Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572467
B. Friedlander, A. Weiss
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.
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572542
V. Samar, G. Kulkarni, V. Udpikar, P. Damle, I. Parasnis, K. Swartz, M. Raghuveer
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572465
E.J. Baranoski
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572527
D. Murray, J. Zerubia
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.
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572512
Y. Chan, S. P. Morton, G. Niezgoda
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
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572510
C. Bussiere, D. Hatzinakos
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.
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572444
A. Quinn
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
一个客观的奥卡姆先验，惩罚复杂性的参数信号假设是由贝叶斯基础推导出来的。在此过程中推导出新的复杂性的定量定义。这改进了编码理论最小消息长度(MML)标准的当前变体，其中复杂性定义作为启发式强加。结果表明，在边际贝叶斯推理中，奥卡姆先验是自然产生的，但在联合推理中，奥卡姆先验是不存在的。1. 当采用系统假设来解释观测数据集时，信号识别问题就出现了。令d = (d1，…)， d ~) ~是一维观测值的有限集合。先验假设2表明。系统假设(1)必须在奥卡姆剃刀(即简单的愿望)[3]的背景下进行评估，为了时间序列分析的目的，它表明随机性不能与决定论相适应。考虑一组假设，21~12，…，用p.p.集参数化，0,1,0,2，…。分别。第k个假设的似然函数(LF)是1(& I d, 2，) = p(d I &， I k)。经典的方法是通过最大化l()来确定d的模型。． 由此产生的点推理，如。吃晚饭。supel I(& I d，如)标识一个模型1(解决模型选择问题)和一组参数值(解决参数估计问题)。在开放式推理中，我们可以假设自由度不断增加的模型(例如，模型阶数不断增加)。以这种方式，ll被还原为(1)，11。11为e的P.D.F.所隐含的规范[1]。最终，基于可能性的推理机失败了，因为它对奥卡姆剃刀不敏感。D = s + e主体与客体之间的关系复杂度为0是未知的确定性成分(即“信号”)，e是未知的、非系统残差的向量。如果假设是参数化的，则s = s(8)。分解(1)的有效性必须对特定的d进行评估，因为推理任务模型选择、参数估计的所有后续步骤都依赖于它。1.1. 贝叶斯范式的基本概念是，与归纳推理相关的信念被唯一地量化为概率，并始终使用概率演算进行操作[1]。从基本原理[1]推导出了信念演算与概率演算的等价性。假设中的任何未知数，无论是固定的还是随机的，都有一个可能值的域R。信念在R上的分布由p.d.f表示。这构成了概率参数(p.p.)的定义[I]，这是正统推理的随机变量(r.v.)概念的适当贝叶斯扩展。这两种定义合并在一起时，其本身是随机的。承认奥卡姆推理的问题往往分为两个不同的阶段来处理[3-71]:(i)提出一个可接受的(可量化的)复杂性定义;(ii)采用包含该复杂度的单调递减函数的推理过程。Jeffreys[3]试图确定简单性和拟合接近度之间的变化率。他的“简单性假设”指出，“更简单的定律有更大的先验概率”。这种主观的做法忽视了客观地产生原则的前景。
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572492
Q. Jin, K. Wong, Q. Wu
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572470
J. Guerci, E. Feria
{"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":null,"pages":null},"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}
Pub Date : 1994-06-26DOI: 10.1109/SSAP.1994.572523
A. Haimovich, A. Vadhri
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.
{"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":null,"pages":null},"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}
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