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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301-310 订阅

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A Tracker Adjunct processing System for Reconsideration of Firm Tracker Decisions

A Tracker Adjunct Processing System for Reconsideration of F...

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conference on signal and data processing of small targets 2010

作者： Trawick, David J. Slocumb, Benjamin J. Paffenroth, Randy C. Numerica Corp Loveland CO USA

ISBN: (纸本)9780819481627

Most modern maximum likelihood multiple target tracking systems (e. g., Multiple Hypothesis Tracking (MHT) and Numerica's Multiple Frame Assignment (MFA)) need to determine how to separate their input measurements into subsets corresponding to the observations of individual targets. These observation sets form the tracks of the system, and the process of determining these sets is known as data association. Real-time constraints frequently force the use of only the maximum likelihood choice for data association (over some time window), although alternative data association choices may have been considered in the process of choosing the most likely. This paper presents a Tracker Adjunct processing (TAP) system that captures and manages the uncertainty encountered in making data association decisions. The TAP combines input observation data and the data association alternatives considered by the tracker into a dynamic Bayesian network (DBN). The network efficiently represents the combined alternative tracking hypotheses. Bayesian network evidence propagation methods are used to update the network in light of new evidence, which may consist of new observations, new alternative data associations, newly received late observations, hypothetical connections, or other flexible queries. The maximum likelihood tracking hypothesis can then be redetermined, which may result in changes to the best tracking hypothesis. The recommended changes can then be communicated back to the associated tracking system, which can then update its tracks. In this manner, the TAP's interpretation makes the firm, fixed (formerly maximum likelihood) decisions of the tracker "softer," i.e., less absolute. The TAP can also assess (and reassess) track purity regions by ambiguity level. We illustrate the working of the TAP with several examples, one in particular showing the incorporation of critical, late or infrequent data. These data are critical in the sense that they are very valuable in resolving

关键词： Multiple Hypothesis Tracking Uncertainty Management Dynamic Bayesian Networks Late data Association

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Identifying chemicals from their Raman spectra using minimum description length

Identifying chemicals from their Raman spectra using minimum...

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conference on signal and data processing of small targets 2010

作者： Palkki, Ryan D. Lanterman, Aaron D. Georgia Inst Technol Ctr Signal & Image Proc Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9780819481627

Raman spectroscopy has been a powerful means of chemical identification in a variety of fields, partly because of its non-contact nature and the speed at which measurements can be taken. Given a library of known Raman spectra, a common detection approach is to first estimate the relative amount of each chemical present, and then compare the estimated mixing coefficients to an ad hoc threshold. We present a more rigorous detection scheme by formulating the problem as one of Multiple Hypothesis Detection (MHD) and using the maximum a posteriori (MAP) decision rule to minimize the probability of classification error. The probability that a specific target chemical is present is estimated by summing the estimated probabilities of all the hypotheses containing it. Alternatively, since we do not typically have reasonable priors for the hypotheses, it is perhaps preferable to interpret the result as an abstract score corresponding to the Minimum Description Length (MDL) approach. The resulting detection performance of this approach is compared to that of several other classification algorithms.

关键词： Raman spectroscopy detection classification minimum description length (MDL)

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Adaptive Tracking in Sensor data Fusion

Adaptive Tracking in Sensor Data Fusion

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conference on signal and data processing of small targets 2010

作者： Drummond, Oliver E. CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)9780819481627

In the design of a target track processor, specific models and parameters are selected for use in the processing of the sensor data. Some of the parameters may be known and some are not known with certainty so estimates of the values are used. The values of some of the design parameters may be significantly in error and so adjustments are needed. In the field of Kalman filters for example, adaptive methods (sometimes called adaptive filters) have been implemented to estimate in real time (sometimes slowly) more appropriate values for some of the design parameters. While existing adaptive filter methods might be considered for use in estimating some of the parameters in tracking, the possibility of misassociations of measurements to tracks introduces substantial anomalies that may require alternative adaptive methods (possibly more complex). Tracking with data from multiple sensors as in sensor data fusion introduces both additional advantages and challenges for adaptive methods relative to single sensor tracking. Fusion also involves functions beyond those typical of tracking and many fusion functions depend on the estimated tracks and/or influence the tracking performance. To date, most tracker design efforts have been directed toward improving the accuracy of the estimated target state using fixed design parameters (and models) and little effort has been directed to real time adaptive processing to improving the estimates of the design parameters. This paper addresses the degradation in tracker and fusion performance caused by inaccurate design parameters and addresses some considerations for adaptive tracking in sensor data fusion.

关键词： adaptive tracking adaptive filters covariance consistency gradual degradation adaptive fusion

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Numerical experiments for nonlinear filters with exact particle flow induced by log-homotopy

Numerical experiments for nonlinear filters with exact parti...

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conference on signal and data processing of small targets 2010

作者： Daum, Fred Huang, Jim Raytheon 225 Presidential Way Woburn MA 01801 USA

ISBN: (纸本)9780819481627

We show numerical experiments on a new theory of exact particle flow for nonlinear filters. This generalizes our theory of particle flow that was already many orders of magnitude faster than standard particle filters and which is several orders of magnitude more accurate than the extended Kalman filter for difficult nonlinear problems. The new theory generalizes our recent log-homotopy particle flow filters in three ways: (1) the particle flow corresponds to the exact flow of the conditional probability density;(2) roughly speaking, the old theory was based on incompressible flow (like subsonic flight in air), whereas the new theory allows compressible flow (like supersonic flight in air);(3) the old theory suffers from obstruction of particle flow as well as singularities in the equations for flow, whereas the new theory has no obstructions and no singularities. Moreover, our basic filter theory is a radical departure from all other particle filters in three ways: (a) we do not use any proposal density;(b) we never resample;and (c) we compute Bayes' rule by particle flow rather than as a point wise multiplication.

关键词： Particles Particle filters Nonlinear filtering Filtering (signal processing) Electronic filtering Monte Carlo methods Probability theory signal to noise ratio Communication engineering Digital filtering

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UWB Microwave Monopulse Radar System for Breast Cancer Detection

UWB Microwave Monopulse Radar System for Breast Cancer Detec...

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4th International conference on signal processing and Communication Systems (ICSPCS)/12th International Symposium on DSP and Communication Systems (DSPCS)/9th Workshop on the Internet, Telecommunications and signal processing (WTSP)

作者： Bialkowski, Marek E. Wang, Yifan Abbosh, Amin Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia

ISBN: (纸本)9781424479078

The concept of an Ultra Wideband (UWB) Monopulse Microwave Radar System for detection and location of breast cancer is described. The system uses a two-element Tapered Slot Antenna (TSA) array accompanied by a UWB 180 degrees hybrid which scans the breast. When the breast tissue features symmetry with respect to the array axis and the hybrid enables the 180 degrees out phase feeding operation, the array automatically eliminates background while extracting information about an off axis target. The data obtained from the scan is post-processed using a novel imaging algorithm to locate the target. The design of the required 2-element UWB array antenna and the 180 degrees hybrid is presented. The validity of the proposed breast cancer detection method is demonstrated in an example of a simple breast phantom formed by a cylindrical plastic container filled with a vegetable oil and small cylindrical targets.

关键词： UWB microwave monopulse radar system biomedical imaging breast cancer detection breast cancer location breast tissue cancer imaging algorithm microwave antenna arrays microwave imaging radar imaging small cylindrical targets tapered slot antenna array ult

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Association Ambiguity Management in Mixed data Dimension Tracking Problems

Association Ambiguity Management in Mixed Data Dimension Tra...

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conference on signal and data processing of small targets 2010

作者： Thornbrue, James R. Knight, J. Nate Slocumb, Benjamin J. Numerica Corp Loveland CO USA

ISBN: (纸本)9780819481627

Association and fusion of passive direction finding (DF) reports with active radar tracks from airborne targets is challenging because of the low dimensionality of the common kinematic measurement space. Often, multi-target scenarios lead to significant data association ambiguity. Classically, the approach to this problem is a simple hypothesis test wherein a batch of DF sensor measurements is associated with either zero or one of the radar tracks;assignment of multiple DF tracks to a single radar track is allowed without regard to compatibility, and this can lead to detrimental results. This paper develops a new approach for managing the ambiguity. The problem is formulated as a two-dimensional assignment, and any association ambiguity is determined from the k best solutions. Firm association decisions are made only when the ambiguity is at an acceptable level. The ambiguity information is also available in real time as an output to the system operator. An improved batch association score, relative to previous works, is formulated that addresses statistical correlations between individual measurement-to-track residuals;this new score is a likelihood ratio generated from Kalman Filter residuals. Where previous scoring methods lead to incorrect ambiguity assessments in certain scenarios, the new approach yields accurate results. Because the score is recursive, the batch may be extended over an arbitrary number of measurements, helping to manage association ambiguities over time. Simulation results are shown to demonstrate the algorithm.

关键词： radar electronic warfare fusion ambiguity angle-only multi-hypothesis direction finding tracking

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Multipath-Assisted Multitarget Tracking with Reflection Point Uncertainty

Multipath-Assisted Multitarget Tracking with Reflection Poin...

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conference on signal and data processing of small targets 2010

作者： Subramaniam, M. Tharmarasa, R. McDonald, M. Kirubarajan, T. McMaster Univ ETF Lab ECE Dept Hamilton ON L8S 4K1 Canada DRDC O Defence Sci Radar Syst Ottawa ON K1A 0K2 Canada

ISBN: (纸本)9780819481627

In this paper, the previous work multipath-assisted multitarget tracking using multiframe assignment is extended to the case where there are uncertainties in multipath reflection points at the receiver. An algorithm is proposed for initiating and tracking multiple targets using multiple transmitters and receivers. This algorithm is capable of exploiting multipath target returns from distinct and unknown propagation modes. When multipath returns are not utilized appropriately within the tracker, (e.g., discarded as clutter or incorporated with incorrect propagation mode assumption) the potential information in the multipath returns is lost. In real scenarios, it is more appropriate to assume that the locations of the reflection points/surfaces are not accurately known. Integrating multipath information into the tracker by correctly identifying the multipath mode and identifying the reflection point can help improve the accuracy of tracking. The challenge in improving tracking results using multipath measurements is the fusion of direct and multipath measurements from the common target when the multipath-reflection mode is unknown. The problem becomes even more challenging with false alarms and missed detections. We propose an algorithm to track the target with uncertainty in multipath reflection points/surface using the multiframe assignment technique. Simulation results are presented to show the effectiveness of the proposed algorithm on a ground target tracking problem.

关键词： Target tracking multipath data association multiframe assignment

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Two-level automatic multiple target joint tracking and classification

Two-level automatic multiple target joint tracking and class...

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conference on signal and data processing of small targets 2010

作者： He, Xiaofan Tharmarasa, Ratnasingham Pelletier, Michel Kirubarajan, Thia McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada ICx Radar Syst Laval PQ Canada

ISBN: (纸本)9780819481627

Target classification is of great importance for modern tracking systems. The classification results could be fed back to the tracker to improve tracking performance. Also, classification results can be applied for target identification, which is useful in both civil and military applications. While some work has been done on Joint Tracking and Classification (JTC), which can enhance tracking results and make target identification feasible, a common assumption is that the statistical description of classes is predefined or known a prior. This is not true in general. In this paper, two automatic multiple target classification algorithms, which can automatically classify targets without prior information, are proposed. The algorithms learn the class description from the target behavior history. The input to the algorithm is the noisy target state estimate, which in turn depends on target class. Thus, class description is learnt from the target behavior history rather than being predefined. This motivates the proposed two-level tracking and classification formulation for automatic multiple target classification. The first level consists of common tracking algorithm such as the Joint Probability data Association (JPDA), the Multiple Hypothesis Tracking (MHT) or the Probability Hypothesis Density (PHD) filter. In the second level, a Mean-Shift (MS) classifier and a PHD classifier are applied to learn the class descriptions respectively based on the state estimations from the first level tracker. The proposed algorithms only require the kinematic measurements from common radar. However, feature information can be easily integrated. Besides theoretical derivations, extensive experiments based on both simulated and real data are performed to verify the efficiency of the proposed technique.

关键词： Classification Mean-Shift algorithm Probability Hypothesis Density tracking multiple targets

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small target detection in SAR image using the alpha-stable distribution model

Small target detection in SAR image using the alpha-stable d...

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2nd International conference on Image Analysis and signal processing, IASP'2010

作者： Xu, Jia Han, Wei He, Xiu-Feng Chen, Ren-Xi Department of Earth Sciences and Engineering Hohai University Nanjing China 91635 Army Beijing China

ISBN: (纸本)9781424455553

The Constant False Alarm Rate (CFAR) algorithm is most commonly used for small target detection in SAR images. As the goodness-of-fit of distribution model to SAR clutter has great effect on the performance of algorithm, after a comprehensive statistical analysis of background clutters of different SAR data, a modified CFAR algorithm based on the Alpha-stable distribution is proposed for detecting small targets in SAR images, especially under the extremely inhomogeneous background clutter. Considering for the complexity of Alpha-stable distribution model, the parameter estimation and threshold determining steps of the modified algorithm are introduced in detail. Performance of the algorithm is assessed by experiments on ADTS data. Compared with typical two-parameter CFAR (TP-CFAR) algorithm based on Gaussian distribution and K-CFAR algorithm based on K distribution, the proposed method is demonstrated to be most suitable for detecting small target in extremely inhomogeneous regions. © 2010 IEEE.

关键词： Synthetic aperture radar

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Passive Acoustic Sensing for Detection of small Vessels

Passive Acoustic Sensing for Detection of Small Vessels

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Washington State conference and Trade Center (WSCTC)

作者： Sorensen, Eric Ou, Helen H. Zurk, Lisa M. Siderius, Martin Portland State Univ NW Electromagnet & Acoust Res Lab Dept Elect & Comp Engn Portland OR 97201 USA

ISBN: (纸本)9781424443338

This paper investigates the system requirements and signal processing tools for a semi-autonomous passive acoustic boat detection framework for monitoring marine environments. The objective is to develop a surveillance system which provides wide area coverage, automatic (boat) detection, and ultimately the ability to communicate real-time alerts (i.e., a bell-ringer system). A critical challenge for a successful system is the ability to accurately detect (and possibly classify) targets in the presence of noise. A wavelet detection algorithm has been developed to identify the presence of a boat in acoustic signals overwhelmed by noise from snapping shrimp and humpback whales. A feature extraction tool is introduced to examine the harmonically related structures in shipping noise. These methods are applied on acoustic data collected in a series of field tests in Hawaii, in which small-scale sensor networks were deployed near Marine Protected Areas (MPA) to assess the ability to monitor boat activity. A simulation tool developed based on Bellhop is introduced to study boat signatures under different environmental conditions.

关键词： Hawaii acoustic signal processing acoustic signals marine environments monitoring marine protected areas marine vehicles monitoring object detection passive acoustic sensing semi-autonomous passive acoustic boat detection signal processing tools small vessels detection

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