IEEE Seventh SP Workshop on Statistical Signal and Array Processing最新文献

{"title":"Noise Covariance Modeling In Array Processing","authors":"B. Friedlander, A. Weiss","doi":"10.1109/SSAP.1994.572467","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572467","url":null,"abstract":"We consider the problem of direction finding in the presence of colored noise whose covariance matrix is unknown. The ambient noise covariance matrix can be modeled by a sum of Hermitian matrices known up to a multiplicative scalar. Using this model, we estimate jointly the directions of arrival of the signals and the noise model parameters. Under certain conditions, it is possible to obtain unbiased and efficient estimates of the signal directions.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127257530","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":"Wavelet and Principal Components Decomposition of Pattern- Reversal Visual Evoked Potentials in Patients with Degenerative Retinal Diseases","authors":"V. Samar, G. Kulkarni, V. Udpikar, P. Damle, I. Parasnis, K. Swartz, M. Raghuveer","doi":"10.1109/SSAP.1994.572542","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572542","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127461808","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 Network Array Processing","authors":"E.J. Baranoski","doi":"10.1109/SSAP.1994.572465","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572465","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128795008","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":"Multi-Spectral Data Fusion Using a Markov Random Field Model : Application to Satellite Image Classification","authors":"D. Murray, J. Zerubia","doi":"10.1109/SSAP.1994.572527","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572527","url":null,"abstract":"I n this paper, we present a method of classifying multi-spectral satellite images. Data fusion of the multi-spectral images is achieved using a Markov random field approach. Classification is expressed as an energy minimization, problem and solved using Simulated Annealing with the Gibbs Sampler fo r label updating. The results of two digerent methods of class training, supervised and unsupervised, are shown. The proposed fusion method improved the results over those with only a single input channel.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129035899","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":"Passive Sonar Detection and Localization by Matched Filtering","authors":"Y. Chan, S. P. Morton, G. Niezgoda","doi":"10.1109/SSAP.1994.572512","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572512","url":null,"abstract":"operation that estimates the number of targets. This paper presents a new method of detecting and tracking low signal-to-noise ratio (SNR) wide-band targets on a constant course and velocity trajectory. A track-before-detect strategy is adopted using spatial images constructed from conventional beamformer power bearing maps and a discrete bank of three dimensional matched velocity filters. A Neyman-Pearson detector forms a key feature of this technique, allowing the selection of trajectory solutions to be automated. Theoretical receiver operating characteristic curves show the increase in detection gain under low SNR conditions for matched velocity filtering in comparison to detect-before-track methods. Notationally, boldfaced lower-case and upper-citse symbols denote vectors and matrices, respectively. 11. Background We model the ocean as a non-dispersive, homogeneous propagation medium. The wavefield consists of Ns independent wide-band point sources of acoustic energy located in the far field of a horizontally oriented linear array of M equi-spaced sensors. At time t we denote Ns 4 2 ) = CSl(t) + n(t) (1) 3=1","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848709","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 Novel Motion Estimation Technique Using Genetic Algorithm Search","authors":"C. Bussiere, D. Hatzinakos","doi":"10.1109/SSAP.1994.572510","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572510","url":null,"abstract":"Traditional motion estimation (ME) techniques have relied upon the assumption that their evaluation function was sufficiently unimodal to warrant the application of simple gradient based search to find the displacement vector field or their image sequence. What we have done is to iDVF) evelop a more robust M E technique which uses Genetic Algorithms (GA) to maintain a statistically generated p o p ulation of candidate solutions’. Our ME technique works within a complex motion environment containing three dimensions of displacement and rotation and thus requires 6 degrees of freedom. These 6 degrees of freedom are implemented using a novel frequency domain image warping technique which reestablished a frame to frame correspondence and allows for the application of a correlation measure as the fitness function. T h e paper presents a discussion of the advantages of GAS for multimodal search in the context of motion estimation in a complex environment and presents a novel means of hybridizing the search so as to improve the convergence properties of the algorithm. Simulation results are used to show the performance of GAS in locating global solutions to the ME problem.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121409585","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 New Objective Measure Of Signal Complexity Using Bayesian Inference","authors":"A. Quinn","doi":"10.1109/SSAP.1994.572444","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572444","url":null,"abstract":"An objective Ockham prior which penalizes complexity in parametric signal hypotheses is derived from Bayesian fundamentals. Novel quantitative definitions of complexity are deduced under the procedure. This improves on current variants of the coding theoretic Minimum Message Length (MML) criterion where complexity definitions are imposed as heuristics. It is shown that the Ockham prior arises naturally in marginal Bayesian inference, but is excluded if joint inference is adopted. 1. I N T R O D U C T I O N : BAYESIAN SYSTEMATIC HYPOTHESES The Signal Identification problem arises whenever a systematic hypothesis is adopted to explain an observed data set. Let d = (d l , . . . , d ~ ) ~ be a finite set of one-dimensional observations. The prior hypothesis, 2, asserts that 2. O C K H A M ’ S R A Z O R Systematic hypotheses (1) must be assessed in the context of Ockham’s Razor (i.e. the Desideratum of Simplicity) [3] which, for the purposes of time series analysis, states that randomness must not be fitted with determinism. Consider a set of hypotheses, 21~12, . . ., parameterized with p.p. sets, 0 1 , 0 2 , . . . respectively. The likelihood function (LF) for the kth hypothesis is 1(& I d, 2,) = p(d I &, I k ) . The classical approach is to identify a model for d by maximizing l ( . ) . The resulting point inference, arg . sup, . supel I(& I d, lk), identifies both a mode1 (solving the Model Selection problem) and a set of parameter values (solving the Parameter Estimation problem). In open-ended inference, we may posit models with increasing numbers of degrees of heedom (e.g. increasing model order). In this manner, llell is reduced (l), 11 . 11 being the norm implied by the p.d.f. of e [l]. Ultimately, the likelihood-based inference machine fails because of its insensitivity to Ockham’s Razor. d = s + e 2.1. Subjec t ive vs. Objec t ive Complexi ty s # 0 is the unknown deterministic component (i.e. the ‘signal’) and e is the vector of unknown, non-systematic residuals. If the hypothesis is parametric, then s = s(8). The validity of the decomposition (1) must be assessed for a particular d, since all subsequent steps in the inference taskmodel selection, parameter estimationdepend upon it. 1.1. Probabi l i ty as Belief Calculus The fundamental concept underlying the Bayesian Paradigm is that the beliefs associated with inductive inference are uniquely quantified as probabilities and are consistently manipulated using the Probability Calculus [l]. The equivalence of the Belief Calculus and the Probability Calculus has been deduced from fundamentals [’I. Any unknowneither fixed or random-in a hypothesis has a domain, R, of possible values. The distribution of beliefs across R is expressed by a p.d.f. This constitutes tlie definition of a probabilistic parameter (p.p.) [ I ] which is tlie appropriate Bayesian extension of the random variable (r.v.) concept of orthodox inference. The two definitions merge when the uiikiiown is i~iherently random. The p","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122587815","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":"Optimum Wavelet Design for Transient Detection","authors":"Q. Jin, K. Wong, Q. Wu","doi":"10.1109/SSAP.1994.572492","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572492","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132506113","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":"Recent Advances In The Theory And Application Of Predictive-transform Space-time Array Processing","authors":"J. Guerci, E. Feria","doi":"10.1109/SSAP.1994.572470","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572470","url":null,"abstract":"","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131145813","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":"Rejection of Narrow-Band Interferences in PN Spread Spectrum Systems Using an Eigenanalysis Algorithm","authors":"A. Haimovich, A. Vadhri","doi":"10.1109/SSAP.1994.572523","DOIUrl":"https://doi.org/10.1109/SSAP.1994.572523","url":null,"abstract":"A new eigenanalysis based adaptive algorithm is suggested for rejecting narrow-band interferences in spread spectrum communications. The optimal linear interference canceler implemented as a transversal filter is found from the solution of the Wiener-Hopf equations. A different approach is suggested by the eigenanalysis of the data across the filter taps. The spread spectrum signal has a white spectrum, i.e., its energy is uniformly distributed across the eigenvalues of the correlation matrix. The interference, however, has its energy concentrated in just a few large eigenvalues. The corresponding eigenvectors contain all the frequency domain information required to reject the interference. The eigenanalysis based canceler is referred to as an Eigencanceler and is derived as a modified prediction error filter. An adaptive algorithm based on the power method is shown to provide faster convergence than the LMS and RLS algorithms.","PeriodicalId":151571,"journal":{"name":"IEEE Seventh SP Workshop on Statistical Signal and Array Processing","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123732920","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}