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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251-260 订阅

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Information-based data Prioritization in Distributed Tracking Systems

Information-based Data Prioritization in Distributed Trackin...

引用

conference on signal and data processing of small targets

作者： Coult, Nick Knight, J. Nate Leed, Woody Danford, Scott Paffenroth, Randy Poore, Aubrey Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819490711

Effective multi-sensor, multi-target, distributed composite tracking requires the management of limited network bandwidth. In this paper we derive from first principles a value of information for measurements that can be used to sort the measurements in order from most to least valuable. We show the information metric must account for the models and filters used by the composite tracking system. We describe how this value of information can be used to optimize bandwidth utilization and illustrate its effectiveness using simulations that involve lossy and latent network models.

关键词： Kullback-Leibler Divergence data prioritization distributed tracking

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data Modeling for Nonlinear Track Prediction of targets through Obscurations

Data Modeling for Nonlinear Track Prediction of Targets thro...

引用

conference on signal and data processing of small targets

作者： Jaenisch, Holger Handley, James Johns Hopkins Univ Zanvyl Krieger Sch Baltimore MD 21218 USA Licht Strahl Engn INC Toney AL 35773 USA

ISBN: (纸本)9780819490711

A novel algorithm for predicting target tracks through obscurations is introduced. This prediction method uses radar ground track indicators and the hidden transfer function (HTF) to predict future target locations. The HTF method is described in detail, and results provided that quantify track accuracy, forecast accuracy, and the percentage of tracks exiting an obscuration occurring that occur within the forecasted region. Five different classifier methods are shown for labeling short segments of track history. Each classifier method is scored and significance testing used to determine that the data Model and SMART lookup table (LUT) were significantly better than the other classifier approaches.

关键词： data Modeling Random Forest Spatial Voting Predictive lookup tables Smart lookup tables

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Estimating trackability

Estimating trackability

引用

conference on signal and data processing of small targets

作者： Van Zandt, James R. MITRE Corp. (United States)

ISBN: (纸本)9780819490711

Target tracking sensors and algorithms are usually evaluated using Monte Carlo simulations covering a large parameter space. We show a tracker for which the evaluation can be greatly simplified. We apply it to the one dimensional crossing track problem (e. g. ground target tracking in a dense target environment, where targets are confined to a road), and estimate the probability that measurements and tracks are incorrectly associated. If only position is measured, we find the probability of a misassociation is a very simple analytic function of the relevant parameters: measurement standard deviation, measurement interval, target density, and target acceleration. For normally distributed target velocities, the average time between misassociations also has a simple form. We suggest roll-up metrics for tracking sensors and tracking problems.

关键词： tracking data association performance evaluation performance metrics

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Measurement level AIS/Radar Fusion for Maritime Surveillance

Measurement level AIS/Radar Fusion for Maritime Surveillance

引用

conference on signal and data processing of small targets

作者： Habtemariam, Biruk K. Tharmarasa, R. Meger, Eric Kirubarajan, T. McMaster Univ ECE Dept Hamilton ON Canada ExactEarth Ltd Cambridge ON Canada

ISBN: (纸本)9780819490711

Using the Automatic Identification System (AIS) ships identify themselves intermittently by broadcasting their location information. However, traditionally radars are used as the primary source of surveillance and AIS is considered as a supplement with a little interaction between these data sets. The data from AIS is much more accurate than radar data with practically no false alarms. But unlike the radar data, the AIS measurements arrive unpredictably, depending on the type and behavior of a ship. The AIS data includes target IDs that can be associated to initialized tracks. In multitarget maritime surveillance environment, for some targets the revisit interval form the AIS could be very large. In addition, the revisit intervals for various targets can be different. In this paper, we proposed a joint probabilistic data association based tracking algorithm that addresses the aforementioned issues to fuse the radar measurements with AIS data. Multiple AIS IDs are assigned to a track, with probabilities updated by both AIS and radar measurements to resolve the ambiguity in the AIS ID source. Experimental results based on simulated data demonstrate the performance the proposed technique.

关键词： maritime target tracking automatic identification system (AIS) probabilistic data association data association

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Three plot correlation-based small infrared target detection in dense sun-glint environment for infrared search and track

Three plot correlation-based small infrared target detection...

引用

conference on signal and data processing of small targets

作者： Kim, Sungho Choi, Byungin Kim, Jieun Kwon, Soon Kim, Kyung-Tae Yeungnam Univ LED IT Fus Technol Res Ctr 214-1 Dae Dong Gyeonsan Si Gyeongbuk 712749 South Korea Samsung Thales Co Elect Opt Lab Yongin South Korea Agcy Def Dev Taejon South Korea Daegu Gyeongbuk Inst Sci & Technol Taegu South Korea POSTECH Electmagnet Technol Lab Pohang South Korea

ISBN: (纸本)9780819490711

This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust detection performance of the proposed method via real infrared test sequences including synthetic targets.

关键词： IRST Sun-glint False alarms Three plot correlation

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Space-time signal processing for distributed pattern detection in sensor networks

Space-time signal processing for distributed pattern detecti...

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conference on signal and data processing of small targets

作者： Paffenroth, Randy C. du Toit, Philip C. Scharf, Louis L. Jayasumana, Anura P. Banadara, Vidarshana Nong, Ryan Numerica Corp Loveland CO 80528 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA

ISBN: (纸本)9780819490711

We present a theory and algorithm for detecting and classifying weak, distributed patterns in network data that provide actionable information with quanti fiable measures of uncertainty. Our work demonstrates the effectiveness of space-time inference on graphs, robust matrix completion, and second order analysis for the detection of distributed patterns that are not discernible at the level of individual nodes. Motivated by the importance of the problem, we are specifically interested in detecting weak patterns in computer networks related to Cyber Situational Awareness. Our focus is on scenarios where the nodes (terminals, routers, servers, etc.) are sensors that provide measurements (of packet rates, user activity, central processing unit usage, et c.) that, when viewed independently, cannot provide a de finitive determination of the underlying pattern, but when fused with data from across the network both spatially and temporally, the relevant patterns emerge. The approach is applicable to many types of sensor networks including computer networks, wireless networks, mobile sensor networks, and social networks, as well as in contexts such as databases and disease outbreaks.

关键词： Matrices Sensor networks Algorithm development Computer networks signal processing Sensors Algorithms Time metrology Principal component analysis Social networks

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Prediction, Tracking, and Retrodiction for Path-Constrained targets

Prediction, Tracking, and Retrodiction for Path-Constrained ...

引用

conference on signal and data processing of small targets

作者： Krishanth, K. Tharmarasa, R. Kirubarajan, T. Valin, P. Meger, E. McMaster Univ Hamilton ON Canada Def Res & Dev Ottawa ON Canada ExactEarth Ltd Cambridge ON Canada

ISBN: (纸本)9780819490711

This paper presents algorithms for prediction, tracking, and retrodiction for targets whose motion is constrained by external conditions (e. g., shipping lanes, roads). The targets are moving along a path, defined by way-points and segments. Measurements are obtained by sensors at low revisit rates (e. g., spaceborne). Existing tracking algorithms assume that the targets follow the same motion model between successive measurements, but in a low revisit rate scenario targets may change the motion model between successive measurements. The proposed prediction algorithm addresses this issue by considering possible motion model whenever targets move to a different segment. Further, when a target approaches a junction, it has the possibility to travel into one of the multiple segments connected to that junction. To predict the probable locations, multiple hypotheses for segments are introduced and a probability is calculated for each segment hypothesis. When measurements become available, segment hypothesis probability is updated based on a combined mode likelihood and a sequential probability ratio test is carried out to reject the hypotheses. Retrodiction for path constrained targets is also considered, because in some scenarios it is desirable to find out the target's exact location at some previous time (e. g., at the time of an oil leakage). A retrodiction algorithm is also developed for path constrained targets so as to facilitate motion forensic analysis. Simulation results are presented to validate the proposed algorithm.

关键词： prediction target tracking retrodiction path-constrained targets segment hypothesis

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Maximum Likelihood Probabilistic data Association (ML-PDA) Tracker Implemented in Delay-Bearing Space Applied to Multistatic Sonar data Sets

Maximum Likelihood Probabilistic Data Association (ML-PDA) T...

引用

conference on signal and data processing of small targets

作者： Schoenecker, Steven Willett, Peter Bar-Shalom, Yaakov USN 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)9780819490711

The Maximum Likelihood Probabilistic data Association (ML-PDA) tracker is an algorithm that has been shown to work well against low-SNR targets in an active multistatic framework with multiple transmitters and multiple receivers. In this framework, measurements are usually received in time-bearing space. Prior work on ML-PDA implemented the algorithm in Cartesian measurement space - this involved converting the measurements and their associated covariances to (x, y) coordinates. The assumption was made that Gaussian measurement error distributions in time-bearing space could be reasonably approximated by transformed Gaussian error distributions in Cartesian space. However, for data with large measurement azimuthal uncertainties, this becomes a poor assumption. This work compares results from a previous study that applied ML-PDA in a Cartesian implementation to the Metron 2009 simulated dataset against ML-PDA applied to the same dataset but with the algorithm implemented in time-bearing space. In addition to the Metron dataset, a multistatic Monte Carlo simulator is used to create data with properties similar to that in the Metron dataset to statistically quantify the performance difference of ML-PDA operating in Cartesian measurement space against that of ML-PDA operating in time-bearing space.

关键词： ML-PDA ML-PMHT Multistatic Bistatic Active Sonar Target Tracking Expectation Maximization EM

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Detecting Clustered Chem/Bio signals in Noisy Sensor Feeds Using Adaptive Fusion

Detecting Clustered Chem/Bio Signals in Noisy Sensor Feeds U...

引用

conference on signal and data processing of small targets

作者： Lundberg, Scott Calderon, Chris Paffenroth, Randy Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819490711

Chemical and biological monitoring systems are faced with the challenge of detecting weak signals from contaminants of interest while at the same time maintaining extremely low false alarm rates. We present methods to control the number of false alarms while maintaining power to detect;evaluating these methods on a fixed sensor grid. Contaminants are detected using signals produced from underlying sensor-specific detection algorithms. By learning from past data, an adaptive background model is constructed and used with a multi-hypothesis testing method to control the false alarm rate. Detection methods for chemical/biological releases often depend on specific models for release types and missed detection rates at the sensors. This can be problematic in field situations where environment specific effects can alter both a sensor's false alarm and missed detection characteristics. Using field data, the false alarm statistics of a given sensor can be learned and used for inference;however the missed detection statistics for a sensor are not observable while in the field. As a result, we pursue methods that do not rely on accurate estimates of a sensor's missed detection rate. This leads to the development of the Adaptive Regions Method that under certain assumptions is designed to conservatively control the expected rate of false alarms produced by a fusion system over time, while maintaining power to detect.

关键词： chemical/biological detection false discovery rate data ambiguity data fusion sensor network Fisher's Method multiple hypothesis testing

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Ambiguous data Association and Entangled Attribute Estimation

Ambiguous Data Association and Entangled Attribute Estimatio...

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conference on signal and data processing of small targets

作者： Trawick, David J. Du Toit, Philip C. Paffenroth, Randy C. Norgard, Gregory J. Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819490711

This paper presents an approach to attribute estimation incorporating data association ambiguity. In modern tracking systems, time pressures often leave all but the most likely data association alternatives unexplored, possibly producing track inaccuracies. Numerica's Bayesian Network Tracking database, a key part of its Tracker Adjunct Processor, captures and manages the data association ambiguity for further analysis and possible ambiguity reduction/resolution using subsequent data. Attributes are non-kinematic discrete sample space sensor data. They may be as distinctive as aircraft ID, or as broad as friend or foe. Attribute data may provide improvements to data association by a process known as Attribute Aided Tracking (AAT). Indeed, certain uniquely identifying attributes (e. g. aircraft ID), when continually reported, can be used to define data association (tracks are the collections of observations with the same ID). However, attribute data arriving infrequently, combined with erroneous choices from ambiguous data associations, can produce incorrect attribute and kinematic state estimation. Ambiguous data associations define the tracks that are entangled with each other. Attribute data observed on an entangled track then modify the attribute estimates on all tracks entangled with it. For example, if a red track and a blue track pass through a region of data association ambiguity, these tracks become entangled. Later red observations on one entangled track make the other track more blue, and reduce the data association ambiguity. Methods for this analysis have been derived and implemented for efficient forward filtering and forensic analysis.

关键词： Attribute Estimation Attribute Aided Tracking Classification Aided Tracking Multiple Hypothesis Tracking Uncertainty Management Dynamic Bayesian Networks Late data

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