{"title":"A Bayesian Estimation Bound based on the Optimal Bias Function","authors":"Z. Ben-Haim, Yonina C. Eldar","doi":"10.1109/CAMSAP.2007.4497961","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497961","url":null,"abstract":"We consider the problem of finding a lower bound on the minimum mean-squared error in a Bayesian estimation problem. The bound of Young and Westerberg, which is based on determining the optimal bias function, is extended to the case of a vector parameter. A numerical study demonstrates that the bound is both tighter and simpler to compute than alternative techniques.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"82 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":"130115495","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":"Computing The Nash Bargaining Solution for the 2X2 Frequency Selective Interference Channel","authors":"E. Zehavi, Amir Leshem","doi":"10.1109/CAMSAP.2007.4497999","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497999","url":null,"abstract":"In this paper we extend our previous work analyzing the interference channel as a conflict situation to frequency selective channels and joint TDM/FDM strategies. We provide an O(K log K) complexity algorithm for computing the Nash bargaining solution under mask constraint for the 2times2 frequency selective interference channel (with K frequency bins) under joint FDM/TDM strategies. Simulation results are also provided. We also show the convexity of the N players game with similar strategies.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"60 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":"122535771","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":"Mimo SAR Imaging: Signal Synthesis and Receiver Design","authors":"Jian Li, Xiayu Zheng, P. Stoica","doi":"10.1109/CAMSAP.2007.4497972","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497972","url":null,"abstract":"A multi-input multi-output (MIMO) radar can be used to form a synthetic aperture for high resolution imaging. To successfully utilize the MIMO synthetic aperture radar (SAR) system for practical imaging applications, constant-modulus transmit signal synthesis and optimal receive filter design play critical roles. We present in this paper a computationally attractive cyclic optimization algorithm for the synthesis of constant-modulus transmit signals with good auto- and cross- correlation properties. Then we go on to discuss the use of an instrumental variables approach to design receive filters that can be used to minimize the impact of scatterers in nearby range bins on the received signals from the range bin of interest (the so-called range compression problem). Finally, we present a number of numerical examples to demonstrate the effectiveness of the proposed approaches.","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":"130750676","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":"Signal Processing Aspects of Real-Time DNA Microarrays","authors":"H. Vikalo, B. Hassibi, A. Hassibi","doi":"10.1109/CAMSAP.2007.4497991","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497991","url":null,"abstract":"Data acquisition in conventional fluorescent-based microarrays takes place after the completion of a hybridization phase. During the hybridization phase, target analytes bind to their corresponding capturing probes on the array. The conventional microarrays attempt to detect presence and quantify amounts of the targets by collecting a single data point, supposedly taken after the hybridization process has reached its steady-state. Recently, so-called real-time microarrays capable of acquiring not only the steady-state data but the entire kinetics of hybridization have been proposed in [1]. The richness of the information obtained by the real-time microarrays promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to the conventional microarrays. In the current paper, we study the signal processing aspects of the real-time microarray data acquisition.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"17 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":"130805918","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}
R. Gribonval, B. Mailhé, H. Rauhut, K. Schnass, P. Vandergheynst
{"title":"Multichannel Thresholding with Sensing Dictionaries","authors":"R. Gribonval, B. Mailhé, H. Rauhut, K. Schnass, P. Vandergheynst","doi":"10.1109/CAMSAP.2007.4497983","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497983","url":null,"abstract":"This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this algorithm and show that the latter results in much weaker conditions on the dictionary, sensing dictionary pair. We then do numerical simulations to confirm our theoretical findings, showing that p-thresholding is an interesting low complexity alternative to simultaneous greedy or convex relaxation algorithms for processing sparse multichannel signals with balanced coefficients, and finally point a connection to compressed sensing exploiting the additional freedom in designing the sensing dictionary.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"4 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":"114748802","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":"A Statistically Based Preconditioner for Two-Dimensional Microwave Tomography","authors":"A. Fhager, M. Gustafsson, S. Nordebo, M. Persson","doi":"10.1109/CAMSAP.2007.4497993","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497993","url":null,"abstract":"This paper presents an estimation approach to preconditioning for gradient based inverse scattering algorithms. In particular, a two-dimensional inverse problem is considered where the permittivity and conductivity profiles are unknown and the input data consists of the scattered field over a certain bandwidth. A time-domain least-squares formulation is employed and the inversion algorithm is based on a conjugate gradient algorithm together with an FDTD-electromagnetic solver. A Fisher information analysis is used to estimate the Hessian of the error functional. A robust preconditioner is then obtained by choosing a parameter scaling such that the scaled Fisher information has a unit diagonal, cf., the Jacobi preconditioner in numerical analysis. Numerical examples of image reconstruction are included to illustrate the efficiency of the proposed technique.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"1 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":"121864266","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":"Bearings-Only Tracking with Biased Measurements","authors":"M. Bugallo, Ting Lu, P. Djurić","doi":"10.1109/CAMSAP.2007.4498016","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498016","url":null,"abstract":"This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines a standard particle filter and one Kalman filter to efficiently resolve the fusion problem. The algorithms are tested and compared by computer simulations which offer insight into the advantages and disadvantages of the proposed method.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"1 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":"116919373","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":"True Cramer-Rao Bound for Estimating Synchronization Parameters from a Linearly Modulated Bandpass Signal with Unknown Data Symbols","authors":"N. Noels, M. Moeneclaey","doi":"10.1109/CAMSAP.2007.4497957","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497957","url":null,"abstract":"This paper considers the Cramer-Rao bound (CRB) for estimating the synchronization parameters from a linearly modulated waveform with unknown data symbols. The key idea is to reduce the computational complexity associated with the CRB by exploiting the specific structure of the observed signal. The current contribution builds on previous work, but in this case, no distributional assumptions are made about the data symbols. A generalized closed-form expression of the CRB is presented, the resulting computational complexity is discussed, and numerical results are provided.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"49 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":"125041521","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":"Sequential Monte Carlo Methods for Shallow Water Tracking Using Multiple Sensors with Adaptive Frequency Selection","authors":"J. Zhang, A. Papandreou-Suppappola","doi":"10.1109/CAMSAP.2007.4498017","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498017","url":null,"abstract":"We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance, we design a frequency selection algorithm which adaptively chooses the optimal observation frequency for the sensors at each time instant. The improved tracking performance is demonstrated using simulations.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"1 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":"123152738","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 Distributed Decision Over Wireless Sensor Networks Affected by Multipath Fading","authors":"G. Scutari, S. Barbarossa","doi":"10.1109/CAMSAP.2007.4498011","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498011","url":null,"abstract":"The design of sensor networks capable of reaching a consensus on a globally optimal decision test, without the need for a fusion center, is a problem that has received considerable attention in the last years. Many consensus algorithms have been proposed, with convergence conditions depending on the graph describing the interaction among the nodes. In most works, the graph is undirected and there are no propagation delays. Only recently, the analysis has been extended to consensus algorithms incorporating a homogeneous delay. The more realistic case of inhomogeneous delays has been considered only for agreement algorithms, whose goal is to make all nodes able to reach a common value, but without the need to force such a final value to coincide with a prescribed globally optimal decision function. In this work, we propose a consensus algorithm where each node converges to a globally optimal decision statistic, valid for a wideband wireless network, where the link between each pair of nodes is a multipath, frequency-selective, channel. The main contribution of the paper is to derive necessary and sufficient conditions on the network topology and sufficient conditions on the channel transfer functions guaranteeing the exponential convergence of the proposed algorithm to a globally optimal decision value, for any bounded delay condition.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"37 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":"127142411","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}