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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

共 865 条记录，以下是301-310 订阅

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Temporal Characterization of small Arms Muzzle Flash in the Broadband Visible

Temporal Characterization of Small Arms Muzzle Flash in the ...

引用

conference on signal and data processing of small targets 2010

作者： Burke, Tom Bratlie, Duane Northrop Grumman Corp 2910 W Almondbury Dr Pasadena MD 21122 USA Northrop Grumman Corp Laurel MD 20707 USA

The authors have developed a simple system for characterizing the muzzle flash duration of common military small-arms ammunition as a feeder for system design configurations. This paper is a synopsis of the efforts an... 详细信息

ISBN: (纸本)9780819481627

关键词： small Arm Muzzle Flash Broadband Visible Characterization

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Needle Picking: A Sampling Based Track-Before-Detection Method for small targets

Needle Picking: A Sampling Based Track-Before-Detection Meth...

引用

conference on signal and data processing of small targets 2010

作者： Porikli, Fatih Mitsubishi Elect Res Labs Cambridge MA USA

ISBN: (纸本)9780819481627

We present a computationally efficient track-before-detect algorithm that achieves more than 50% true detection at 10-6 false alarm rate for pixel sized unknown number of multiple targets when the signal-to-noise ratio is less than 7dB. Without making any assumptions on the distribution functions, we select a small number of cells, so called as needles, and generate motion hypotheses using the target state transition model. We accumulate cell likelihoods along each hypothesis in the temporal window and append the accumulated values to the corresponding queues of the cell positions in the most recent image. We assign a target in case the queue maximum is greater than a threshold that produces the specified false alarm rate.

关键词： Automatic target detection track-before-detect radar ATR TBD

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Wide-Area Feature-Aided Tracking with Intermittent Multi-Sensor data

Wide-Area Feature-Aided Tracking with Intermittent Multi-Sen...

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conference on signal and data processing of small targets 2010

作者： Carthel, Craig Coraluppi, Stefano Bryan, Karna Arcieri, Gianfranco NATO Undersea Res Ctr I-19126 La Spezia Italy

ISBN: (纸本)9780819481627

This paper addresses multi-sensor surveillance where some sensors provide intermittent, feature-rich information. Effective exploitation of this information in a multi-hypothesis tracking context requires computationally-intractable processing with deep hypothesis trees. This report introduces two approaches to address this problem, and compares these to single-stage, track-while-fuse processing. The first is a track-before-fuse approach that provides computational efficiency at the cost of reduced track continuity;the second is a track-break-fuse approach that is computationally efficient without sacrificing track continuity. Simulation and sea trial results are provided.

关键词： Multi-sensor multi-target tracking maritime surveillance multi-stage data fusion

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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...

引用

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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small Moving targets Detection Using Outlier Detection Algorithms

Small Moving Targets Detection Using Outlier Detection Algor...

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conference on signal and data processing of small targets 2010

作者： Reljin, Natasa McDaniel, Samantha Pokrajac, Dragoljub Pejcic, Nebojsa Vance, Tia Lazarevic, Aleksandar Latecki, Longin Jan Delaware State Univ 1200 N DuPont Hwy Dover DE 19901 USA United Technol Res Ctr E Hartford CT 06108 USA Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9780819481627

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detection algorithms, namely the incremental connectivity-based outlier factor and the incremental local outlier factor to modified Stauffer-Grimson algorithm. Each video sequence is represented with spatial-temporal blocks extracted from the raw video. Principal component analysis (PCA) is applied on these blocks in order to reduce the dimensionality of extracted data. Extensive experiments performed on several data sets, including infrared sequences from OSU Thermal Pedestrian database repository, and data collected at Delaware State University from FLIR Systems PTZ cameras have shown promising results in using outlier detection for detection of small moving targets.

关键词： Incremental outlier detection connectivity-based outlier factor local outlier factor modified Stauffer-Grimson method motion detection principal component analysis outlier spatial-temporal blocks

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Multitarget sensor resolution model for arbitrary target numbers

Multitarget sensor resolution model for arbitrary target num...

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conference on signal and data processing of small targets 2010

作者： Svensson, Daniel Ulmke, Martin Danielsson, Lars Chalmers Univ Technol Dept Signals & Syst SE-41296 Gothenburg Sweden Fraunhofer FKIE Sensor Data & Informat Fus D-53343 Wachtberg Germany Volvo Car Corp Act Safety Elect SE-40508 Gothenburg Sweden

ISBN: (纸本)9780819481627

In many surveillance problems the observed objects are so closely spaced that they cannot always be resolved by the sensor(s). Typical examples for partially unresolved measurements are the surveillance of aircraft in formation, and convoy tracking for ground surveillance. Ignoring the limited sensor resolution in a tracking system may lead to degraded tracking performance, in particular unwanted track-losses. In this paper we extend the resolution model by Koch and van Keuk, given for two partially unresolved objects, to the case of arbitrary object numbers. We also derive the effects of the resolution model to the multi-target likelihood function and the possible data associations. Further, it is shown how the model can be integrated into the Joint Probabilistic data Association Filter (JPDAF).

关键词： Sensor resolution Target tracking Closely spaced targets Group targets Extended objects Negative information

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Maximum Likelihood Probabilistic data Association Tracker Applied to Bistatic Sonar data Sets

Maximum Likelihood Probabilistic Data Association Tracker Ap...

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conference on signal and data processing of small targets 2010

作者： Schoenecker, Steven Willett, Peter Bar-Shalom, Yaakov USN Naval Undersea Warfare Ctr Div Newport 1176 Howell St Newport RI 02841 USA Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819481627

In the early 1990's, the Maximum Likelihood Probabilistic data Association (ML-PDA) tracker was developed in a passive sonar framework, and subsequent research has shown it to be effective for tracking very low SNR targets. This was done with both active and passive sonar, for targets that have some given type of deterministic motion. Recent work has focused on applying ML-PDA to bistatic sonar data. Here, we apply ML-PDA in a sliding window implementation to three bistatic data sets used by the MSTWG (Multistatic Tracking Working Group): the SEABAR 2007 data set, the TNO Blind 2008 data set, and a new blind data set provided by Metron in 2009.

关键词： ML-PDA Multistatic Bistatic Active Sonar Target Tracking

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Algorithms for Distributed Chemical Sensor Fusion

Algorithms for Distributed Chemical Sensor Fusion

引用

conference on signal and data processing of small targets 2010

作者： Lundberg, Scott Paffenroth, Randy Yosinski, Jason Numerica Corperat Loveland CO 80538 USA

ISBN: (纸本)9780819481627

The fusion of Chemical, Biological, Radiological, and Nuclear (CBRN) sensor readings from both point and stand-off sensors requires a common space in which to perform estimation. In this paper we suggest a common representational space that allows us to properly assimilate measurements from a variety of different sources while still maintaining the ability to correctly model the structure of CBRN clouds. We design this space with sparse measurement data in mind in such a way that we can estimate not only the location of the cloud but also our uncertainty in that estimate. We contend that a treatment of the uncertainty of an estimate is essential in order to derive actionable information from any sensor system;especially for systems designed to operate with minimal sensor data. A companion paper(1) further extends and evaluates the uncertainty management introduced here for assimilating sensor measurements into a common representational space.

关键词： Chemical detection modeling uncertainty ambiguity data fusion filtering

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Probabilistic Tracking of Multiple Extended targets using Random Matrices

Probabilistic Tracking of Multiple Extended Targets using Ra...

引用

conference on signal and data processing of small targets 2010

作者： Wieneke, Monika Koch, Wolfgang Fraunhofer FKIE D-53343 Wachtberg Germany

ISBN: (纸本)9780819481627

Conventional tracking algorithms rely on the assumption that the targets under observation are point source objects. However, due to increasing resolution capabilities of modern sensors, the point source assumption is often not suitable and estimating the target extension becomes a crucial aspect. Recently, a Bayesian approach to extended target tracking using random matrices has been proposed. Within this approach, ellipsoidal object extensions are modeled by random matrices and treated as additional state variables to be estimated. However, only a single-target solution has been presented so far. In this work we present the multi-target extension of this approach. We derive a new variant of Probabilistic Multi-Hypothesis Tracking (PMHT) that simultaneously estimates the ellipsoidal shape and the kinematics of each target. For this purpose, the PMHT auxiliary function is extended by random matrices representing the target ellipsoids. Both, the ellipsoids and the kinematic states are iteratively optimized by specific Kalman filter formulae arising directly from the auxiliary function. The new method is demonstrated and evaluated by simulative examples.

关键词： PMHT Probabilistic Multi-Hypothesis Tracking Multi-Target Tracking Random Matrices Extended targets Group targets Target Clusters

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Probabilistic data Association in High Clutter Environments

Probabilistic Data Association in High Clutter Environments

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conference on signal and data processing of small targets 2010

作者： Tharmarasa, R. Lang, T. McDonald, Mike Kirubarajan, T. McMaster Univ Hamilton ON Canada Gen Dynam Canada Ottawa ON Canada Def Res & Dev Canada Ottawa ON Canada

ISBN: (纸本)9780819481627

data association is the key component in single or multiple target tracking algorithms with measurement origin. Probabilistic data Association (PDA), in which all validated measurements are associated probabilistically to the predicted estimate, is a well-known method to handle the measurement origin uncertainty. In PDA, the effect of measurement origin uncertainty is incorporated into the updated covariance by adding the spread of the innovations term. The updated covariance may become very large after few time steps in high clutter scenarios due to spread of the innovations term. Large covariance results in a large gate, which is used to limit the possible measurements that could have originated from the target. Hence, the track will be lost and estimate will just follow the prediction. Also, large gate will make the well-separated target assumption invalid, even if the targets are well-separated. Hence, after a few time steps all the targets in the surveillance region come under the same group, making the Joint Probabilistic data Association (JPDA). In this paper, adaptive gating techniques are proposed to avoid the steady increase in the updated covariance in high clutter. The effectiveness of the proposed techniques is demonstrated on simulated data.

关键词： Probabilistic data association high clutter target tracking measurement origin uncertainty adaptive gating

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