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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 1999"

共 764 条记录，以下是201-210 订阅

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Hot starts for track maintenance in multisensor-multitarget tracking

Hot starts for track maintenance in multisensor-multitarget ...

引用

conference on signal and data processing of small targets 1997

作者： Poore, AB Colorado State Univ. (United States)

ISBN: (纸本)0819425850

Large classes of data association problems in multiple hypothesis tracking applications involving multiple and single sensor systems can be formulated as multidimensional assignment problems, which are then fundamental in both track initiation and maintenance. In particular, track maintenance uses an N-frame moving window over the scans of data from multiple sensors giving rise to the repeated solution of (N + 1)-dimensional assignment problems. Due to the complexity of these NP-hard problems and the fact that one must solve these problems to the noise level in the problem, it is most advantageous to take advantage of a previously solved problem over a previous window. This work explores this issue in track maintenance.

关键词： Sensors Computer programming Algorithm development Detection and tracking algorithms Surveillance data fusion Mathematics Patents Scientific research Surveillance systems

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Multitarget mixture reduction algorithm with incorporated target existence recursions

Multitarget mixture reduction algorithm with incorporated ta...

引用

conference on signal and data processing of small targets 2000

作者： Ristic, B Arulampalam, S Def Sci & Technol Org Surveillance Res Div Salisbury SA 5108 Australia

ISBN: (纸本)0819436747

The paper derives a deferred logic data association algorithm based on the mixture reduction approach originally due to Salmond [SPIE vol.1305, 1990]. The novelty of the proposed algorithm provides the recursive formulae for both data association and target existence (confidence) estimation, thus allowing automatic track initiation and termination. The track initiation performance of the proposed filter is investigated by computer simulations. It is observed that at moderately high levels of clutter density the proposed filter initiates tracks more reliably than its corresponding PDA filter. An extension of the proposed filter to the multi-target case is also presented. In addition, the paper compares the track maintenance performance of the MR algorithm with an MHT implementation.

关键词： target tracking data association track initiation multiple hypothesis

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A complex-domain adaptive order statistic filter and its application to signal detection in non-Gaussian noise and clutter

A complex-domain adaptive order statistic filter and its app...

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conference on signal and data processing of small targets 2010

作者： Fernandez, Manuel Aridgides, Tom Lockheed Martin MS2 Syracuse NY 13221 USA

ISBN: (纸本)9780819481627

This paper presents an adaptive Order-Statistic Filter (OSF) that can operate in the real and the complex data domains to maximize the gain in signal to noise and/or clutter ratio. This distribution-independent non-linear filter approximates the optimal filter when the background is not Gaussian (e.g., speckle-type clutter, Gamma noise, etc.), producing a "Gaussianized" residual that ensures the near-optimality of subsequent processing stages that assume Gaussian statistics (e.g., background-normalization/CFAR, signal classification, etc.). Furthermore, the residual resulting from an adaptive OSF stage can implicitly be re-filtered, driving the ensuing residuals ever closer to being Gaussian-distributed. The output of such recursive version of our adaptive OSF can thus approximate optimality in the maximum likelihood sense (e.g., in the case of signal detection, by maximizing the probability of detection while minimizing the probability of false alarm).

关键词： Order statistic adaptive complex non-linear non-Gaussian recursive filter beamforming

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RADAR TARGET DISCRIMINATION USING WAVELET TRANSFORMS

RADAR TARGET DISCRIMINATION USING WAVELET TRANSFORMS

引用

conference on signal and data processing of small targets 1994

作者： VARAD, R MITRE CORP BEDFORDMA 01730

ISBN: (纸本)0819415391

In this paper, we describe a new approach to radar target discrimination. Specifically, we will apply it to the problem of exo-atmospheric object discrimination from UHF radar returns. The method uses wavelet transforms, pattern recognition techniques such as feature spaces, vectors and neural net classifiers. Feature vectors for each object are constructed from the wavelet transforms of the input data samples. The feature vectors are based on energies at each scale of the wavelet transforms and therefore effectively circumvent the problem of noncoherence due to target and ionospheric effects. This is a very important consideration when coherent signal processing is not feasible. The feature vectors are input to an unsupervised learning neural network for classification of the objects. In unsupervised learning, the network output is not forced towards a desired response for each input pattern but allowed to learn proximity to past input patterns. Limited results from simulated radar cross-section (RCS) data indicate that most objects can be correctly classified. The results also show that the overall scheme is quite immune to fair amounts of gaussian as well as uniformly distributed noise. Further efforts are under way to test the methodology against real object data as well as more extensive simulations.

关键词： Wavelets Neural networks Radar Wavelet transforms signal to noise ratio signal processing Neurons Image classification Doppler effect Machine learning

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Tracking of low-observable small targets against clutter

Tracking of low-observable small targets against clutter

引用

13th conference on signal and data processing of small targets

作者： Shue, L Venkateswarlu, R Lim, ET Nanyang Technol Univ Ctr Signal Proc Singapore 639798 Singapore

ISBN: (纸本)0819441872

Detection and tracking of low-observable moving targets against heavy clutter in a sequence of infrared images is an important research area. The focus of research in this area is to reliably pick up the most potential targets, track the targets with varying speed and direction, and at the same time reduce the false alarm rate to ala acceptable level. However, there is no single method that works equally well hi all situations. This paper presents an integrated algorithm based on area-correlation tracker (ACT) and Kalman filter for improving ACT performance for targets with varying speed and direction. Divergence and loss of target when the target is stationary are the two typical problems associated with ACT. In our algorithm, we propose to overcome these shortcomings by introducing an online procedure for updating (or not updating in the case of occlusions) the reference template, in conjunction with linear predictions by using a Kalman filter.

关键词： small target image registration area-correlation Kalman filter

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Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets

Detection of unusual trajectories using multi-objective evol...

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25th SPIE conference on signal and data processing of small targets

作者： Smolinski, Tomasz G. Newell, Trevor McDaniel, Samantha Pokrajac, David Delaware State Univ Dept Comp & Informat Sci Dover DE 19901 USA

ISBN: (纸本)9780819497079

Detection of unusual trajectories of moving objects (e.g., people, automobiles, etc.) is an important problem in many civilian and military surveillance applications. In this work, we propose a multi-objective evolutionary algorithms and rough sets-based approach that breaks down 2-dimensional trajectories into a set of additive components, which then can be used to build a classifier capable of recognizing typical, but yet unseen trajectories, and identifying those that seem suspicious.

关键词： Detection of Unusual Trajectories data Mining Evolutionary Algorithms Rough Sets

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Pipe-lined algorithm and parallel architecture for real-time detection of sparse small objects in images

Pipe-lined algorithm and parallel architecture for real-time...

引用

conference on signal and data processing of small targets 1997

作者： Ito, MR Duong, S McFee, JE Russell, KL Univ. of British Columbia (Canada) Defence Research Establishment Suffield (Canada)

ISBN: (纸本)0819425850

A pipe-lined algorithm and parallel architecture is under development for real time detection of sparse small objects in images. Monochromatic images from an airborne active infrared scanner, images from a low-altitude aircraft-mounted multispectral scanner, and passive infrared imagery obtained from cameras mounted on ground vehicle are the image types intended for the application of this system to the detection of minefields. The paper briefly describes the characteristics of these three different kinds of image sensors and the operating environments. The general image processing system architecture and the functions of each of the components are also presented The feature selection and algorithm adaptations for each of the image classes are described. Preliminary results obtained from an experimental system consisting of a small transputer network and array processors are discussed.

关键词： remote minefield detection pipe-lined algorithm parallel architecture real-time image analysis

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Comparison of data Reduction Techniques Based on SVM Classifier and SVR Performance

Comparison of Data Reduction Techniques Based on SVM Classif...

引用

conference on signal and data processing of small targets

作者： Zhao, Dave Georgescu, Ramona Willett, Peter Univ Connecticut ECE Dept Storrs CT 06269 USA

ISBN: (纸本)9780819487476

In this work, we applied several data compression techniques to simulated data and the Turbofan engine degradation simulation data set from NASA, with the goal of comparing their performance when coupled with the Support Vector Machine (SVM) classifier and the SVM regression (SVR) predictor. We consistently attained correct rates in the neighborhood of 90% for simulated data set, with the Principal Component Analysis (PCA), Sparse Reconstruction by Separable Approximation (SpaRSA) and Partial Least Squares (PLS) having a slight edge over the other data reduction methods for data classification. We achieved 22% error rate with SRM for the Turbofan data set 1 and 40% error rate with PCA for Turbofan data set 2. Throughout the tests we have performed, PCA proved to be the best data reduction method.

关键词： data reduction PCA PLS SRM OMP SpaRSA Classification Regression SVM SVR

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CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOWN NON-targets FOR PLDA-SVM SCORING

CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOW...

引用

IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Rao, Wei Mak, Man-Wai Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479928934

Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers and claimants only. We have previously demonstrated that better performance can be achieved by incorporating the information of background speakers in the scoring process via speaker-dependent SVMs. This is achieved by defining a PLDA score space with dimension equal to the number of training i-vectors for each target speaker. The new protocol in NIST 2012 SRE permits systems to use the information of other target-speakers (called known non-targets) in each verification trial. In this paper, we exploit this new protocol to enhance the performance of PLDA-SVM scoring by using the score vectors of both known and unknown non-targets as the impostor class data to train the speaker-dependent SVMs. Because some target speakers have one enrollment utterance only, which results in severe imbalance in the speaker-and impostor-class data for SVM training. This paper shows that if the enrollment utterance is sufficiently long, a number of target-speaker i-vectors can be generated by an utterance partitioning and resampling technique, resulting in much better scoring SVMs. Results on NIST 2012 SRE demonstrate the advantages of pooling the known and unknown non-targets for training the SVMs and that the resampling techniques can help the SVM training algorithm to find better decision boundaries for those speakers with only a small number of enrollment utterances.

关键词： I-vectors probabilistic linear discriminant analysis empirical kernel maps likelihood ratio kernels NIST 2012 SRE

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data association in target tracking using priority queues

Data association in target tracking using priority queues

引用

17th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T Farooq, M McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

data association is one of the main components of target tracking. While, in its simplest form, data association links a list of tracks to a list of measurements or links two lists of measurements (2-D association), the more complex problem involves assignment of multiple number of such lists (S-D association where S > 3). In target tracking, the presence of false detections (false alarms) and the absence of detections from some targets (missed detections) complicate the problem of data association further. In this work, we explore the possibility of applying track ordering in priority queues to solve the association problem more efficiently. The basic component of our algorithm is to form priority queues by permutations of the tracks. Each queue is served on a first-come-first-served basis, i.e., each track is assigned to the best measurement available based on its turn in the queue. It can be shown that the best solution to the 2-D problem can be obtained from one of these queues. However, the solution is computationally expensive even for a moderate number of targets. In this paper we show that due to redundancy only a small fraction of the total number of permutations is required to be evaluated to obtain the best solution.

关键词： Multisensor-multitarget tracking 2-D assignment algorithms priority queue m-best solutions

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