{"title":"Computable Lower Bounds for Deterministic Parameter Estimation","authors":"É. Chaumette, J. Galy, F. Vincent, P. Larzabal","doi":"10.1109/CAMSAP.2007.4497960","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497960","url":null,"abstract":"This paper is primarily tutorial in nature and presents a simple approach (norm minimization under linear constraints) for deriving computable lower bounds on the MSE of deterministic parameter estimators with a clear interpretation of the bounds. We also address the issue of lower bounds tightness in comparison with the MSE of ML estimators and their ability to predict the SNR threshold region. Last, as many practical estimation problems must be regarded as joint detection-estimation problems, we remind that the estimation performance must be conditional on detection performance, leading to the open problem of the fundamental limits of the joint detection-estimation performance.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131788615","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}
{"title":"Modeling Count Data from Multiple Sensors: A Building Occupancy Model","authors":"J. Hutchins, A. Ihler, Padhraic Smyth","doi":"10.1109/CAMSAP.2007.4498010","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498010","url":null,"abstract":"Knowledge of the number of people in a building at a given time is crucial for applications such as emergency response. Sensors can be used to gather noisy measurements which when combined, can be used to make inferences about the location, movement and density of people. In this paper we describe a probabilistic model for predicting the occupancy of a building using networks of people-counting sensors. This model provides robust predictions given typical sensor noise as well as missing and corrupted data from malfunctioning sensors. We experimentally validate the model by comparing it to a baseline method using real data from a network of optical counting sensors in a campus building.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126031510","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}
{"title":"Wavefront Adaptive Sensing for Radar Spread Clutter Mitigation","authors":"I. Bilik, O. Kazanci, J. Krolik","doi":"10.1109/CAMSAP.2007.4497996","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497996","url":null,"abstract":"In spatially inhomogeneous, Doppler-spread radar environments, adaptive processing is often precluded because neither the target wavefront is sufficiently known nor is signal-free training data available. This paper presents a new clutter mitigation method designed to overcome these challenges by combining minimum variance (MV) adaptive beamforming and blind source separation (BSS) for distributed sources. Wavefront adaptive sensing (WAS) is a hybrid adaptive beamforming approach which uniformly maximizes gain against clutter by avoiding MV signal cancellation due to mismatch at high SNR and poor BSS threshold performance at low SNR. Simulation results are presented for target detection in a multi-mode spread-Doppler over-the-horizon radar scenario.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129696199","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}
{"title":"Sparse Bayesian Estimation of Superimposed Signal Parameters","authors":"D. Shutin, G. Kubin","doi":"10.1109/CAMSAP.2007.4498018","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498018","url":null,"abstract":"This paper addresses parameter estimation of superimposed signals jointly with their number within the Bayesian framework. We combine sparse Bayesian machine learning methods with the state of the art SAGE-based parameter estimation algorithm. Existing sparse Bayesian methods allow to assess model order through priors over model parameters, but do not consider models nonlinear in parameters. SAGE-based parameter estimation does allow nonlinear model structures, but lacks a mechanism for model order estimation. Here we show how Gaussian and Laplace priors can be applied to enforce sparsity and determine the model order in case of superimposed signals, as well as develop an EM-based learning algorithm that efficiently estimate parameters of the superimposed signals as well as prior parameters that control the sparsity of the learned models. Our work extends the existing approaches to complex data and models nonlinear in parameters. We also present new analytical and empirical studies of the Laplace sparsity priors applied to complex data. The performance of the proposed algorithm is analyzed using synthetic data.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114362805","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}
E. Matskani, N. Sidiropoulos, Z. Luo, L. Tassiulas
{"title":"Joint Multicast Beamforming and Admission Control","authors":"E. Matskani, N. Sidiropoulos, Z. Luo, L. Tassiulas","doi":"10.1109/CAMSAP.2007.4497997","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497997","url":null,"abstract":"Wireless multicasting is quickly emerging as an important enabling technology for mass content distribution to mobile hand-held devices. With channel state information at the transmitter, it is possible to tailor transmissions by selective beamforming towards specific user groups, thereby harnessing the wireless 'broadcast advantage' and managing co-channel interference. Wireless (physical layer) multicasting options are currently being debated in the emerging UMTS-LTE standard. A key problem in this context is admission control: it is often infeasible or impractical to serve all subscribers from a single access point, due to mutual interference or power limitations. This paper considers the joint multicast beamforming and admission control problem, aiming to maximize the number of subscribers that can be served and minimize the power required to serve them. The joint problem is NP-hard, yet suitable reformulation reveals that it can be naturally relaxed to a semidefinite program (SDP), leading to an approximation by deflation over SDP. Experimental results using measured channels indicate that the proposed approximation approach is fast and efficient.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134043877","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}
{"title":"Semi-Supervised Life-Long Learning with Application to Sensing","authors":"Qiuhua Liu, X. Liao, L. Carin","doi":"10.1109/CAMSAP.2007.4497950","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497950","url":null,"abstract":"We present a semi-supervised multitask learning (MTL) framework, where we have multiple partially labeled data manifolds, each defining a classification task for which we wish to design a semi-supervised classifier. These different data sets may be observed simultaneously, or over the sensor \"lifetime\". We propose a soft sharing prior over the parameters of all classifiers and learn all tasks jointly. The soft-sharing prior enables any task to robustly borrow information from related tasks. The semi-supervised MTL combines the advantages of semi-supervised learning and multitask learning, thus further improving the generalization performance of each classifier. Our MTL (or life-long learning) framework is based on our previous semi-supervised learning formulation, termed neighborhood-based classifier (NeBC) [1]. The performance of the semi-supervised MTL is validated by experimental results on several sensing data sets.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003580","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}
{"title":"Bootstrapping Autoregressive Plus Noise Processes","authors":"C. Debes, A. Zoubir","doi":"10.1109/CAMSAP.2007.4497963","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497963","url":null,"abstract":"We address the problem of estimating confidence intervals for the parameters of an autoregressive plus noise process, in particular when the additive noise is non-Gaussian. We demonstrate how the independent data bootstrap can be used to solve this problem. We motivate an autoregressive moving-average modeling approach and apply the recursive maximum algorithm for parameter estimation. Computer simulations are carried out to show the performance of the proposed method. Furthermore a real data example from automotive engineering has been considered for assessing our approach. Using a pressure signal from inside the combustion chamber, we show how confidence intervals for the autoregressive parameters can be calculated.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133739256","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}
{"title":"Compressed Sensing Using Prior Information","authors":"R. V. Borries, C. J. Miosso, C. Potes","doi":"10.1109/CAMSAP.2007.4497980","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497980","url":null,"abstract":"Compressed sensing has recently emerged as a technique allowing a discrete-time signal with a sparse representation in some domain to be reconstructed with theoretically perfect accuracy from a limited number of linear measurements. Current applications range from sensor networks to tomography and general medical imaging. In this paper, we show that the amount of samples which must be taken from a signal with sparse Discrete-time Fourier Transform (DFT) can be reduced compared to the original compressed sensing approach if information on the support of the sparse domain can be employed. More precisely, the required number of samples in time domain is reduced by exactly the amount of known frequencies associated to non-zero coefficients. Our results additionally provide a link between the so-called fractional Fourier transform and compressed sensing framework, when the positions of all the non-zero components are known.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115038725","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}
{"title":"The \"Hook and Loop\" Resampling Plane","authors":"R. Iskander, W. Alkhaldi","doi":"10.1109/CAMSAP.2007.4497966","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497966","url":null,"abstract":"We propose a new resampling scheme that takes literally the concept of the non-parametric bootstrap in which new samples are generated from the empirical distribution function. The introduced resampling concept is totally heuristic, but already shows promising results when applied to model selection. We show that for a range of linear models, the proposed resampling scheme outperforms the classical model selection techniques as well as its predecessor, the non-parametric bootstrap. It also simplifies the practical problem of choosing residual scaling or the length of the subsample that exists in the traditional bootstrap based model selection approach.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125995045","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}
{"title":"Optimal Decentralized Linear Precoding for Wideband Non-Cooperative Interference Systems based on Game Theory","authors":"G. Scutari, D. Palomar, S. Barbarossa","doi":"10.1109/CAMSAP.2007.4497971","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497971","url":null,"abstract":"In this paper we formulate the problem of finding the optimal pre- coding/multiplexing strategy in an infrastructureless multiuser scenario as a noncooperative game. We first consider the theoretical problem of maximizing mutual information on each link, given constraints on the spectral mask and transmit power. Then, to accommodate practical implementation aspects, we focus on the competitive maximization of the transmission rate on each link, using finite order constellations, under the same constraints as above plus a constraint on the average error probability. We prove that in both cases a NE always exists and the optimal precoding/multiplexing strategy leads to a (pure strategy) diagonal transmission for all the users. Thanks to this result, we can reduce both original complicated matrix-valued games to a simpler unified vector power control game. Thus, we derive sufficient conditions for the uniqueness of the NE of such a game, that are proved to have a broader validity than conditions known in the literature for special cases of our game. Finally, we show that the Nash equilibria of the vector game can be reached using the so-called asynchronous iterative waterfilling algorithm.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127308693","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}