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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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IMAGE-ENHANCED PASSIVE TRACKING OF MANEUVERING targets

IMAGE-ENHANCED PASSIVE TRACKING OF MANEUVERING TARGETS

引用

conference on signal and data processing of small targets 1994

作者： HUTCHINS, RG USN POSTGRAD SCHDEPT ELECT & COMP ENGNMONTEREYCA 93943

ISBN: (纸本)0819415391

The design of effective pointing-and-tracking systems is particularly difficult when the target is capable of evasive maneuvers. Traditional approaches employed to detect a change in maneuver acceleration rely on observing changes in the residual error process, which introduces a time lag between the onset of a maneuver and its detection. This problem is exacerbated when only passive bearing measurements are available because measurement localization errors are infinite in one dimension. Earlier studies using active (range and bearing) measurement models have suggested the use of an augmenting imaging sensor to determine target orientation, from which the likely direction of a maneuver acceleration can be inferred. This paper extends these earlier results to the case of passive (bearing only) measurements, again using an augmenting imaging sensor to estimate target orientation. This sequence of orientation measurements is used to infer the onset and direction of a maneuver acceleration. As in these earlier studies, orientation measurements provide significant value in localizing and tracking the target during a maneuver.

关键词： Image processing Motion models Sensors Imaging systems Image filtering Nonlinear filtering Optical sensors Monte Carlo methods Motion measurement Systems modeling

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MAXIMUM-LIKELIHOOD METHOD FOR PROBABILISTIC MULTI-HYPOTHESIS TRACKING

MAXIMUM-LIKELIHOOD METHOD FOR PROBABILISTIC MULTI-HYPOTHESIS...

引用

conference on signal and data processing of small targets 1994

作者： STREIT, RL LUGINBUHL, TE USN CTR UNDERSEA WARFARE CTRDIV NEWPORTNEWPORTRI 02841

ISBN: (纸本)0819415391

In a multi-target multi-measurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; instead, the probability that each measurement belongs to each track is estimated using a maximum likelihood algorithm derived by the method of Expectation-Maximization. These measurement-to-track probability estimates are intrinsic to the multi-target tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. Unlike MHT algorithms, the PMHT algorithm does not maintain explicit hypothesis lists. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning.

关键词： Detection and tracking algorithms Silicon Expectation maximization algorithms Algorithm development Error analysis Process modeling Filtering (signal processing) Algorithms Environmental sensing Information operations

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FEEDFORWARD NEURAL NETWORKS TO SOLVE THE CLOSELY-SPACED OBJECTS PROBLEM

FEEDFORWARD NEURAL NETWORKS TO SOLVE THE CLOSELY-SPACED OBJE...

引用

conference on signal and data processing of small targets 1994

作者： BARTOLAC, TJ ANDERT, EP SPARTA SYST INC EL SEGUNDOCA 90245

ISBN: (纸本)0819415391

We have applied a feed-forward neural network to the task of resolving closely-spaced objects (CSO). Traditional algorithmic methods are computationally expensive or numerically unstable, and techniques based on ad hoc rules are too subjective. Our approach relies on the principle that a sufficiently complex neural network can approximate an arbitrary function to an arbitrary degree of accuracy. We train a neural network to approximate the multi- dimensional function that maps from detector signal space to CSO parameter space, using an aggressive Hessian-based training algorithm and training set examples synthesized from the known inverse function. We find two important empirical results: we can simultaneously identify when the training set size is sufficient to adequately represent the mapping function, and when the network has achieved optimum generalization capability, for a given degree of network complexity. Thus we can predict the network and training set sizes necessary to achieve a given mission performance. Finally, we show how such a network can be used to provide sub-pixel resolution capabilities for missions observing both single objects and CSOs, as part of a real-time 2D sensor processor.

关键词： Sensors Neural networks signal detection Detection and tracking algorithms Computer architecture Error analysis Associative arrays Network architectures signal processing Electronics

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APPLICATION OF A MAXIMUM-LIKELIHOOD ALGORITHM TO THE DETECTION OF targets IN SYNTHETIC-APERTURE RADAR (SAR) IMAGES

APPLICATION OF A MAXIMUM-LIKELIHOOD ALGORITHM TO THE DETECTI...

引用

conference on signal and data processing of small targets 1994

作者： SCHOENDORF, WH MARZETTA, TL PERLOVSKY, LI MARTINSEN, EA WALLACE, RG RAIS, H AFFENS, DW NICHOLS RES CORP WAKEFIELDMA

ISBN: (纸本)0819415391

A detection algorithm was developed using the Maximum Likelihood Adaptive Neural System a neural network that adaptively estimates the probability density functions (pdf) of all classes of objects in the data set. This algorithms was used to detect downed aircraft in a heavy clutter environment in SAR images. In this study the portion of the image under study contains hundreds of thousands of pixels, the pixel statistics are estimated and the pixel having the lowest likelihood is labeled as the target pixel. This is an unsupervised learning approach to the target detection problem because no learning data on the background or the target is used to detect the target. The approach relies on an accurate estimate of the image likelihood function in order to provide a good characterization of the scene. This approach was applied to several images collected in a variety of heavily wooded areas under different environmental conditions with excellent results. This approach was also used to provide insight into the scene phenomenology by associating specific basis functions in each likelihood characterization with particular attributes of the image.

关键词： Synthetic aperture radar Target detection Detection and tracking algorithms data modeling Algorithm development Scattering Matrices Machine learning Neural networks Statistical analysis

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EFFECT OF CROSS-CORRELATION ON TRACK-TO-TRACK FUSION

EFFECT OF CROSS-CORRELATION ON TRACK-TO-TRACK FUSION

引用

conference on signal and data processing of small targets 1994

作者： SAHA, RK MITRE CORP BEDFORDMA 01730

ISBN: (纸本)0819415391

Since the advent of target tracking systems employing a diverse mixture of sensors, there has been increasing recognition by air defense system planners and other military system analysts of the need to integrate these tracks so that a clear air picture can be obtained in a command center. A popular methodology to achieve this goal is to perform track-to-track fusion, which performs track-to-track association as well as kinematic state vector fusion. This paper seeks to answer analytically the extent of improvement achievable by means of kinetic state vector fusion when the tracks are obtained from dissimilar sensors (e.g., Radar/ESM/IRST/IFF). It is well known that evaluation of the performance of state vector fusion algorithms at steady state must take into account the effects of cross-correlation between eligible tracks introduced by the input noise which, unfortunately, is often neglected because of added computational complexity. In this paper, an expression for the steady-state cross-covariance matrix for a 2D state vector track-to-track fusion is obtained. This matrix is shown to be a function of the parameters of the Kalman filters associated with the candidate tracks being fused. Conditions for positive definiteness of the cross-covariance matrix have been derived and the effect of positive definiteness on performance of track-to-track fusion is also discussed.

关键词： Sensors Filtering (signal processing) Matrices Kinematics Detection and tracking algorithms Chemical elements Electronic filtering Algorithm development data fusion Sensor fusion

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MULTICOVARIANCE MATCHED-FILTER FOR TARGET DETECTION AND BACKGROUND RECOGNITION

MULTICOVARIANCE MATCHED-FILTER FOR TARGET DETECTION AND BACK...

引用

conference on signal and data processing of small targets 1994

作者： SCHEFFE, M BLANE, MM COOPER, DB BROWN UNIV DIV ENGNENGN MAN MACHINE SYST LABPROVIDENCERI 02912

ISBN: (纸本)0819415391

This paper deals with optimum point target detection in a single-frame, multicolor image, such as a multispectral infrared or polarimetric synthetic aperture radar picture. Criteria for optimum filtering here include either maximum output signal-to-noise ratio or a (local, adaptive) Gaussian hypothesis test to distinguish between clutter-alone versus target-plus- clutter. The multicovariance filter completely uses all the joint variability of the problem, in both space and frequency, in a way that generalizes both the traditional spatial matched filter and also techniques involving scalar ratios between frequency bands. This full generalization involves possibly very large matrix blocks, which describe statistical correlations in both space and frequency, not just scalar correlation coefficients between two bands at a time. Some simple conceptual models and examples are discussed which help reduce the complexity of what is potentially a very large linear algebra problem.

关键词： signal to noise ratio Target detection Image filtering Electronic filtering Optical filters Gaussian filters Infrared radiation Image segmentation Infrared photography Spatial filters

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LONG-TERM INTEGRATION FOR small TARGET DETECTION

LONG-TERM INTEGRATION FOR SMALL TARGET DETECTION

引用

conference on signal and data processing of small targets 1994

作者： ALLEN, MR SCI APPLICAT INT CORP SAN DIEGOCA 92121

ISBN: (纸本)0819415391

Long term integration, defined as integration along paths through multiple resolvable volumes, can significantly increase moving target detection sensitivity in clutter. Physically, this improvement comes primarily from the ability to smooth over and thus reduce clutter spatial variations that are responsible for its "spiky" statistical behavior. A secondary improvement comes from increased ability to smooth over aspect-dependent target fluctuations. However, since target motion is not known, increased complexity results from the need to implement multiple hypothesized integration paths. Implementation complexity depends on integration duration, spatial resolution, bounds on target speed and maneuverability, and the subsequent path mismatch losses. The current analysis focuses on point defense using a predetermined set of constant radial velocity paths that do not cross fixed beam patterns; i.e., simple "range walks". The integration across resolvable volumes is perlormed noncoherently, although coherent integration can take place within resolvable volumes. Thus, coherent integration across resolvable volumes is not considered. The K-density spatial variation clutter model is used, where the pulse to pulse temporal fluctuation is a correlated complex Gaussian random process whose "local" mean varies independently from scan to scan according to a gamma density. Although any form of coherent processing can be included, an MTI filter is used explicitly. Although propagation effects are not considered, long term integration should provide an additional detection sensitivity improvement by increased ability to smooth over these fluctuations. Starting with conventional integration in a single resolvable volume, detection performance is determined using a novel approximation that involves finding the equivalent number of statistically independent returns before square law detection. This allows the results of Marcum and Swerling to be modified and used as conditional proba

关键词： Target detection Radar Radon Antennas Chromium Defense and security Fourier transforms Numerical integration Information technology Spatial resolution

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RESULTS OF THE JUNE 1993 YUMA GROUND PENETRATION EXPERIMENT

RESULTS OF THE JUNE 1993 YUMA GROUND PENETRATION EXPERIMENT

引用

conference on Aerial Surveillance Sensing Including Obscured and Underground Object Detection

作者： MIRKIN, MI GROSCH, TO MURPHY, TJ AYASLI, S HELLSTEN, H VICKERS, R RALSTON, JM MIT LINCOLN LABLEXINGTONMA 02173

ISBN: (纸本)0819415219

Recently, considerable interest has been expressed in the use of radar to detect underground targets both small (e.g., antipersonnel mines) and large (e.g., buried vehicles). Particular interest has been directed at airborne SAR for this purpose. Several important issues requiring study include the scattering signature of objects buried in soil media, the attenuation and scattering of radar energy in inhomogeneous soils, and the impact of clutter (and particularly the impact of surface clutter layover) on subsurface target detection and recognition. To address these issues, a radar ground penetration experiment was conducted in the desert near Yuma, AZ from June 4 to 15, 1993. In this experiment a number of large and small targets of various shapes were buried at depths up to 3 m, and data was collected using several air- and ground-based radars using both real and synthetic aperture data processing. The variety of radars available covered the range from 20 to 1500 MHz. The data collected was calibrated with sufficient accuracy to permit the measurement of in situ radar signatures, allowing the calculation of ground penetration losses. data from this test have been analyzed to develop a phenomenological understanding of soil penetration losses and clutter backscattering, and to investigate the characteristic signatures of specific buried targets. These data are compared to laboratory soil measurements and modeling studies. This paper will describe the experiment, sensors, sample radar measurements and some of the results of the data analysis.

关键词： Radar Sensors Soil science Target detection Synthetic aperture radar Fourier transforms Antennas Metals Image processing signal processing

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ULTRAWIDEBAND SAR CLUTTER SUPPRESSION BY AR SPECTRAL ESTIMATION

ULTRAWIDEBAND SAR CLUTTER SUPPRESSION BY AR SPECTRAL ESTIMAT...

引用

conference on Algorithms for Synthetic Aperture Radar Imagery

作者： SHAW, SW SRI INT MENLO PKCA 94025

ISBN: (纸本)0819415340

Ultrawideband (UWB) SAR images often contain clutter and other types of spatially correlated noise. To reduce these effects, we have developed a complex, multichannel, autoregressive image- modeling and inverse-filtering algorithm. Our algorithm extends a previously reported technique to include complex image data and a solution to the directionality problem of 2D AR models. A multichannel image pixel, represented by a vector, is modeled as an autoregressive process with a nonsymmetric half-plane (NSHP) support region. The parameters of this NSHP AR model, represented as matrices, are estimated by minimizing a squared residual. To account for possible nonstationarity in the image statistics, the estimation is performed over a series of small subwindows, which lends a degree of adaptabtility to the algorithm. Once estimates of the complex AR parameter matrices are obtained, they are used in an inverse filter. To account for the directionality inherent in the NSHP filter, we have adopted a bi-directional filtering approach, with the results of the two filters combined such that targets are enhanced and clutter is minimized. This algorithm has been applied to various UWB SAR images and shown to be an effective whitening filter. The resulting signal-to-clutter enhancement depends on a variety of factors, including clutter statistics, target size, and AR model order. A version of the whitening algorithm has been incorporated into our standard set of tools for processing UWB SAR images.

关键词： Autoregressive models Synthetic aperture radar Image processing Detection and tracking algorithms Matrices data modeling Algorithm development Image filtering Statistical analysis Image analysis

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Recursive Estimation techniques for detection of small objects in infrared image data

Recursive Estimation techniques for detection of small objec...

引用

1992 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 1992

作者： Soni, Tarun Zeidler, James R. Ku, Walter H. Department of Electrical and Computer Engineering University of California at San Diego San DiegoCA92093-0407 United States Code 804 Naval Ocean Systems Center San DiegoCA92152-5000 United States

ISBN: (纸本)0780305329

This paper describes a recursive detection scheme for point targets in infrared(IR) images. Estimation of the background noise is done using a weighted autocorrelation matrix update method and the detection statistic is calculated using a recursive technique. A weighting factor allows the algorithm to have finite memory and deal with nonstationary noise characteristics. The detection statistic is created by using a matched filter for colored noise, using the estimated noise autocorrelation matrix. The relationship between the weighting factor, the nonstationarity of the noise and the probability of detection is described. Some results on one- and two-dimensional infrared images are presented. © 1992 IEEE.

关键词： Matched filters

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