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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 1999"

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Adaptive multispectral detection of small targets using spatial and spectral convergence factor

Adaptive multispectral detection of small targets using spat...

引用

conference on signal and data processing of small targets 1996

作者： Nolibe, L Borgnino, J Ducoulombier, M Artaud, M DCN CTR TECH SYST NAVALS LUTTE SURFACE AIRF-83800 TOULONFRANCE

ISBN: (纸本)0819421405

The main infrared search and track systems (IRST) purpose is to realize optimal discrimination between true targets and background clutter (false alarm). In such single band systems, two dimensional least mean square (TDLMS) adaptive filter achieve good results in small target detection. However, detection performance are strongly dependent on background correlation length. When difference between background and target correlation length is too small, performance detection decreases. The method presented in this paper is applied to a naval dual band panoramic surveillance system for target detection at low elevation angles. It consists in adjusting the time-varying convergence factor of TDLMS filter, not only by using spatial statistics, but also by integrating a local spectral parameter. The use of this information is based on theoretical spectral radiance discrimination in LWIR and MWIR bands, between targets and backgrounds. When the local spectral parameter matches spectral background response, the filter reactivity is optimum via the spatial convergence factor, whereas it is decreased in the presence of spectral target characteristics. We achieve in this way a better target-to-clutter discrimination and independently of background correlation length.

关键词： adaptive filtering multispectral processing target detection IRST systems

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Retrodiction for Bayesian multiple hypothesis/multiple target tracking in densely cluttered environment

Retrodiction for Bayesian multiple hypothesis/multiple targe...

引用

conference on signal and data processing of small targets 1996

作者： Koch, W FGAN FORSCHUNGSINST FUND & MATHD-53343 WACHTBERGGERMANY

ISBN: (纸本)0819421405

Sensor data processing in a dense target/dense clutter environment is inevitably confronted with data association conflicts which correspond with the multiple hypothesis character of many modern approaches (MHT: multiple hypothesis tracking). In this paper we analyze the efficiency of retrodictive techniques that generalize standard fixed interval smoothing to MHT applications. 'Delayed estimation' based on retrodiction provides uniquely interpretable and accurate trajectories from ambiguous MHT output if a certain time delay is tolerated. In a Bayesian framework the theoretical background of retrodiction and its intimate relation to Bayesian MHT is sketched. By a simulated example with two closely-spaced targets, relatively low detection probabilities, and rather high false return densities, we demonstrate the benefits of retrodiction and quantitatively discuss the achievable track accuracies and the time delays involved for typical radar parameters.

关键词： retrodiction dense target/dense clutter environment probabilistic data association (PDA) multiple hypothesis tracking (MHT) target/identification/classification

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Gray scale morphology for small object detection

Gray scale morphology for small object detection

引用

conference on signal and data processing of small targets 1996

作者： Sang, N Zhang, TX Wang, GY HUAZHONG UNIV SCI & TECHNOL INST PATTERN RECOGNIT & ARTIFICIAL INTELLIGENCEWUHAN 430074HUBEIPEOPLES R CHINA

ISBN: (纸本)0819421405

In this paper, we present morphological processing using median operation for small object detection. First, we perform median morphological operation on the gray-scale image with structuring element A and make all scene regions of size equal to the central A's area or larger brighter (for bright objects) or darker (for dark objects) and other regions approximate invariably. Second, we perform median morphological operation on the gray-scale image with larger structuring element B and make all scene regions of size equal to the central B's area or smaller darker (for bright objects) or brighter (for dark objects) and other regions approximate invariably. Third, we calculate the absolute difference of above two outputs. All object regions between the smallest and largest will be enhanced and all background regions will be weakened. Then a simple threshold can extract all objects with some smaller background regions. Finally, those smaller background regions whose areas are smaller than structuring element A can be eliminated by region labeling processing. We find that if (1) contrary to background, the object regions have the signature of discontinuity with their neighbor regions. (2) Each object concentrates relatively in a small region, which can be considered as a homogeneous compact region, our algorithm can achieve satisfactory detection performance.

关键词： object detection mathematical morphology FSP(function- and set-processing) filters

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Nonlinear parameter filtering on the interference with variable structure

Nonlinear parameter filtering on the interference with varia...

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signal and data processing of small targets 1996

作者： Cherdyntsev, Valery A. Cozel, Viktor M. Byelorussian State Univ. of Informatics and Radioelectronics Minsk Belarus

ISBN: (纸本)0819421405

The method of synthesis and analysis of the small signals processing on the variable interference is presented. The synthesis is based on the interference models description by means of probability density sets. It is assumed that the change of interference structure described by Markov's process. The main results of synthesis and analysis of signals processing in the conditions with various types of interference are associated with the necessity of adaptive nonlinear processing of a received signal and variable interference. The adaptive nonlinear processing may attenuate an intensive interference.

关键词： signal processing

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small target detection from image sequences using recursive max filter

Small target detection from image sequences using recursive ...

引用

signal and data processing of small targets 1995 conference

作者： NISHIGUCHI, K KOBAYASHI, M ICHIKAWA, A MITSUBISHI ELECTR CORP AMAGASAKIHYOGOJAPAN

ISBN: (纸本)0819419206

This paper presents a new method for detection of small moving targets from noisy image sequences using a recursive filter, which contains a local maximum filter in the feedback loop. Since all the procedures contained in this method can be implemented by fully parallel algorithm, this method can be realized by a neural network and real-time processing is possible for image sequences at high frame rates. First we investigate the performance of the method on the assumption that the targets move randomly in an image plane and the background noise is white both in time and space. The results show that the proposed method has the ability to suppress the background noise and enhances small moving targets, and that the targets are detected only by thresholding after the processing of the recursive filter. There can be other backgrounds such as clutters and stationary objects. It is also shown that the above method is applicable after preprocessing of conventional time difference method, and is able to suppress these backgrounds. Next we analyze the performance of the method using the property of order statistics. The performance measure is the output signal-to-noise ratio and is represented analytically as a function of input SNR and the parameters included in the method. Finally, the recursive filter is optimized using the result of the analysis.

关键词： Digital filtering Target detection signal to noise ratio Image filtering Detection and tracking algorithms Image processing Statistical analysis Computer simulations Feedback loops Evolutionary algorithms

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Multisensor tracking of ballistic targets

Multisensor tracking of ballistic targets

引用

signal and data processing of small targets 1995 conference

作者： FRENKEL, G INST DEF ANAL ALEXANDRIAVA 22311

ISBN: (纸本)0819419206

This paper addresses the multisensor tracking of targets but considers only the special case of targets on a ballistic trajectory. The scenario consists of two radars tracking the same target. One of these radars periodically sends a track to the other radar for fusion with the track generated by the recipient. A track fusion algorithm for tracking ballistic targets is derived. This algorithm is exercised and illustrated by the Sensor Fusion Architecture Model (SFAM) computer program. Since the repeated track fusion of ballistic trajectories results in correlation that must be removed to preserve the optimality in the resultant estimate, an algorithm that requires the preservation of the last update and the error covariance matrix from another Kalman Filter (KF) also is presented. These data then are used to decorrelate the two track inputs originating at the same-source KF.

关键词： BALLISTIC MISSILE KALMAN FILTER (KF) RADAR SENSOR FUSION TRACKING

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Evaluation of data association techniques in a real multi-target radar tracking environment

Evaluation of data association techniques in a real multi-ta...

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signal and data processing of small targets 1995 conference

作者： HU, ZJ LEUNG, H BLANCHETTE, M ATLANTIS SCI SYST GRP INC OTTAWAON K2C 0P9CANADA

ISBN: (纸本)0819419206

This paper assesses tracking performance of a number of commonly-used data association techniques, including the nearest-neighbor (NN) data association with optimal and sub-optimal assignments, the weighted-average and nearest-neighbor version of the probabilistic data association (PDA), joint probabilistic data association (JPDA), cheap JPDA, and sub-optimal JPDA. The real radar tracking data used for the performance evaluation in this paper contain multiple maneuvering and non-maneuvering air targets in various clutter conditions. The study shows that all the data association methods perform well when the targets are well separated with near straight-line trajectories. In the case of closely spaced and maneuvering targets, the NN and NN version of JPDA methods are more effective than the weighted-average PDA and JPDA methods.

关键词： MULTIPLE TARGET TRACKING data ASSOCIATION NEAREST-NEIGHBOR data ASSOCIATION PROBABILISTIC data ASSOCIATION JOINT PROBABILISTIC data ASSOCIATION

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Range information extraction from tracking data using object kinematic parameters

Range information extraction from tracking data using object...

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signal and data processing of small targets 1995 conference

作者： SHEN, MZ CHINESE ACAD SCI INST OPT & ELECTRCHENGDU 610209PEOPLES R CHINA

ISBN: (纸本)0819419206

This paper will propose a way to extract target position/range information from the angle-only tracking data with the priori knowledge of the target movement. The target trajectory can be expressed as a function of time, called target function, with n parameters. If m parameters (e.g. target original position, target speed, target acceleration, etc.) are known, or can be estimated to some extent of accuracy, then there remain n-m unknown parameters in the target function. In order to determine these unknown parameters, we should take at least n-m set of tracking data at different times, and construct a system of n-m simultaneous equations. Once all parameters become known by solving this system of equations, the target position and target range can be obtained by substituting the specific time value into the target function. Some typical cases are investigate and the application conditions discussed. Computer simulations are also presented.

关键词： OPTICAL TRACKING data PROCESS PASSIVE RANGING

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Comparison of registration error correction techniques for air surveillance radar network

Comparison of registration error correction techniques for a...

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signal and data processing of small targets 1995 conference

作者： LEUNG, H BLANCHETTE, M GAULT, K DEF RES ESTAB OTTAWA SURFACE RADAR SECTOTTAWAONCANADA

ISBN: (纸本)0819419206

This paper considers the effectiveness of different registration algorithms in an air surveillance radar tracking environment. The evaluation is based on real data collected from an operating radar network in Canada. The existing real time quality control (RTQC) routine in the network requires targets to be on both sides of the line between the radars; this type of distribution is usually not available in the area with low air traffic density. The least squares (LS) algorithm which solves for the least squares biases is an alternative to handle this problem. Two algorithms that are considered as being more rigorous in the literature are the maximum likelihood (ML) and generalized least squares (GLS). The ML algorithm is a solution of the registration problem that uses a calculated covariance matrix of the measurement noise. The GLS algorithm is a variation of the ML algorithm where only the variances of the measurement noise are used. Based on the real data analysis, the track separation after registration are identical for the LS and ML algorithm, with the GLS being close most of the time. The result of the RTQC algorithm is close when the radar returns are split evenly between the two sides of the line between the radars. If the data in unevenly distributed, then the RTQC algorithm provides less accurate estimates. Comparing their computational complexities, the RTQC and LS algorithm are very efficient while the ML and GLS algorithm are about hundred times slower in terms of computational speed.

关键词： Radar Image registration Surveillance Detection and tracking algorithms Sensors Control systems data analysis Distance measurement Computing systems Environmental monitoring

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Reliable motion detection of small targets in video with low signal-to-clutter ratios

Reliable motion detection of small targets in video with low...

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Proceedings of the 29th Annual 1995 International Carnahan conference on Security Technology

作者： Nichols, Scott A. Naylor, R.Brian Sandia Natl Lab Albuquerque United States

This paper describes a highly adaptive video motion detection and tracking algorithm that has been developed as part of Sandia's Advanced Exterior Sensor (AES) program. The AES detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include the following: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to-clutter ratios;reliable tracking of targets that show motion that is non-inertial;automatic adaptation to both infrared and visible imagery with variable quality;and suppression of false alarms caused by sensor flaws or cutouts, or both.

关键词： Video signal processing

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