1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.最新文献

{"title":"Reduced complexity blind unitary prewhitening with application to blind source separation","authors":"S. Vorobyov","doi":"10.1109/CAMAP.2005.1574214","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574214","url":null,"abstract":"Eigenvalue decomposition (EVD) of the sample data covariance matrix is, typically, used for calculating the whitening matrix and prewhitening the noisy signals. An important problem here is to reduce the computational complexity of the EVD of the complex-valued sample data covariance matrix. In this paper, we show that the complexity of the prewhitening step for complex-valued signals can be reduced approximately by a factor of four when the real-valued EVD is used instead of the complex-valued. Such complexity reduction can be achieved for any axis-symmetric array. For such class of arrays it enables real-time implementation of the prewhitening step for complex-valued signals. The performance of the proposed procedure is shown in application to a blind source separation (BSS) problem","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127408146","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":"Structured covariance estimation and radar imaging with sparse linear models","authors":"D. Fuhrmann","doi":"10.1109/CAMAP.2005.1574170","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574170","url":null,"abstract":"The problem of the computational complexity of the structure covariance EM algorithm is considered. Ordinarily this algorithm requires O(N/sup 3/) floating point operations, per iteration, for the estimation of an N-point power spectrum. However, if the linear model relating the observations to the underlying variables is sparse, the computational burden can be reduced to O(N) operations. This sparsity can be achieved approximately by a data preprocessing step that causes the effect of each underlying variable to be seen in only one component of the preprocessed observation vectors. An illustrative example involving a rotating linear array as the sensor and a Chebyshev filter bank as the preprocessor is given.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130892405","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":"Designing unique words for reliable channel estimation in multi-antenna block transmission systems","authors":"J. Coon, M. Sandell","doi":"10.1109/CAMAP.2005.1574203","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574203","url":null,"abstract":"Single-carrier, multiple-input multiple-output wireless communication systems generally require knowledge of the channel to equalize a received message. In block transmission systems, such as those that utilize the frequency domain to facilitate channel equalization, short training sequences known as unique words (UWs) can be inserted into the data stream, thus providing a means for estimating and tracking the state of the channel. The problem of designing the UWs can be difficult when certain constraints are placed on the sequences. In this paper, a nonlinear optimization approach is taken to design near-optimal UWs under given constraints, such as a limit on the peak-to-average power ratio (PAPR) of the sequences. Optimality of the sequences is defined with respect to the Cramer-Rao bound on performance for an unbiased estimator","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131015075","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":"Neural network computational technique for high-resolution remote sensing image reconstruction with system fusion","authors":"Y. Shkvarko, J.L. Leyva-Montiel, I. Villalón-Turrubiates","doi":"10.1109/CAMAP.2005.1574211","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574211","url":null,"abstract":"We address a new approach to the problem of improvement of the quality of scene images obtained with several sensing systems as required for remote sensing imagery, in which case we propose to exploit the idea of robust regularization aggregated with the neural network (NN) based computational implementation of the multi-sensor fusion tasks. Such a specific aggregated robust regularization problem is stated and solved to reach the aims of system fusion with a proper control of the NN's design parameters (synaptic weights and bias inputs viewed as corresponding system-level and model-level degrees of freedom) which influence the overall reconstruction performances","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115257407","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":"Two-set expected-likelihood GLRT technique for adaptive detection","authors":"Y. Abramovich, N. Spencer","doi":"10.1109/CAMAP.2005.1574171","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574171","url":null,"abstract":"We introduce a new generalized likelihood-ratio test (GLRT) framework for adaptive detection that differs from Kelly's standard method (E.J. Kelly, 1986) in two main aspects. First, the separate functions of the primary and secondary data are respected, with a single set of interference estimates for both hypotheses being searched to optimize the detection performance. Second, instead of the traditional maximum likelihood (ML) principle, we propose to search for a set of estimates that generates statistically the same likelihood as the unknown true parameters. We present results for a typical example scenario that demonstrates considerable detection performance improvement.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130720929","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":"Warped image factor analysis","authors":"Sungjin Hong","doi":"10.1109/CAMAP.2005.1574199","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574199","url":null,"abstract":"In factor analysis of sequential data (e.g., time-series or digitized images), the measurement sequence remains \"intact\" and is assumed to be consistent across all measurement conditions. Otherwise, recovered sequential factors would be distorted. Shifted and warped factor analyses (SFA and WFA) explicitly fit such measurement-sequence inconsistency. Warped image factor analysis (WIFA) combines two ideas: (a) fitting systematic shape variation of image factors, and (b) decomposing many 2D images into a few image factors. WIFA allows image factors to change shape independently, unlike what is assumed in a data-level adjustment: synchronized shape changes of image factors. The latent-level shape variation modeled in WIFA seems to make recovered factors \"unique\" in some two-way cases, as in SFA and WFA. The shape variation of image factors is parameterized as bilinear warping of segmented images. A quasi-ALS (alternating least squares) algorithm for WIFA is described, which uses alternating regression for factor weights and nonlinear optimization for warping-size parameters. The method is demonstrated with a simulated example","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129991724","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":"An algorithm or the neural fusion of IRST & radar for airborne target detection","authors":"J. Singh","doi":"10.1109/CAMAP.2005.1574179","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574179","url":null,"abstract":"This paper investigates in to the possibility of using a BAM correlating encoding based neural fusion of IRST and radar at the point of the IRST's maximum range. During training phase (in peace time or at a safe place or range), intermittent appearance of a target on IRST display can be recorded in a temporal array. Corresponding intermittent appearance on radar will also be recorded on another array. Treating IRST array as horizontal array and radar array as vertical one, these two binary arrays will be made bipolar by replacing 0s with 1s and multiplied and square or rectangular arrays obtained. A large number of sets can be obtained like this representing the entire representative situations and corresponding square matrices added to form a general weight matrix. Data corresponding to the intermittent appearances of targets and other objects on radar display will be kept in the forms of binary arrays as database. In application phase, if a target is detected through the radar at the maximum range where target appears on the IRST display, radar can be switched off. IRST display will show intermittent appearances of the target, which may be difficult to track or even to discriminate from nearby bird or far off planet/star. The data collected for a number of frames for a single target's estimated intermittent appearance will be stored in an array as binary data. This binary array will be multiplied with the general weight matrix and resulting vertical matrix after thresholding represents an estimated radar data. This approximated radar binary array can be compared with stored radar representations and nearest class can be declared the class of the object present in the scene. As a further improvement, this whole experiment can be performed in a peaceful condition and the estimated radar representation obtained can be compared with exact radar representation and error calculated. Another neural model (like multilayer perceptron) can be used to provide a feedback to correct the errors in the radar estimation. The process basically works as an adaptive filter and predicts a radar array corresponding to the IRST array. The success of the algorithm depends on the training (selecting representative situations) and the implementation methods. Optical implementation with optical associative memories can also be experimented for faster processing.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117281124","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":"Monte Carlo algorithms for tracking a maneuvering target using a network of mobile sensors","authors":"J. Míguez, Antonio Artés-Rodríguez","doi":"10.1109/CAMAP.2005.1574191","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574191","url":null,"abstract":"We address the problem of tracking a maneuvering target that moves over a two-dimensional region using a network of mobile binary sensors. The transmission of binary decisions (presence or absence of the target within the sensor range) is advantageous because it reduces energy consumption considerably. Also, the use of mobile sensors allows tracking the target over a large area with only a limited number of devices. We introduce two algorithms, based on the sequential Monte Carlo methodology, that track the target and the sensors (whose position is also unknown) jointly. The performance of the trackers is illustrated by means of computer simulations.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130903115","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":"Autonomous intelligent radar system (AIRS) for multi-sensor radars","authors":"G. Capraro, W. Baldygo, R. Day, J. Perretta, M. Wicks","doi":"10.1109/CAMAP.2005.1574172","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574172","url":null,"abstract":"An autonomous intelligent radar system (AIRS) deployed on a surveillance aircraft is briefly described. A net-centric compliant approach for integrating AIRS is presented. An overview of unmanned autonomous air vehicle research is provided along with a discussion of some of the issues with integrating AIRS aboard these vehicles.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133086990","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":"Efficient multi-user MIMO downlink precoding and scheduling","authors":"M. Haardt, V. Stankovic, G. del Galdo","doi":"10.1109/CAMAP.2005.1574228","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574228","url":null,"abstract":"Space division multiple access (SDMA) promises high gains in the system throughput of wireless multiple antenna systems. If SDMA is used on the downlink of a multi-user MIMO system, either long-term or short-term channel state information has to be available at the base station (BS) to faciliate the joint precoding of the signals intended for the different users. Precoding is used to efficiently eliminate or suppress multi-user interference (MUI) via beamforming or by using ”dirty-paper” codes. It also allows us to perform most of the complex processing at the BS which leads to a simplification of the mobile terminals. In this paper, we provide an overview of efficient linear and non-linear precoding techniques for multi-user MIMO systems. The performance of these techniques is assessed via simulations on statistical channel models, and on channels generated by the IlmProp, a geometry-based channel model that generates realistic correlations in space, time, and frequency.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133589848","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}