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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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small object hyperspectral detection from a low-flying UAV

Small object hyperspectral detection from a low-flying UAV

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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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Noise Suppressing and Direct Wave Removal in GPR data Based on Shearlet Transform 16

Noise Suppressing and Direct Wave Removal in GPR Data Based ...

引用

16th International conference on Ground Penetrating Radar (GPR)

作者： Wang, X. N. Liu, S. X. Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130026 Peoples R China

ISBN: (纸本)9781509051816

Ground penetrating radar (GPR) is often used to detect buried objects and evaluate structural condition. However, the direct wave and random noise often influence the arrival-time detection and the target-position location. We present a new application of Shearlet transform (ShT) to GPR data processing for direct wave removal and random noise suppression. ShT is a non-adaptive geometric-analysis technique, which has the properties of multi-directions and multi-scale, so it can show the optimal representations of signals in higher dimensions. the original GPR data is transformed to the ShT domain. the direct wave and the remaining GPR signal are effectively separated. While we eliminate the direct wave, the GPR signal is not damaged. the Shearlet coefficients of the GPR signal are relatively large, whereas random noises are relatively small. So we can use the threshold algorithm depending on different scales and directions in the ShT domain to suppress random noise. the GPR signal can be preserved very well and SNR is enhanced.

关键词： GPR Direct wave Random Noise Shearlet transform Suppression

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Maximum likelihood angle extractor in the presence of sea-surface multipath

Maximum likelihood angle extractor in the presence of sea-su...

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14th conference on signal and data processing of small targets

作者： Sinha, A Blair, WD Kirubarajan, T Bar-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06268 USA

ISBN: (纸本)0819444782

In the presence of sea-surface multipath monopulse radar signals from a low elevation target have three alternative paths in addition to the direct (radar-to-target) path due to reflections from the sea surface. the specular reflection causes significant signal fading. the diffuse reflection causes an approximately constant bias to the in-phase component of the monopulse ratio, which is the standard extractor of the direction of arrival (DOA) in the monopulse processing. the diffuse reflection also causes higher standard deviation to the in-phase component of the monopulse ratio. In this paper we propose a maximum likelihood (ML) angle extraction technique for low elevation targets of known average signal strength having a Rayleigh fluctuation. the results show that this method reduces the error of the estimated angle compared to the conventional monopulse ratio estimator. Subsequently, the ML angle extractor is modified for the unknown average signal strength case. this modified angle extractor has only a small performance degradation compared with the known average signal strength case, but it performs much better than the monopulse ratio based estimator. this angle extractor reduces the root mean square error (RMSE) by more than 50% in the signal processing stage when used in a low flying target (sea skimmer) tracking scenario.

关键词： Radar Reflection signal to noise ratio Specular reflections Error analysis signal processing Detection and tracking algorithms Silicon Analytical research Computer engineering

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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...

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

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

引用

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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Simulation assessment of RCS-aided multiple target tracking

Simulation assessment of RCS-aided multiple target tracking

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19th conference on signal and data processing of small targets

作者： Dunham, Darin T. Ehrman, Lisa. M. Blair, W. Dale Frost, Susan A. Vectraxx Inc Glen Allen VA 23059 USA Georgia Inst Technol Sensors & Electromagnet Applicat Lab Georgia Tech Res Inst Atlanta GA 30332 USA Vectraxx Inc Ft Collins CO 80521 USA

ISBN: (纸本)9780819468475

Closely-spaced (but resolved) targets pose a significant challenge for single-frame unique measurement-to-track data association algorithms. this is due to the similarity of the Mahalanobis distances between the closely-spaced measurements and tracks. Contrary to conventional wisdom, adding target feature information (e.g target amplitude) does not necessarily improve the probability of correctly assigning measurements to tracks. In this paper, the theoretical limitations of using radar cross section (RCS) data to aid in measurement-to-track association are reviewed. the results of a hi,h-fidelity simulation assessment of the benefits of RCS-aided measurement-to-track association (using the signal-to-Noise Ratio) are given and other possibilities for RCS-aided tracking are discussed. Namely, we show the analytical results of our investigation into using RCS information to determine the presence of merged measurements.

关键词： signal to noise ratio Target detection Sensors Switches Kinematics Radar Mahalanobis distance signal processing Time metrology Detection and tracking algorithms

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MHT tracking for crossing sonar targets

MHT tracking for crossing sonar targets

引用

19th conference on signal and data processing of small targets

作者： Willett, Peter Luginbuhl, Tod Giannopoulos, Evangelos Univ Connecticut Dept ECE Storrs CT 06269 USA Naval Undersea Warfare Ctr Newport RI 02841 USA

ISBN: (纸本)9780819468475

Sometimes radar targets cross and become unresolved;this is a concern, but with a reasonable track depth and an appropriate merged-measurement model the concern is considerably mitigated. Sonar targets, however, can become merged (in the same beam) for considerably longer, particularly with bearing-only measurements. In such cases the crossing times can be 100 scans long, and no reasonable depth exists for an multi-frame tracker that can "see" both ends of the merged period. Further, there is a demonstrable tendency for estimated targets to repel each other as they are being tracked. In this paper we explore the hypothesis-oriented multi-hypothesis tracker (HO-MHT), an MHT approach that uses the new "rollout" optimization insight and the to give an appropriate and cost-effective means to rank hypotheses, and also the PMHT tracker that operates on batches of scans with linear computational complexity in most quantities. We show results in terms of estimation error (RMSE), consistency (NEES) and computational effort in both linear and beam-space tracking scenarios.

关键词： tracking MHT PMHT unresolved targets sonar

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CNN-Based Analysis of Crowd Structure using Automatically Annotated Training data 16

CNN-Based Analysis of Crowd Structure using Automatically An...

引用

16th IEEE International conference on Advanced Video and signal Based Surveillance (AVSS)

作者： Zitouni, M. Sami Sluzek, Andrzej Bhaskar, Harish Khalifa Univ KU Ctr Autonomous Robot Syst KUCARS Abu Dhabi U Arab Emirates Warsaw Univ Life Sci SGGW Warsaw Poland Zero One Infin Consulting Serv Ltd Mississauga ON Canada

ISBN: (纸本)9781728109909

A CNN-based framework is presented for extracting and classifying from static images of crowd (acquired from surveillance systems) individuals, small groups and large groups. A novel approach to the network training has been investigated. Instead of manually outlined ground-truth data, we use automatic annotations by alternative baseline algorithms (which consider both motion and appearance). the proposed CNN detectors are initially trained over rather limited amounts of data. Nevertheless, the detectors are subsequently updated (fine-tuned) by using new batches of automatically annotated samples. those test samples are periodically acquired by the baseline algorithms from the future surveillance data. Fine-tuning is performed when noticeable differences appear between results by the CNN-detectors and the results of baseline algorithms (which may indicate changes in visual conditions, scenarios or updates in the baseline algorithms). We preliminarily demonstrate that satisfactory performances of CNN-based detectors can be achieved, even if the baseline algorithms have limited accuracy. Actually, it was noticed that fine-tuned CNN-detectors can be superior to the baseline algorithms used for automatic annotation of training data (even though the baseline algorithms process both static images and video-sequences). Since only static images are used once the detectors are fully trained, the presented solution can simplify complexity of systems automatically evaluating structure and behavior of crowds.

关键词： Detectors Training data Training Surveillance Feature extraction Motion segmentation Task analysis

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Tracking multiple targets with coordinated turn maneuvers

Tracking multiple targets with coordinated turn maneuvers

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17th conference on signal and data processing of small targets

作者： Morelande, MR Gordon, NJ Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

the problem of tracking multiple maneuvering targets is considered. the usual multiple model approach is adopted in which maneuvering target motion is modeled by assuming that the target motion at each point in time can be described by one of a finite set of dynamic models. Transitions between each mode of target motion are assumed to be Markovian. Target positions are measured in polar coordinates leading to a nonlinear measurement equation. A particle filter is proposed as a solution to the problem. the proposed algorithm seeks to improve upon the performance of a previously proposed particle filter by using measurement-directed proposals and exploiting the structure of the measurement likelihood. the performance analysis focuses on targets which perform coordinated turn maneuvers. An improved model for target motion in this regime is suggested. the performance analysis, using Monte Carlo simulations, demonstrates the improved performance of the proposed algorithm compared to the previously proposed particle filter and the standard Gaussian approximation, the IMM-JPDAF.

关键词： Motion models Particles Detection and tracking algorithms Particle filters Monte Carlo methods Target detection Stochastic processes Sensors Electronic filtering data modeling

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