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共 168 条记录，以下是31-40 订阅

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Detection of small objects in multi-layered infrared images

Detection of small objects in multi-layered infrared images

引用

20th conference on signal and data processing of small targets

作者： Wang, Jing Bao, Shangqi Ralph, Jason F. Goulermas, John Y. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9780819471604

this paper uses super-resolution methods to detect small objects in infrared image sequences from a simulated airborne platform, using image registration techniques for automatic sightline stabilisation. the scene consists of multiple layers, corresponding to a static background scene and layers of cloud cover at varying heights. the motivation is to evaluate the performance of super-resolution methods in the presence of three-dimensional structured infrared clutter.

关键词： object detection super-resolution automatic sightline stabilisation infrared imagery clouds

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PHD filters of second order in target number

PHD filters of second order in target number

引用

18th conference on signal and data processing of small targets

作者： Mahler, Ronald Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)0819462926

the multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In an earlier paper at this conference we derived a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number. Since the CPHD filter has computational complexity O(m(3)) in the number m of measurements, additional approximation is desirable. In this paper we discuss a second-order approximation called the "binomial filter."

关键词： Digital filtering Nonlinear filtering Sensors Target detection Electroactive polymers Filtering (signal processing) Motion models Picosecond phenomena Electronic filtering Linear filtering

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Real-world tracking system performance evaluation

Real-world tracking system performance evaluation

引用

14th conference on signal and data processing of small targets

作者： Blackman, SS Dempster, RJ Raytheon Elect Syst El Segundo CA 90245 USA

ISBN: (纸本)0819444782

Preliminary tracking system design and analysis is typically done using simulated data in which the target truth is known and many techniques have been developed for evaluating performance under these conditions. However, there is a notable lack of any consistent approach for evaluating tracker performance for "real" data in which there may be an unknown number of "targets of opportunity" whose trajectories are not known. In addition, the background clutter/false alarm environment may be unknown so that an important analysis task is to determine the most accurate background models. this paper proposes a set of criteria for evaluating the tracks that are formed using "real" data collected in the field in the presence of an unknown number of "targets of opportunity". these criteria include duration, update history, and measures of kinematic and data association consistency. A scoring method is developed and the use of these criteria for system design is discussed.

关键词： multiple target tracking performance evaluation real data

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Target selection using neural networks

Target selection using neural networks

引用

14th conference on signal and data processing of small targets

作者： Özener, MS Yardimci, Y Univ Enformat Enstitusu Orta Dogu Tekn TR-06351 Ankara Turkey

ISBN: (纸本)0819444782

Target selection is the task of assigning a value or priority to various targets in a scenario. this priority is usually determined by the threat the target poses on the defender in addition to its vulnerability to possible measures to be taken by the defender. In this study, we describe a target selection technique based on neural networks. the utility or value of each target is assumed to be an unknown function acting on certain features of the target such as size, intensity, speed and direction of movement. Neural networks used in the context of function estimation is a viable candidate for determining this unknown function for generating target priorities. Various. neural network configurations are examined and simulation results are presented.

关键词： Neural networks Target detection Neurons Ranging signal processing Image processing Infrared imaging Infrared radiation Sensors databases

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A multiple model multiple hypothesis filter for tracking maneuvering targets

A multiple model multiple hypothesis filter for tracking man...

引用

13th conference on signal and data processing of small targets

作者： Driessen, H Boers, Y THALES NEDERLAND NL-7554 RR Hengelo Netherlands

ISBN: (纸本)0819441872

In this paper a new method is presented to deal with multiple model filtering. the method is the so called Multiple Model Multiple Hypothesis Filter (MMMH filter). For each hypothesis a Kalman filter is running. this hypothesis represents a specific model mode sequence history. the proposed method has a high level of genericity and is highly flexible. the main feature is that the number of hypotheses that are maintained varies with the 'difficulty' of a scenario. It is shown that the MMMH performs better than the widely used Interacting Multiple Model (IMM) filter.

关键词： target tracking mutiple model filtering radar systems

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Peak inspecting and signal recovery methods based on triple correlation - art. no. 623608

Peak inspecting and signal recovery methods based on triple ...

引用

18th conference on signal and data processing of small targets

作者： Cao Hanqiang Li Xutao Chen Rujun Wei Jiaxin Cao Chao Huazhong Univ Sci & Technol Dept Elec & Info Eng Wuhan 430074 Peoples R China

ISBN: (纸本)0819462926

Weak target inspecting and recovering are very important in IR detecting systems. In this paper, triple correlation peak inspecting techniques(TCPIT) are adopted for the signal processing of IR systems in detecting sub-pixel or point targets. Investigations show that the signal-to-noise ratio (SNR) improvement of approximate 23dB can be obtained with the input peak SNR of 0.84 and the input power SNR of -0.93dB. the triple correlation overlapping sampling technique(TCOST) is advanced for restoring signal waveforms of IR detection systems. Investigations show that signal waveforms can effectively be restored in the low signal-to-noise ratio circumstances using this approach.

关键词： triple correlation weak signal inspecting detection of point target IR detecting system

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Simulation of signal and data processing for a pair of GEO IR sensors

Simulation of signal and data processing for a pair of GEO I...

引用

19th conference on signal and data processing of small targets

作者： Keil, Karl-Heinz Hupfer, Werner EADS Astrium GmbH Bremen Germany

ISBN: (纸本)9780819468475

this paper describes experiences and results from developing a basic signal and data processing simulation for a pair of GEO IR sensors observing the boost phase of theater Ballistic Missiles (TBM). the goal of such a system is the detection of launched TBM, also against a cloud background, and the tracking from cloud break ideally up to boost-end. Two GEO satellites are used for stereo view of one and the same non-global limited Field-of-Regard (FOR). they are positioned in such a way that both cover the FOR and provide a sufficient triangulation baseline. signal processing is applied for each of both passive IR sensors in order to detect and track the TBM on the focal plane. the applied approach can be summarized under the term 'velocity filtering'. data processing operates on the 2-D signal processing input from both IR sensors, i.e. azimuth and elevation line-of-sight (LOS) angles as well as their rates. the goal is to provide 3-D tracks of the targets, which can be used to cue early warning or fire control radars. the underlying simulation model constitutes a prototype and vehicle for further research. Nevertheless, even in its current stage it provides a first tool for the analysis and evaluation of corresponding sensor design concepts.

关键词： signal processing data processing Satellites Filtering (signal processing) Digital filtering Sensors Clouds Infrared sensors Target detection Optical filters

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Target classification with data from multiple sensors

Target classification with data from multiple sensors

引用

14th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)0819444782

the methods used in the classification of multiple small targets can be very different from the methods commonly used in traditional pattern recognition. First, there may be characteristics of the features for. each target class that can permit simpler computations than other features. In addition, in classifying targets, the target tracks are updated as new data becomes available and hence there can be a sequence of feature measurements that are available for the target classification process. In addition, with multiple targets, the a priori information may be in a form that make the classification processing for one target dependent on the classification processing of other targets. these aspects of target classification that make that processing different from traditional pattern recognition are the concern of this paper. To limit the length of the paper, the scope is restricted to classification tasks that allow the linear-Gaussian assumption to be used. Also, the data used in the classification process is restricted to features, i.e., no attributes, and the assumption is the tracker does not employ feature-aided tracking. While these assumptions simplify the discussion, the methods used could be modified to permit classification of a broad scope of classification tasks.

关键词： multiple target tracking features target typing detection classification recognition identification and discrimination automatic target recognition (ATR) multiple models Bayesian methods and Kalman filter

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Background modeling and target segmentation via modified Kalman filtering

Background modeling and target segmentation via modified Kal...

引用

16th conference on signal and data processing of small targets

作者： Searle, SJ Univ Adelaide Sch Elect & Elect Engn CSSIP Adelaide SA 5005 Australia

ISBN: (纸本)081945351X

In target detection and tracking applications with imagery data taken from a moving camera platform, it is necessary to segment potential targets in each image frame. this is typically done by preprocessing individual images to exploit some known attribute about the data. Often these methods make many false detections, particularly in the presence of additive noise, and the results thus require significant post-processing. A means of estimating the background in the imagery sequence under the formalism of the Kalman filter is suggested. this background estimate is then used to recast the segmentation problem as one of outlier detection, and the result of segmentation is used to modify the filter update. Ways of making the technique computationally benign are discussed. the technique is used to analyse a simulated image sequence, and the performance is compared to that of a single-frame back-round-estimation technique. the feasibility of target segmentation via background tracking is thus demonstrated.

关键词： Image segmentation Filtering (signal processing) Target detection Matrices Cameras Image filtering Digital filtering Electronic filtering Optical filters Image analysis

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Robust scale invariant small target detection using the Laplacian scale-space theory

Robust scale invariant small target detection using the Lapl...

引用

20th conference on signal and data processing of small targets

作者： Kim, Sungho Yang, Yukyung Lee, Joohyoung Park, Yongchan Agcy Defense Dev Taejon 305600 South Korea

ISBN: (纸本)9780819471604

this paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). the conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. the scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. the scale invariant feature can detect different sizes of targets robustly. Experimental results with real FUR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.

关键词： small target robust detection varying size scale invariant scale-space

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