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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2010"

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PERFORMANCE EVALUATION OF A MULTISENSOR data FUSION IMPLEMENTATION - A PROGRESS REPORT

PERFORMANCE EVALUATION OF A MULTISENSOR DATA FUSION IMPLEMEN...

引用

conference on signal and data processing of small targets 1994

作者： BOILY, E PARAMAX SYST CANADA INC MONTREAL H4P 1K6PQCANADA

ISBN: (纸本)0819415391

A sensor-level hard fusion system is currently being developed for the Canadian Patrol Frigate (CPF) within the simulation environment provided by the Combat System Test and Support Facility at Paramax. A number of operational criteria have been selected to evaluate and to compare the performance of the CPF's Command and Control System versus the data fusion system. The criteria operate on tracking and on identification in terms of time efficiency and precision. Since the implementation is done on a simulation environment, the target's ground truth is available (off-line in the current implementation). Some criteria are computed and displayed in real-time and the criteria needing access to ground truth are evaluated off-line. A subset of the performance evaluation criteria are presented and discussed. Implementation considerations are also presented.

关键词： data fusion databases Sensors Filtering (signal processing) data modeling Electronic filtering Environmental sensing Statistical analysis Telecommunications Software development

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RADAR TARGET DISCRIMINATION USING WAVELET TRANSFORMS

RADAR TARGET DISCRIMINATION USING WAVELET TRANSFORMS

引用

conference on signal and data processing of small targets 1994

作者： VARAD, R MITRE CORP BEDFORDMA 01730

ISBN: (纸本)0819415391

In this paper, we describe a new approach to radar target discrimination. Specifically, we will apply it to the problem of exo-atmospheric object discrimination from UHF radar returns. The method uses wavelet transforms, pattern recognition techniques such as feature spaces, vectors and neural net classifiers. Feature vectors for each object are constructed from the wavelet transforms of the input data samples. The feature vectors are based on energies at each scale of the wavelet transforms and therefore effectively circumvent the problem of noncoherence due to target and ionospheric effects. This is a very important consideration when coherent signal processing is not feasible. The feature vectors are input to an unsupervised learning neural network for classification of the objects. In unsupervised learning, the network output is not forced towards a desired response for each input pattern but allowed to learn proximity to past input patterns. Limited results from simulated radar cross-section (RCS) data indicate that most objects can be correctly classified. The results also show that the overall scheme is quite immune to fair amounts of gaussian as well as uniformly distributed noise. Further efforts are under way to test the methodology against real object data as well as more extensive simulations.

关键词： Wavelets Neural networks Radar Wavelet transforms signal to noise ratio signal processing Neurons Image classification Doppler effect Machine learning

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PERFORMANCE PREDICTION OF TRACKING IN CLUTTER WITH NEAREST-NEIGHBOR FILTERS

PERFORMANCE PREDICTION OF TRACKING IN CLUTTER WITH NEAREST-N...

引用

conference on signal and data processing of small targets 1994

作者： LI, XR BARSHALOM, Y UNIV HARTFORD DEPT ELECT ENGNW HARTFORDCT 06117

ISBN: (纸本)0819415391

The measurement that is `closest' to the predicted target measurement is known as the `nearest neighbor' measurement in target tracking. A common method currently in wide use for tracking in clutter is the so-called nearest neighbor filter, which uses only the nearest neighbor measurement as if it is the true one. This paper presents a technique for prediction without recourse to expensive Monte Carlo simulations of the performance of the nearest neighbor filter. This technique can quantify the dynamic process of tracking divergence as well as the steady state performance. The technique is based on a general approach to the performance prediction of algorithms with both continuous and discrete uncertainties developed recently by the authors.

关键词： Electronic filtering Error analysis Plasma display panels Target detection Algorithm development Monte Carlo methods Detection and tracking algorithms Filtering (signal processing) Time metrology Aluminum

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MEAN-FIELD THEORY AND MULTITARGET TRACKING

MEAN-FIELD THEORY AND MULTITARGET TRACKING

引用

conference on signal and data processing of small targets 1994

作者： KASTELLA, K UNISYS GOVT SYST CORP ST PAULMN 55164

ISBN: (纸本)0819415391

This paper presents a novel Kalman filter for track maintenance in multitarget tracking using thresholded sensor data at high target/clutter densities and low detection levels. The filter is robust against tracking errors induced by crossing tracks, clutter and missed detections and the computational complexity of the filter scales well with problem size. There are two key features that differentiate this approach from earlier work. First, in order to enhance tracking of close tracks, the filter explicitly models the error correlations that occur between such target pairs. These error correlations arise due to the measurement to track association ambiguity present when target separations are comparable to the measurement errors in the sensors. Second, in order to reduce the computational load, the filter exploits techniques from statistical field theory to simplify the combinatorial complexity of measurement to track association. This is accomplished by developing a mean-field approximation to the summation over all associations.

关键词： Sensors Error analysis Detection and tracking algorithms Electronic filtering Target detection Filtering (signal processing) Matrices Motion models Digital filtering Statistical analysis

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COMPARISON OF MODELED TO MEASURED INFRARED MULTISPECTRAL TARGET BACKGROUND SIGNATURES

COMPARISON OF MODELED TO MEASURED INFRARED MULTISPECTRAL TAR...

引用

conference on signal and data processing of small targets 1994

作者： SCHWARTZ, CR CEDERQUIST, JN ROGNE, TJ ENVIRONM RES INST MICHIGAN DIV ADV CONCEPTSANN ARBORMI 48113

ISBN: (纸本)0819415391

A single pixel hyperspectral signature model was developed to support detailed infrared phenomenological analysis, optimal spectral band selection, and target detector algorithm development. Model predictions of infrared hyperspectral signatures of targets and backgrounds were compared to measurements made in the field by an infrared Fourier transform spectrometer. The model was successful in predicting the actual spectral features present in the measured data. Details of the model, an example of its accuracy, and the implications for infrared phenomenological analysis and multispectral target detection algorithms are given.

关键词： data modeling Atmospheric modeling Reflectivity Temperature metrology Infrared radiation Thermal modeling Infrared signatures Spectral models Bidirectional reflectance transmission function Target detection

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TRACK ASSOCIATION USING CORRECTION FOR BIAS AND MISSING data

TRACK ASSOCIATION USING CORRECTION FOR BIAS AND MISSING DATA

引用

conference on signal and data processing of small targets 1994

作者： BLACKMAN, SS BANH, ND HUGHES AIRCRAFT CO EL SEGUNDOCA 90245

ISBN: (纸本)0819415391

One technique for multisensor tracking forms sensor level tracks using measurements received from the individual sensors. Then, the sensor level tracks are combined into a central level trackfile by performing multisensor track-to-track association and track fusion. Due to difference in sensor resolution, detection capability and coverage, there may be targets for which a track is formed by one sensor but not by the other. Also, tracks formed on the same target by multiple sensors may differ due to multisensor misalignment (or bias) error. This paper addresses these problems by developing a method to perform multisensor track-to-track association under the conditions of intersensor bias and missing track data. An augmentation to the association matrix is developed to account for the face that each sensor may not contain a full set of tracks for all targets in the field of view. An iterative approach is used to estimate and correct for the bias error. Monte Carlo simulation results are presented to illustrate the methods and close correspondence is found between these results and the theoretical probability of correct association.

关键词： Sensors Error analysis Monte Carlo methods Detection and tracking algorithms Optical resolution Statistical analysis Target detection Clouds Electro optical sensors Information technology

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data FUSION OF ASSOCIATION HYPOTHESES IN A DISTRIBUTED SENSOR NETWORK

DATA FUSION OF ASSOCIATION HYPOTHESES IN A DISTRIBUTED SENSO...

引用

conference on signal and data processing of small targets 1994

作者： AGATE, CS ILTIS, RA UNIV CALIF SANTA BARBARA CTR INFORMAT PROC RESDEPT ELECT & COMP ENGNSANTA BARBARACA 93106

ISBN: (纸本)0819415391

A fusion algorithm is presented for a multisensor tracking system, in which the local trackers are N-scan data association filters. Previously, a fusion algorithm was given for the case where the local trackers are JPDA filters. Here, a fusion algorithm is presented for the more general case of local N-scan data association filters, of which the JPDA is a special case (N equals 0). The fusion equations consist of a simultaneous updating of the global hypothesis probabilities, and conditional global target state estimates. Two communication schemes between the local trackers and global processor are considered. A unidirectional communication scheme is examined in which the local trackers send the updated hypothesis probabilities and conditional target state estimates to the global processor; the local nodes then continue to track without knowledge of the global estimates. A bidirectional communication scheme is examined in which the local trackers send the updated hypothesis probabilities and conditional target state estimates to the global processor.

关键词： Detection and tracking algorithms Algorithm development data fusion Computer simulations Sensors Monte Carlo methods Electronic filtering data communications Filtering (signal processing) Sensor networks

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REVISIT CONTROL OF A PHASED-ARRAY RADAR FOR TRACKING MANEUVERING targets WHEN SUPPORTED BY A PRECISION ESM SENSOR

REVISIT CONTROL OF A PHASED-ARRAY RADAR FOR TRACKING MANEUVE...

引用

conference on signal and data processing of small targets 1994

作者： WATSON, GA BLAIR, WD USN CTR SURFACE WARFAREDAHLGREN DIVDEPT SYST RES & TECHNOLDAHLGRENVA 22448

ISBN: (纸本)0819415391

Since phased array radars have the ability to perform adaptive sampling of the target trajectory by radar beam positioning, proper control of the radar has the potential for significantly improving many aspects associated with the tracking of multiple maneuvering targets. When supported by additional sensors, the sampling of the phased array radar can be reduced significantly. However, controlling the revisit times of the phase array radar becomes more difficult. The technique proposed in this paper uses the Interacting Multiple Model algorithm to track maneuvering targets and control the radar revisit time and pointing when the radar is supported by a Precision Electronic Support Measures (PESM) sensor. Algorithms for tracking with multiple sensors, computing the radar revisit time, and pointing the radar are presented in this paper. Performance comparisons are given with the radar using adaptive data rates and the PESM providing measurements at regular intervals and intermittently.

关键词： Radar Sensors Detection and tracking algorithms Error analysis Time metrology Phased arrays Motion models Switching Electronic filtering Electronic support measures

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IMPROVING FRAME INTEGRATION PERFORMANCE IN THE PRESENCE OF UNKNOWN SENSOR JITTER

IMPROVING FRAME INTEGRATION PERFORMANCE IN THE PRESENCE OF U...

引用

conference on signal and data processing of small targets 1994

作者： HARTLESS, ML MARTIN MARIETTA CORP OCEAN RADAR & SENSOR SYSTUTICANY 13503

ISBN: (纸本)0819415391

Temporal frame integration performance in white noise is investigated as a function of the sensor point spread function (PSF), the uncompensated sensor jitter level and the velocity bin quantization used in the algorithm implementation. The analysis is provided for both recursive and non-recursive frame integration implementations. The results show that for a given sensor PSF, jitter level and velocity bin quantization, a limit exists on how many frames can be effectively integrated assuming constant velocity hypotheses for the target. It is also shown that to obtain maximum performance from multi-frame integration, knowledge of the residual rms jitter level needs to be included as part of the optimal space-time filter. A simple non- linear method for reducing the frame integration loss when there is uncompensated sensor jitter present in the data is also presented.

关键词： Sensors Digital filtering signal to noise ratio Quantization Detection and tracking algorithms Point spread functions Image resolution Electronic filtering Spatial filters Image filtering

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BEARINGS-ONLY TRACKING IN A DISTRIBUTED SENSOR NETWORK USING REDUCED SUFFICIENT STATISTICS

BEARINGS-ONLY TRACKING IN A DISTRIBUTED SENSOR NETWORK USING...

引用

conference on signal and data processing of small targets 1994

作者： ANDERSON, KL ILTIS, RA UNIV CALIF SANTA BARBARA CTR INFORMAT PROC RESDEPT ELECT & COMP ENGNSANTA BARBARACA 93106

ISBN: (纸本)0819415391

A distributed parameter estimation algorithm is presented for a general nonlinear measurement model with additive Gaussian noise. We show that the Bayes-closed estimation algorithm developed by Kulhavy, when extended to the multisensor case leads to a linear fusion rule, regardless of the form of a local a posteriori densities. Specifically, the Kulhavy algorithm generates a set of reduced sufficient statistics representing the local sensor densities, which are simply added and subtracted at the global processor to obtain optimum fusion. We discuss various approximations to the Bayes-closed algorithm which leads to a practical parameter estimator for the nonlinear measurement model, and apply such an approximate technique to the bearings-only tracking problem. The performance of the distributed tracker is compared to an alternative algorithm based on the extended Kalman filter (EKF) implemented in modified polar coordinates. It is shown that the Bayes-closed estimator does not diverge in the sense of an ordinary EKF, and hence the Bayes-closed technique can be employed in both a unidirectional and bidirectional transmission mode.

关键词： Received signal strength Detection and tracking algorithms Sensors Reconstruction algorithms Algorithm development Error analysis data communications Statistical analysis Filtering (signal processing) Sensor networks

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