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

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BAYESIAN BIG BANG

BAYESIAN BIG BANG

引用

conference on signal and data processing of small targets

作者： Daum, Fred Huang, Jim Raytheon Co Waltham MA 02451 USA

ISBN: (纸本)9780819487476

We show that the flow of particles corresponding to Bayes' rule has a number of striking similarities with the big bang, including cosmic inflation and cosmic acceleration. We derive a PDE for this flow using a log-homotopy from the prior probability density to the posteriori probability density. We solve this PDE using the gradient of the solution to Poisson's equation, which is computed using an exact Green's function and the standard Monte Carlo approximation of integrals. The resulting flow is analogous to Coulomb's law in electromagnetics. We have used no physics per se to derive this flow, but rather we have only used Bayes' rule and the definition of normalized probability and a log-homotopy parameter that could be interpreted as time. The details of this big bang resemble very recent theories much more closely than the so-called new inflation models, which postulate enormous inflation immediately after the big bang.

关键词： Particles Physics Probability theory Thermodynamics General relativity signal to noise ratio Electromagnetism Palladium Quantum mechanics Monte Carlo methods

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A Gaussian Sum Filter Framework for Space Surveillance

A Gaussian Sum Filter Framework for Space Surveillance

引用

conference on signal and data processing of small targets

作者： Horwood, Joshua T. Aragon, Nathan D. Poore, Aubrey B. Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819487476

While standard Kalman-based filters, Gaussian assumptions, and covariance-weighted metrics work remarkably well in data-rich tracking environments such as air and ground, their use in the data-sparse environment of space surveillance is more limited. In order to properly characterize non-Gaussian density functions arising in the problem of long term propagation of state uncertainties in the two-body problem, a framework for a Gaussian sum filter is described which achieves uncertainty (covariance) consistency and an accurate approximation to the Fokker-Planck equation up to a prescribed accuracy. The filter is made efficient and practical by (i) using coordinate systems adapted to the physics (i. e., orbital elements), (ii) only requiring a Gaussian sum to be defined along one of the six state space dimensions, and (iii) the ability to initially select the component means, covariances, and weights by way of a lookup table generated by solving an offline nonlinear optimization problem. The efficacy of the Gaussian sum filter and the improvements over the traditional unscented Kalman filter are demonstrated within the problems of data association and maneuver detection.

关键词： Gaussian sum filter uncertainty consistency covariance consistency data association space surveillance

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Algorithm Development for Automated Outlier Detection and Background Noise Reduction during NIR Spectroscopic data processing

Algorithm Development for Automated Outlier Detection and Ba...

引用

conference on signal and data processing of small targets

作者： Abookasis, David Workman, Jerome J. Ariel Univ Ctr Samaria Dept Elect & Elect Engn IL-44837 Ariel Israel Biotechnol Business Associates Orange CA 92612 USA Liberty Univ Lynchburg VA 24502 USA US Natl Univ La Jolla CA 92037 USA

ISBN: (纸本)9780819487476

This study describes a hybrid processing algorithm for use during calibration/validation of near-infrared spectroscopic signals based on a spectra cross-correlation and filtering process, combined with a partial-least square regression (PLS) analysis. In the first step of the algorithm, exceptional signals (outliers) are detected and remove based on spectra correlation criteria we have developed. Then, signal filtering based on direct orthogonal signal correction (DOSC) was applied, before being used in the PLS model, to filter out background variance. After outlier screening and DOSC treatment, a PLS calibration model matrix is formed. Once this matrix has been built, it is used to predict the concentration of the unknown samples. Common statistics such as standard error of cross-validation, mean relative error, coefficient of determination, etc. were computed to assess the fitting ability of the algorithm Algorithm performance was tested on several hundred blood samples prepared at different hematocrit and glucose levels using blood materials from thirteen healthy human volunteers. During measurements, these samples were subjected to variations in temperature, flow rate, and sample pathlength. Experimental results highlight the potential, applicability, and effectiveness of the proposed algorithm in terms of low error of prediction, high sensitivity and specificity, and low false negative (Type II error) samples.

关键词： Spectra Correlation Outlier Detection Orthogonal signal Correction Partial-Least Square Regression Calibration and Validation

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A data-driven approach for processing heterogeneous categorical sensor signals

A data-driven approach for processing heterogeneous categori...

引用

conference on signal and data processing of small targets

作者： Calderon, Christopher P. Jones, Austin Lundberg, Scott Paffenroth, Randy Numerica Corp Loveland CO 80538 USA

ISBN: (纸本)9780819487476

False alarms generated by sensors pose a substantial problem to a variety of fusion applications. We focus on situations where the frequency of a genuine alarm is "rare" but the false alarm rate is high. The goal is to mitigate the false alarms while retaining power to detect true events. We propose to utilize data streams contaminated by false alarms (generated in the field) to compute statistics on a single sensor's misclassification rate. The nominal misclassification rate of a deployed sensor is often suspect because it is unlikely that these rates were tuned to the specific environmental conditions in which the sensor was deployed. Recent categorical measurement error methods will be applied to the collection of data streams to "train" the sensors and provide point estimates along with confidence intervals for the parameters characterizing sensor performance. By pooling a relatively small collection of random variables arising from a single sensor and using data-driven misclassification rate estimates along with estimated confidence bands, we show how one can transform the stream of categorical random variables into a test statistic with a limiting standard normal distribution. The procedure shows promise for normalizing sequences of misclassified random variables coming from different sensors (with a priori unknown population parameters) to comparable test statistics;this facilitates fusion through various downstream processing mechanisms. We have explored some possible downstream processing mechanisms that rely on false discovery rate (FDR) methods. The FDR methods exploit the test statistics we have computed in a chemical sensor fusion context where reducing false alarms and maintaining substantial power is important. FDR methods also provide a framework to fuse signals coming from non-chem/bio sensors in order to improve performance. Simulation results illustrating these ideas are presented. Extensions, future work and open problems are also briefly discuss

关键词： False Alarms CBRN data Fusion False Discovery Rate Categorical data Event Queues

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Information Exchanged Between Fusion Tracker and Other Fusion Functions

Information Exchanged Between Fusion Tracker and Other Fusio...

引用

conference on signal and data processing of small targets

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

ISBN: (纸本)9780819487476

The state of the art of tracking has matured and consequently, the priorities for improved performance and expanded or new processing capabilities have changed. Future directions in algorithm development in tracking and related data processing are not easy to predict with accuracy. The future priorities of development tasks predicted in this presentation are subjective;that is, simply the author's view. While there will continue to be algorithm development to improve many aspects of tracking, the emphasis is expected to change in favor of expanded or new capabilities. This paper concentrates on the interactions between the fusion tracker and the other fusion functions. Fusion is expected to have higher priority for algorithm development than tracking with single sensor data. The interactions between the fusion tracker and the other fusion functions are expected to be of special interest to achieve advanced fusion performance. To facilitate this discussion, the categories of the state of the art of tracking are expanded beyond the previous paper. Many aspects of single sensor, multiple target tracking have matured during the last 20 years but room for improvement remains. In contrast, fusion of data from multiple distributed sensors is far less mature and interest is expected to continue to increase. Many fusion systems pose challenges that are not of much concern in tracking with data from a single sensor and algorithm development of those aspects of fusion will continue to be needed. The capability of the many functions and users of the output of trackers needs to be improved and expanded. Consequently, an increase is expected in the need to improve in the interactions between the fusion tracker and the other fusion functions. Some of important interactions between the fusion tracker and the other fusion functions of a high performance multiple sensor fusion system are discussed and a dialogue on this topic is encouraged.

关键词： Multiple target tracking tracking with data from multiple sensors sensor data fusion network centric processing tracking open issues covariance consistency Kalman filter

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Performance Evaluations of Multipath Multitarget Tracking using PCRLB

Performance Evaluations of Multipath Multitarget Tracking us...

引用

conference on signal and data processing of small targets

作者： Subramaniam, M. Punithakumar, K. Tharmarasa, R. McDonald, M. Kirubarajan, T. McMaster Univ ECE Dept ETF Lab Hamilton ON L8S 4K1 Canada St Josephs Hlth Care London London ON N6A 4V2 Canada Def Res Dev Canada Radar Syst Ottawa ON K1A 0K2 Canada

ISBN: (纸本)9780819487476

In this paper, we study the performance of the multipath-assisted multitarget tracking using multiframe assignment for initiating and tracking multiple targets by employing one or more transmitters and receivers. The basis of the technique is to use the posterior Cramer-Rao lower bound (PCRLB) to quantify the optimal achievable accuracy of target state estimation. When resolved multipath signals are present at the sensors, if proper measures are not taken, multiple tracks will be formed for a single target. In typical radar systems, these spurious tracks are removed from tracking, and therefore the information carried in such target return tracks are wasted. In multipath environment, in every scan the number of sensor measurements from a target is equal to the number of resolved signals received by different propagation modes. The data association becomes more complex as this is in contrary to the standard data association problem whereas the total number of sensor measurements from a target is equal to at most one. This leads to a challenging problem of fusing the direct and multipath measurements from the same target. We showed in our evaluations that incorporating multipath information improves the performance of the algorithm significantly in terms of estimation error. Simulation results are presented to show the effectiveness of the proposed method.

关键词： Target tracking multipath PCRLB performance data association multiframe assignment

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GPGPU Based Real-time Conditional Dilation for Adaptive Thresholding for Target Detection

GPGPU Based Real-time Conditional Dilation for Adaptive Thre...

引用

conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII

作者： Morgenstern, Jim Zell, Bradley Vision4ce LLC Severna Pk MD USA

ISBN: (纸本)9780819486226

A significant topic in many image processing systems is the derivation of a threshold to actuate the automated analysis of outputs from spectral filters and/or anomaly filters, the detection of targets and/or classes of objects which are different than the local background clutter. There are cases where the signals of interest have contrast locally against their immediate surroundings but the application of a global threshold over the entire image produces poor results with missed detections and numerous false alarms. In such cases an adaptive or local threshold operator offers a more robust solution. One local threshold function is the conditional dilation which produces a reference image via a series of dilations which are conditioned on not exceeding the signal levels in the original image. In the limit this reference image becomes a threshold surface where only areas or objects exhibiting contrast locally remain after application of the threshold. Algorithms have been introduced which enable use of conditional dilation in realtime systems by reducing the unbounded series of dilations to a small, fixed number of operations. In the present work we present an adaptation of this algorithm to both single CPU systems and also to systems which incorporate a GPGPU device which enables a highly parallel version of the algorithm subject to the unique architecture constraints of the GPGPU. Execution timings for comparison are introduced: The GPGPU offers somewhat better performance than the single CPU system despite the GPGPU architecture not being suitable for implementation of a neighborhood process.

关键词： Image processing Target detection Optical filters Detection and tracking algorithms data processing Hyperspectral target detection Image analysis Sensors Image compression Parallel computing

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A target detection algorithm based on peripheral anomaly

A target detection algorithm based on peripheral anomaly

引用

IEEE signal processing and Communications Applications (SIU)

作者： A. Onur Karalı Tayfun Aytaç Alper Yıldırım Ö. Nezih Gerek TÜBİTAK BİLGEM UEKAE İLTAREN Ankara Turkey Elektrik-Elektronik Mühendisliği Bölümü Anadolu Üniversitesi Eskisehir Turkey

Automatic target detection algorithms enable in military and civilian applications the detection of low-contrast and spatially small targets that can not be detected by a human operator and remove the requirement of permanent operator control for surveillance systems. Considering many different background patterns and target models, it is not possible to define a single target detection algorithm that succeeds in all scenarios. In this paper, a novel target detection algorithm is proposed for targets above sea-level and sky targets. Peripheral anomaly values are calculated for image blocks depending on the difference of the statistical data between target and background patterns and target and background regions are differentiated by comparing these values with scenario dependent threshold values. Experimental results show that the proposed method has high success rate in the detection of targets above sea-level and sky targets in visual band images.

关键词： Object detection Marine vehicles signal processing algorithms signal processing conferences Histograms Solid modeling

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The JPDAF in Practical Systems: Approximations

The JPDAF in Practical Systems: Approximations

引用

conference on signal and data processing of small targets 2010

作者： Romeo, Kevin Crouse, David F. Bar-Shalom, Yaakov Willett, Peter Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819481627

In this paper we look at various algorithms for approximating the target-measurement association probabilities of the Joint Probabilistic data Association Filter (JPDAF). We consider their computational complexity and compare their performance with respect to the Mean Optimal Subpattern Assignment (MOSPA) statistic in a scenario involving closely-spaced targets.

关键词： Detection and tracking algorithms Target detection Monte Carlo methods Computer simulations Error analysis Time metrology Algorithm development Computing systems data processing signal processing

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Unified sensor management in unknown, dynamic clutter

Unified sensor management in unknown, dynamic clutter

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conference on signal and data processing of small targets 2010

作者： Mahler, Ronald El-Fallah, Adel Lockheed Martin MS2 (United States) Scientific Systems Co. Inc. (United States)

ISBN: (纸本)9780819481627

In recent years the first author has developed a unified, computationally tractable approach to multisensor-multitarget sensor management. This approach consists of closed-loop recursion of a PHD or CPHD filter with maximization of a "natural" sensor management objective function called PENT (posterior expected number of targets). In this paper we extend this approach so that it can be used in unknown, dynamic clutter backgrounds.

关键词： Sensors Target detection Process modeling Space sensors Current controlled current source data processing Electronic filtering Probability theory signal detection signal processing

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