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共 168 条记录，以下是21-30 订阅

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Detection of small objects in multi-layered infrared images

Detection of small objects in multi-layered infrared images

引用

20th conference on signal and data processing of small targets

作者： Wang, Jing Bao, Shangqi Ralph, Jason F. Goulermas, John Y. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9780819471604

this paper uses super-resolution methods to detect small objects in infrared image sequences from a simulated airborne platform, using image registration techniques for automatic sightline stabilisation. the scene consists of multiple layers, corresponding to a static background scene and layers of cloud cover at varying heights. the motivation is to evaluate the performance of super-resolution methods in the presence of three-dimensional structured infrared clutter.

关键词： object detection super-resolution automatic sightline stabilisation infrared imagery clouds

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Robust scale invariant small target detection using the Laplacian scale-space theory

Robust scale invariant small target detection using the Lapl...

引用

20th conference on signal and data processing of small targets

作者： Kim, Sungho Yang, Yukyung Lee, Joohyoung Park, Yongchan Agcy Defense Dev Taejon 305600 South Korea

ISBN: (纸本)9780819471604

this paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). the conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. the scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. the scale invariant feature can detect different sizes of targets robustly. Experimental results with real FUR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.

关键词： small target robust detection varying size scale invariant scale-space

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small object hyperspectral detection from a low-flying UAV

Small object hyperspectral detection from a low-flying UAV

引用

20th conference on signal and data processing of small targets

作者： Murray-Krezan, J. Neumann, J. G. Leathers, R. A. USN Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819471604

small object detection with a low false alarm rate remains a challenge for automated hyperspectral detection algorithms when the background environment is cluttered. In order to approach this problem we are developing a compact hyperspectral sensor that can be fielded from a small unmanned airborne platform. this platform is capable of flying low and slow, facilitating the collection of hyperspectral imagery that has a small ground-sample distance (GSD) and small atmospheric distortion. Using high-resolution hyperspectral imagery we simulate various ranges between the sensor and the objects of interest. this numerical study aids in analysis of the effects of stand-off distance on detection versus false alarm rates when using standard hyperspectral detection algorithms. Preliminary experimental evidence supports our simulation results.

关键词： hyperspectral HSI GSD small object detection

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Assurance regions in tracking

Assurance regions in tracking

引用

20th conference on signal and data processing of small targets

作者： Sworder, David D. Boyd, John E. Hutchins, R. G. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Cub Def Applicat San Diego CA 92186 USA Naval Postgrad Sch Dept EECE Monterey CA 93943 USA

ISBN: (纸本)9780819471604

An assurance region at level p, A(P=p), is an area in motion space that contains the target with assigned probability p. It is on the basis of A(P=p) that an action is taken or a decision made. Common model-based trackers generate a synthetic distribution function for the kinematic state of the target. Unfortunately, this distribution is very coarse, and the resulting A(P=p) lack credibility. It is shown that a, map-enhanced, multiple model algorithm reduces the tracking, error and leads to a compact assurance region.

关键词： nonlinear filters target tracking data fusion

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Concurrent MAP data association and absolute bias estimation with an arbitrary number of sensors

Concurrent MAP data association and absolute bias estimation...

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20th conference on signal and data processing of small targets

作者： Kragel, Bret D. Danford, Scott Poore, Aubrey B. Numerica Corp Loveland CO USA

ISBN: (纸本)9780819471604

Bias estimation using objects with unknown data association requires concurrent estimation of both biases and optimal data association. this report derives maximum a posteriori (MAP) data association likelihood ratios for concurrent bias estimation and data association based on sensor-level track state estimates and their joint error covariance. Our approach is unique for two reasons. First, we include a bias prior that allows estimation of absolute sensor biases, rather than just relative biases. Second, we allow concurrent bias estimation and association for an arbitrary number of sensors. the two-sensor likelihood ratio is derived as a special case of the general M-sensor result.

关键词： multiple target tracking MAP bias estimation data association assignment problems track-to-track association

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Robust method for detecting an infrared small moving target based on the facet-based model

Robust method for detecting an infrared small moving target ...

引用

20th conference on signal and data processing of small targets

作者： Lee, Hwal-Suk Kim, Seokkwon Park, Dong-Jo Kim, Jieun Park, Changhan Korea Adv Inst Sci & Technol Sch EECS 373-1 Guseong Dong Taejon 305701 South Korea Agcy Def Dev Taejon South Korea SAMSUNG THALES Co Ltd Adv Technol R&D Ctr Yongin 446712 Gyeonggi South Korea

ISBN: (纸本)9780819471604

In this paper, a new condition for the target is proposed to increase the robustness of the facet-based detection method for zero-mean Gaussian noise. In the proposed algorithm, the pixels detected from the maximum extremum condition are checked further to discern if they are false maximum points in the proposed scheme. the experimental results show that the proposed algorithm is much more robust for zero-mean Gaussian noise than the conventional detection method.

关键词： infrared image small target detection cubic facet model Gaussian noise

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Aspect aware UAV localization

Aspect aware UAV localization

引用

20th conference on signal and data processing of small targets

作者： Berger, Christian R. Zhou, Shengli Willett, Peter Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819471604

We consider target detection and tracking of stealthy targets. these targets can be characterized by a, strong aspect dependence leading to difficult detectability without a multi-static setup. Even in a multi-static setup only sensors in a, certain zone can detect the return signal, if the the aspect dependent return has a small bandwidth. We propose a solution based on a large number of simple sensor, as using many receivers increases the probability of detection. the sensors are simple in the sense that they only transmit binary detection results to a fusion center that has comparatively deep capabilities, and they do not need to know their own position or communicate with other sensors. We characterize the target position estimation performance using the Cramer-Rao bound and simulation results, considering uncertainty in nuisance parameters as the sensor positions or the specifics of the aspect dependence. We suggest a data collection protocol that includes locating sensors that detect the target and has low communication complexity. As a, novelty we also include information about "non-localized" sensors, as sensors which do not detect the target stay quiet to save bandwidth and energy, therefore are not known to the fusion center except via knowledge of the deployed sensor density and deployment region.

关键词： Sensors Target detection signal detection Binary data Unmanned aerial vehicles Receivers Fusion energy signal to noise ratio Error analysis data communications

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Discriminating small extended targets at sea from clutter and other classes of boats in infrared and visual light imagery

Discriminating small extended targets at sea from clutter an...

引用

20th conference on signal and data processing of small targets

作者： van den Broek, Sebastiaan P. Bouma, Henri Degache, Marianne A. C. TNO Def Secur & Safety NL-2509 JG The Hague Netherlands

ISBN: (纸本)9780819471604

Operating in a coastal environment, with a multitude of boats of different sizes, detection of small extended targets is only one problem. A further difficulty is in discriminating detections of possible threats from alarms due to sea and coastal clutter, and from boats that are neutral for a specific operational task. Adding target features to detections allows filtering out clutter before tracking. Features can also be used to add labels resulting from a classification step. Both will help tracking by facilitating association. Labeling and information from features can be an aid to an operator, or can reduce the number of false alarms for more automatic systems. In this paper we present work on clutter reduction and classification of small extended targets from infrared and visual light imagery. Several methods for discriminating between classes of objects were examined, with an emphasis on less complex techniques, such as rules and decision trees. Similar techniques can be used to discriminate between targets and clutter, and between different classes of boats. Different features are examined that possibly allow discrimination between several classes. data recordings are used, in infrared and visual light, with a range of targets including rhibs, cabin boats and jet-skis.

关键词： classification clutter reduction target recognition small targets boats jet-ski

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

Efficiency and sensitivity of methods for assessing ambiguit...

引用

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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the probability of misassociation between neighboring targets

The probability of misassociation between neighboring target...

引用

20th conference on signal and data processing of small targets

作者： Areta, Javier A. Bar-Shalom, Yaakov Rothrock, Ronald Univ Connecticut Storrs CT 06269 USA Sparta Syst Inc Arlington VA 22209 USA

ISBN: (纸本)9780819471604

this paper presents procedures to calculate the probability that the measurement originating from an extraneous target will be (mis)associated with a target of interest for the cases of Nearest Neighbor and Global association. It is shown that these misassociation probabilities depend., under certain assumptions, on a particular - covariance weighted - norm of the difference between the targets' predicted measurements. For the Nearest Neighbor association, the exact solution, obtained for the case of equal innovation covariances, is based on a noncentral chi-square distribution. An approximate solution is also presented for the case of unequal innovation covariances. For the Global case an approximation is presented for the case of "similar" innovation covariances. In the general case of unequal innovation covariances where this approximation fails, an exact method based on the inversion of the characteristic function is presented. the theoretical results, confirmed by Monte Carlo simulations, quantify the benefit of Global vs. Nearest Neighbor association. these results are applied to problems of single sensor as well as centralized fusion architecture multiple sensor tracking.

关键词： Monte Carlo methods Sensors Matrices Target detection Detection and tracking algorithms Radar signal to noise ratio Algorithm development Distance measurement Sensor fusion

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