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检索条件"任意字段=13th Conference on Signal and Data Processing of Small Targets"

共 447 条记录，以下是151-160 订阅

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Efficiency and sensitivity of methods for assessing ambiguity in data association decisions

Efficiency and sensitivity of methods for assessing ambiguit...

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20th conference on signal and data processing of small targets

作者： Kragel, Bret D. Herman, Shawn M. Roseveare, Nick J. Numerica Corp Loveland CO USA

ISBN: (纸本)9780819471604

the central problem in multitarget, multisensor surveillance is that of determining which reports from separate sensors arise from common objects. Due to stochastic errors in the source reports, there may be multiple data association hypotheses with similar likelihoods. Moreover, established methods for performing data association make fundamental modeling assumptions that hold only approximately in practice. For these reasons, it is beneficial to include some measure of uncertainty, or ambiguity, when reporting association decisions. In this paper, we perform an analysis of the benefits versus runtime performance of three methods of producing ambiguity estimates for data association: enumeration of the k-best data association hypotheses, importance sampling, and Markov Chain Monte Carlo estimation. In addition, we briefly examine the sensitivity of ambiguity estimates to violations of the stochastic model used in the data association procedure.

关键词： multitarget tracking data association importance sampling Markov Chain Monte Carlo murty's method k-best enumeration

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A range rate based detection technique for tracking a maneuvering target

A range rate based detection technique for tracking a maneuv...

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17th conference on signal and data processing of small targets

作者： Ru, JF Chen, HM Li, XR Chen, GH Univ New Orleans Dept Elect Engn New Orleans LA 70148 USA

ISBN: (纸本)0819459186

In this paper a novel approach for detecting unknown target maneuver using range rate information is proposed based on the generalized Page's test with the estimated target acceleration magnitude. Due to the high nonlinearity between the range rate measurement and the target state, a measurement conversion technique is used to treat rancre rate as a linear measurement in Cartesian coordinates so that a standard Kalman filter can be applied. the detection performance of the proposed algorithm is compared with that of existing maneuver detectors over various target maneuver motions. In addition, a model switching tracker based on the proposed maneuver detector is compared with the state-of-the-art IMM estimator. the results indicate the effectiveness of the maneuver detection scheme which simplifies the tracker design. the tracking performance is also evaluated using a steady state analysis.

关键词： Target detection Motion models Sensors Error analysis Filtering (signal processing) Detection and tracking algorithms Switching Motion estimation Performance modeling Radar

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Attributes in tracking and classification with incomplete data

Attributes in tracking and classification with incomplete da...

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16th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)081945351X

An obvious use for feature and attribute data is for classification and in combat identification. the term classification is used broadly here to include discrimination, detection, target typing, identification, and pattern recognition. An additional use is in the data (or track) association process to reduce the misassociations, often called feature aided tracking. Previous papers discussed the integration of features and attributes into target track processing in addition to their use in multiple target classification. the distinction is made between feature and attribute data because they are processed differently. the term features applies to data from continuous sample space and attributes applies to data from discrete sample space. the primary concern of this paper is to address processing of attributes when there is incomplete data. For example in fusion of sensor data from distributed sensors, a sensor processor might make a hard decision on whether a target exhibits a specific target class (or attribute value) or not (without providing additional information such as likelihoods or probabilities). In another example, a sensor might distribute the likelihood of the most likely target class (or attribute property) and no information on the other possibilities. Both classification and attribute aided tracking is addressed for these examples of incomplete data. the purpose of this paper is to show the feasibility of a simple approach to dealing with incomplete data.

关键词： multiple target tracking feature aided tracking features attributes categorical features multiple models Bayesian methods and Kalman filter

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Exploring the Merit of Collaboration in Classification and Compression of Epilepsy EEG signal 13

Exploring the Merit of Collaboration in Classification and C...

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13th International Joint conference on Biomedical Engineering Systems and Technologies

作者： Ahmad, Rushda Basir Khan, Nadeem Ahmad Lahore Univ Management Sci Dept Elect Engn Lahore Pakistan

ISBN: (纸本)9789897583988

Ambulatory electroencephalogram (EEG), allows collection of patients data over extended periods of time. However, as a small recording requires large memory for storage, and this makes EEG data storage an arduous task. Moreover, classification of EEG for extraction of relevant information is relatively challenging, and selective data retrieval depends on task at hand. Consequently, EEG data storage and classification need to be computationally efficient. this paper presents a combined scheme, for the simultaneous compression and classification of EEG data, which not only decreases the overall computational effort, but also allows selective archiving and retrieval of data. Huffman and Arithmetic coding techniques are employed on CHB-MIT scalp EEG database and the results are presented in form of compression ratio (CR) and percentage root mean square distortion (PDR). For classification, Intelligent Neurologist Support System (INSS), has been used. the classifier output apart from being stored as data, is also used for intelligent data reduction, when only specific information is required, resulting in increased CR and decreased PDR, which is desired. Hence, the results show intelligent compression and reduction of data results in efficient management of EEG data. the signal undergoes state-of-the-art compression such that on reconstruction it almost maintains the same classification accuracy as the original one.

关键词： Compression Binary Encoding Epilepsy EEG

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On the challenge of keeping ATLAS tile calorimeter raw data

On the challenge of keeping ATLAS tile calorimeter raw data

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13th International conference on Advanced Technology and Particle Physics, ICATPP 2011

作者： Tsiskaridze, V.K. Physikalisches Institut Albert-Ludwigs-Universität Freiburg 3 Hermann-Herder-Str. Freiburg 79104 Germany Physics Faculty Iv. Javakhiahvili Tbiliai State University 1 Chavchavadze ave. Tbilisi 0128 Georgia

ISBN: (纸本)981440506X

the Tile Calorimeter (theCal) for the ATLAS experiment at the CERN Large Hadron Collider (LHC) is currently taking data with proton-proton collisions. the TileCal read-out system was initially designed to reconstruct the data in real-time and to store for each channel the signal amplitude, time and quality factor at the required high rate. this approach implied discarding 80% of the raw data that correspond to noise or small signals. Practical experience operating in this scheme with increasing rate have led to several modifications and understanding that some kind of data compression is helpful during data processing and storing. An alternate approach is to use online reconstruction for Level 2 triggering only and to implement a data flow lossless compression scheme for further offline analysis. A new version of the lossless compression algorithm is proposed which allows to both save the complete raw data and to feed the trigger with the reconstructed signal amplitude and time. It does not increase the data flow as compared to the existing approach and the size of the data fragments transmitted is more stable. We will describe the lossless compression algorithm as a possible upgrade of the Tile data acquisition and highlight some details of the implementation. We will report on its testing and validation and on the overall performance measured on high rate tests, calibration and s√ = 7 TeV proton-proton collisions runs.

关键词： data acquisition

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data association combined with the probability hypothesis density filter for multitarget tracking

Data association combined with the probability hypothesis de...

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16th conference on signal and data processing of small targets

作者： Lin, L Bar-Shalom, Y Kirubarajan, T Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (纸本)081945351X

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approach (MHT/assignment) may not give satisfactory results. this is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter, which propagates the PHD or the first moment instead of the full multitarget posterior density, was proposed in. (6-10) In this approach, the integral of the PHD over a region in the state space is the expected number of targets within this region and the peaks in the PHD can be regarded as the estimated locations of the targets at a given time step. In this approach the data association problem is not considered, i.e., the PHD is obtained only for a frame at a time. In our paper, a data association method combined with the PHD approach is proposed for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD filter to get the estimated number and locations of the targets at each time step, and then perform the "peak-to-track" association, whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard PHD filter. Using our approach, the multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging and termination. Simulation results demonstrate that our approach is computationally feasible and effective.

关键词： data association probability hypothesis density multitarget tracking peak extraction group target tracking assignment algorithms

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Design and implementation of location service based on signal strength difference 13

Design and implementation of location service based on signa...

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13th IEEE International conference on Intelligent Environments (IE)

作者： Jing Haifeng Wang Bin China Univ Petr Qingdao 266580 Peoples R China China Univ Petr ShengLi Sch Qingdao 266580 Peoples R China

ISBN: (纸本)9781538619209

Wireless local area network (WLAN) is a widely used wireless communication in recent years. Large scale shopping malls in the wireless local area network in the custom business positioning services are mostly accurate positioning to the store. However, the existing localization algorithms have many disadvantages, such as large signal acquisition, loss of signal and low precision when the scene is switched. In this paper, we propose a signal strength difference (SSD) algorithm for the localization of the intensity difference in wireless local area networks. the algorithm does not use the signal directly, but uses the small signal difference, which can reduce the loss caused by the environment and reduce the impact of different types of terminals on the receiving performance. At the same time, only the signal intensity data of the existing terminal can be accurately positioned, the precision is high, and the positioning cost is not needed to be installed, which greatly reduces the production cost and has wide popularization value.

关键词： Location Based Service signal intensity difference WLAN

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A Bayesian network tracking database

A Bayesian network tracking database

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16th conference on signal and data processing of small targets

作者： Obermeyer, FH Poore, AB Numer Corp Ft Collins CO USA

ISBN: (纸本)081945351X

Most maximum likelihood (ML) trackers based on measurement fusion (measurement-to-measurement or measurement-to-track) or track-to-track fusion produce a single data association hypothesis together with kinematic track state estimates. Uncertainty in the track states due to process and measurement noise is represented by covariance matrices, however uncertainty in the data association is either entirely neglected or representative of only limited types of association uncertainty. this paper presents a Bayesian-Network uncertainty management system for use in conjunction with maximum-likelihood trackers. the system, termed the Bayesian Network Tracking database (BNTD) comprises algorithms for interactive access, whereby expectation values of arbitrary track properties can be calculated over all association hypotheses, and algorithms and data-structures for long-term storage, whereby the complete set of association hypotheses can be efficiently approximated, even over long time intervals. A conjoined MLE/BNTD system is thus capable of supporting target identification (ID), feature-aided tracking, and long-term track maintenance (LTTM).

关键词： Bayesian network tracking database data association multiple hypothesis tracking uncertainty management

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Nonparametric complex background prediction algorithm using FCM clustering for dim point infrared targets detection

Nonparametric complex background prediction algorithm using ...

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4th International conference on Communications, Circuits and Systems (ICCCAS 2006)

作者： Wu, Honggang Li, Xiaofeng Chen, Yuebin Li, Zaiming Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 610054 Peoples R China

ISBN: (纸本)0780395840

A nonparametric background prediction algorithm using fuzzy c-means (FCM) clustering is proposed to enhance the detection of dim small infrared targets. in image data. the target of interest is assumed to have a very small spatial spread, and is obscured by heavy background clutter. the input image data is firstly segmented using FCM clustering, and then the nonparametric regressive method is applied to predict background in each cluster respectively. Subsequently the background is subtracted from the input data, leaving components of the target signal in the residual noise. Experiment results show better detecting performance for the output data by the algorithm of this paper than by other traditional methods.

关键词： Image analysis

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13th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2017

13th International Conference on Intelligent Information Hid...

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13th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2017

ISBN: (纸本)9783319638584

the proceedings contain 103 papers. the special focus in this conference is on Intelligent Information Hiding and Multimedia signal processing. the topics include: Extraction of EEG components based on time - frequency blind source separation;an algorithm for asymmetric clipping detection based on parameter optimization;automatic facial age estimate based on convolution neural network;the application of eye tracking in education;adaptive multiple description depth image coding based on wavelet sub-band coefficients;adaptive histogram shifting based reversible data hiding;design and implementation of network video encryption system based on STM32 and AES algorithm;implementation of a drone-based video streamer;response selection of interview-based dialog system using user focus and semantic orientation;development and evaluation of Julius-compatible interface for kaldi ASR;voice conversion from arbitrary speakers based on deep neural networks with adversarial learning;evaluation of nonlinear tempo modification methods based on sinusoidal modeling;towards an interrogation speech manipulation detection method using speech fingerprinting;detection of singing mistakes from singing voice;a study of audio watermarking method using non-negative matrix factorization for a duet of different instruments;a wind noise detection algorithm for monitoring infrasound using Smartphone as a sensor device;study on speech representation based on spikegram for speech fingerprints;embedding multiple audio data using information misreading technique;an automatic detection method for Morse signal based on machine learning and building of a practical monitoring system for the small wind turbine.

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