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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271-280 订阅

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NUMERICAL EXPERIMENTS FOR COULOMB'S LAW PARTICLE FLOW FOR NONLINEAR FILTERS

NUMERICAL EXPERIMENTS FOR COULOMB'S LAW PARTICLE FLOW FOR NO...

引用

conference on signal and data processing of small targets

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

ISBN: (纸本)9780819487476

We show numerical results for a new nonlinear filtering algorithm that is analogous to Coulomb's law. We have invented a new theory of exact particle flow for nonlinear filters. The flow of particles corresponding to Bayes' rule is computed from the gradient of the solution of Poisson's equation, and it is analogous to Coulomb's law. Our theory is a radical departure from other particle filters in several ways: (1) we compute Bayes' rule using a flow of particles rather than as a pointwise multiplication;(2) we never resample particles;(3) we do not use a proposal density;(4) we do not use importance sampling or any other MCMC algorithm;and (5) our filter is roughly 6 to 8 orders of magnitude faster than standard particle filters for the same accuracy.

关键词： Particles Nonlinear filtering Particle filters Algorithm development signal processing signal to noise ratio Algorithms Interference (communication) Electronic filtering Filtering (signal processing)

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Comparison of data Reduction Techniques Based on SVM Classifier and SVR Performance

Comparison of Data Reduction Techniques Based on SVM Classif...

引用

conference on signal and data processing of small targets

作者： Zhao, Dave Georgescu, Ramona Willett, Peter Univ Connecticut ECE Dept Storrs CT 06269 USA

ISBN: (纸本)9780819487476

In this work, we applied several data compression techniques to simulated data and the Turbofan engine degradation simulation data set from NASA, with the goal of comparing their performance when coupled with the Support Vector Machine (SVM) classifier and the SVM regression (SVR) predictor. We consistently attained correct rates in the neighborhood of 90% for simulated data set, with the Principal Component Analysis (PCA), Sparse Reconstruction by Separable Approximation (SpaRSA) and Partial Least Squares (PLS) having a slight edge over the other data reduction methods for data classification. We achieved 22% error rate with SRM for the Turbofan data set 1 and 40% error rate with PCA for Turbofan data set 2. Throughout the tests we have performed, PCA proved to be the best data reduction method.

关键词： data reduction PCA PLS SRM OMP SpaRSA Classification Regression SVM SVR

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Tracking Group targets Using Hypergraph Matching in data Association

Tracking Group Targets Using Hypergraph Matching in Data Ass...

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conference on signal and data processing of small targets

作者： Wu, Shunguang Xiao, Jiangjiang SRI Int Sarnoff Princeton NJ 08540 USA

ISBN: (纸本)9780819487476

Group moving targets are number of targets independently moving in a physical space but keeping their relative order or pattern invariant. The up to date state-of-the-art multi-target tracking (MTT) data association methods (GNN,JPDA,MHT) are easily fail on group targets tracking problems, since the tracker-to-observation ambiguity cannot be resolved if only using the individual track to observation information. A hypergraph G is represented by G = {V, E}, where V is a set of elements called nodes or vertices, E is a set of non-empty subsets containing d-tuple of vertices called hyperedges. It can be used as a new mathematic tool to represent a group of moving targets if we let each target be a vertex and a d-target subset be an hyperedge. Under this representation, this paper reformulates the traditional MTT data association problem as an hypergraph matching one between the hypergraphs formed from tracks and observations, and shows that the traditional approach (only uses the vertex-to-vertex information) which is a special case under the proposed framework. In addition to the vertex-to-vertex information, since the hyperedge-to-hyperegde information is also used in building the assignment matrix, the hypergraph matching based algorithms give better performance than that from the traditional methods in group target tracking problems. We demonstrate the declaration from simulations as well as video based geotracking examples.

关键词： Group targets tracking data association hypergraph matching

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Application of a joint tracking and identification method to dismount targets

Application of a joint tracking and identification method to...

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conference on signal and data processing of small targets

作者： Blackman, Sam Krikorian, Kapriel Rosen, Robert Durand, Catherine Schwoegler, Stefan Raytheon Co Waltham MA 02451 USA

ISBN: (纸本)9780819487476

This paper presents a method for tracking dismounts/humans in a potentially dense clutter background. The proposed approach uses Multiple Hypothesis Tracking (MHT) for data association and Interacting Multiple Model (IMM) filtering. The problem is made difficult by the presence of random and persistent clutter, such as produced by moving tree branches. There may also be moving targets (such as vehicles and animals) that are not of interest to the user of the tracking system, but that must be tracked in order to separate these targets from the targets of interest. Thus, a joint tracking and identification method has been developed to utilize the features that are associated with dismount targets. This method uses a Dempster-Shafer (D-S) approach to combine feature data to determine the target type (dismount versus other). Feature matching is also included in the computation of the track score used for MHT data association. The paper begins by giving an overview of the features that have been proposed in the literature for distinguishing humans from other types of targets. These features include radar cross section, target dynamics, and spectral and gait characteristics. For example, the number of secondary peaks around the main peak corresponding to the mean Doppler shift is one feature that is sent to the tracker. A large number of secondary peaks will be an indication that the observation is from an animal, rather than a vehicle. Also, if spectral analysis of the variation in Doppler shift due to torso motion yields a distinct periodic pattern with a peak at about 2 Hz, this can be used to identify the target as a human and, along with the target speed, may even be used as a target signature. The manner in which these features are estimated during signal processing and how this data is included in the track score is described. A test program conducted to produce data for analysis and development is described. Typical results derived from real data, collected during

关键词： target tracking identification dismount MHT multiple hypothesis Dempster-Shafer IMM spectral

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Interacting Multiple Model estimators for tracking thousands of interacting, small targets in a complex plasma

Interacting Multiple Model estimators for tracking thousands...

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conference on signal processing, Sensor Fusion, and Target Recognition XX

作者： Oxtoby, Neil P. Ralph, Jason F. Durniak, Celine Samsonov, Dmitry Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9780819486240

Tracking a large number of small targets is a challenging task. This work considers tracking approximately 3000 micron-sized particles in a complex plasma. Inter-particle screened-Coulomb interactions increase the complexity of the tracking problem, which is further complicated by highly nonlinear dynamics - a shock wave. Subsets of the particles are tracked concurrently by Interacting Multiple Model estimators, with the results combined off-line. State estimation is performed by an extended Kalman filter. Estimator performance is quantified on synthetic data, with discussion focussing on aspects of the Interacting Multiple Model.

关键词： tracking extended kalman filter interacting multiple model complex plasma dusty plasma

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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

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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...

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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...

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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...

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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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