mainly due to its nonconvexity. In this paper, we propose a new efficient greedy algorithm for finding the nearest simultaneous diagonalizable family from given matrices, and extend an elegant SD approach named the DODO algorithm to the approximate SD problem by using the proposed algorithm as its preprocessing. Numerical experiments show that the DODO algorithm preprocessed the proposed algorithm achieves more accurate estimations of solutions of the approximate SD problem than the existing ones.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115156342","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 Smoothing Stochastic Phase Retrieval Algorithm for Solving Random Quadratic Systems 一种求解随机二次系统的平滑随机相位检索算法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450729
Samuel Pinilla, Jorge Bacca, J. Tourneret, H. Arguello
{"title":"A Smoothing Stochastic Phase Retrieval Algorithm for Solving Random Quadratic Systems","authors":"Samuel Pinilla, Jorge Bacca, J. Tourneret, H. Arguello","doi":"10.1109/SSP.2018.8450729","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450729","url":null,"abstract":"A novel Stochastic Smoothing Phase Retrieval (SSPR) algorithm is studied to reconstruct an unknown signal x ∈ ℝn or ${{mathbb{C}}^n}$ from a set of absolute square projections yk = |⟨ak; x⟩|2. This inverse problem is known in the literature as Phase Retrieval (PR). Recent works have shown that the PR problem can be solved by optimizing a nonconvex and non-smooth cost function. Contrary to the recent truncated gradient descend methods developed to solve the PR problem (using truncation parameters to bypass the non-smoothness of the cost function), the proposed algorithm approximates the cost function of interest by a smooth function. Optimizing this smooth function involves a single equation per iteration, which leads to a simple scalable and fast method especially for large sample sizes. Extensive simulations suggest that SSPR requires a reduced number of measurements for recovering the signal x, when compared to recently developed stochastic algorithms. Our experiments also demonstrate that SSPR is robust to the presence of additive noise and has a speed of convergence comparable with that of state-of-the-art algorithms.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122391278","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
A Low-Complexity Sub-Nyquist Blind Signal Detection Algorithm For Cognitive Radio 认知无线电中一种低复杂度亚奈奎斯特盲信号检测算法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450733
Kai Cao, Peizhong Lu
{"title":"A Low-Complexity Sub-Nyquist Blind Signal Detection Algorithm For Cognitive Radio","authors":"Kai Cao, Peizhong Lu","doi":"10.1109/SSP.2018.8450733","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450733","url":null,"abstract":"The detection of sparse wideband signal in the sub-Nyquist regime is considered in this paper. We present a low-complexity and robust multiband signal detection algorithm based on algebraic analysis and statistical methods. The original signal is subsampled with Multi-coset sampling. We find that there are some linear constraints between the nonzero spectrum locations. The linear relationship is described by a frequency locator polynomial. The detector does not require priori knowledge about the frequency locations of the signals of interest. Moreover, we show that our method has lower complexity of both samples and computation compared with cyclostationary detection (CD) in the sparse case. Numerical results demonstrate our detector outperforms energy detection (ED) in the sub-Nyquist regime especially in low signal to noise ratio (SNR).","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267266","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
A Comparison Of Clipping Strategies For Importance Sampling 重要性抽样的裁剪策略比较
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450722
Luca Martino, V. Elvira, J. Míguez, Antonio Artés-Rodríguez, P. Djurić
{"title":"A Comparison Of Clipping Strategies For Importance Sampling","authors":"Luca Martino, V. Elvira, J. Míguez, Antonio Artés-Rodríguez, P. Djurić","doi":"10.1109/SSP.2018.8450722","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450722","url":null,"abstract":"Importance Sampling (IS) methods approximate a targeted distribution with a set of weighted samples, drawn from a proposal distribution. Unfortunately, a mismatch between the proposal and the targeted distribution may endanger the performance of the estimators. In this paper, we focus on the so-called nonlinear IS (NIS) framework, where a nonlinear function is applied to the standard importance weights (IWs). The aim of this transformation is to mitigate the well-known problem of the degeneracy of the IWs by controlling the weight variability. We consider the clipping transformation and test its robustness with respect to the choice of the clipping value. We also propose a novel NIS methodology, where not only a subset of weights is modified a posteriori, but also the corresponding samples are moved. We compare these NIS schemes with standard IS and Monte Carlo methods by means of illustrative numerical examples.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432561","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
The Geometry of Constrained Randomwalks and an Application to Frame Theory 约束随机游走的几何及其在框架理论中的应用
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450816
C. Shonkwiler
{"title":"The Geometry of Constrained Randomwalks and an Application to Frame Theory","authors":"C. Shonkwiler","doi":"10.1109/SSP.2018.8450816","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450816","url":null,"abstract":"Random walks in ${{mathbb{R}}^3}$ are classical objects in geometric probability which have, over the last 70 years, been rather successfully used as models of polymers in solution. Modifying the theory to apply to topologically nontrivial polymers, such as ring polymers, has proven challenging, but several recent breakthroughs have been made by thinking of random walks as points in some nice conformation space and then exploiting the geometry of the space. Using tools from symplectic geometry, this approach yields a fast algorithm for sampling loop random walks. Such walks can be lifted via the Hopf map to finite unit norm tight frames (FUNTFs) in ${{mathbb{C}}^2}$, producing an algorithm for randomly sampling FUNTFs in ${{mathbb{C}}^2}$ as well as a mechanism for searching for FUNTFs with nice properties. In general, symplectic geometry seems like a promising tool for understanding the space of FUNTFs in ${{mathbb{C}}^d}$ for any d.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130138476","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
Online Estimation of Coherent Subspaces with Adaptive Sampling 自适应采样的相干子空间在线估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450830
Greg Ongie, David Hong, Dejiao Zhang, L. Balzano
{"title":"Online Estimation of Coherent Subspaces with Adaptive Sampling","authors":"Greg Ongie, David Hong, Dejiao Zhang, L. Balzano","doi":"10.1109/SSP.2018.8450830","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450830","url":null,"abstract":"This work investigates adaptive sampling strategies for online subspace estimation from streaming input vectors where the underlying subspace is coherent, i.e., aligned with some subset of the coordinate axes. We adapt the previously proposed Grassmannian rank-one update subspace estimation (GROUSE) algorithm to incorporate an adaptive sampling strategy that substantially improves over uniform random sampling. Our approach is to sample some proportion of the entries based on the leverage scores of the current subspace estimate. Experiments on synthetic data demonstrate that the adaptive measurement scheme greatly improves the convergence rate of GROUSE over uniform random measurements when the underlying subspace is coherent.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128741536","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
Optimal Constraint Vectors for Set-Membership Proportionate Affine Projection Algorithms 集隶属度比例仿射投影算法的最优约束向量
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450820
M. Spelta, W. Martins
{"title":"Optimal Constraint Vectors for Set-Membership Proportionate Affine Projection Algorithms","authors":"M. Spelta, W. Martins","doi":"10.1109/SSP.2018.8450820","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450820","url":null,"abstract":"Sparsity is an inherent feature of certain practical systems and appears in problems such as channel equalization and echo cancellation. Designed for exploiting the intrinsic structure of sparse environments, while also taking advantage of the data reuse and selection strategies, the set-membership proportionate affine projection algorithm (SM-PAPA) relies on the choice of a constraint vector (CV) that affects the behavior of the adaptive system. Although the selection of this CV has been based on some heuristics, a recent work proposes an optimal CV for the set-membership affine projection algorithm, a particular instance of the SM-PAPA. This paper adopts a convex optimization framework and generalizes the optimal CV concept for the SM-PAPA, allowing its use in sparse systems. Moreover, by using the gradient projection method for solving the related constrained convex problem, this paper demonstrates that the optimal CV can indeed be applied in real-time applications.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124681287","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 Geometrical Study of the Bivariate Fractional Gaussian Noise 二元分数高斯噪声的几何研究
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450737
J. Lefèvre, N. L. Bihan, P. Amblard
{"title":"A Geometrical Study of the Bivariate Fractional Gaussian Noise","authors":"J. Lefèvre, N. L. Bihan, P. Amblard","doi":"10.1109/SSP.2018.8450737","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450737","url":null,"abstract":"We study the stochastic properties of the bivariate fractional Gaussian noise based on a polarization spectral analysis. The originality of the approach consist in the particular attention given to geometric features, achieved by the use of spectral quantities named Stokes parameters. Explicit expressions for these parameters are provided for the bivariate fractional noise. A special attention is given to the notion of degree of polarization, which allows to provide the synthesis of the fractional Gaussian noise as the sum of polarized and unpolarized processes.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"31 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120908207","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
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