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检索条件"任意字段=13th Conference on Signal and Data Processing of Small Targets"

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Tracking Position of Airborne Target on SPx-Radar-Simulator Using Probabilistic data Association Filter 13

Tracking Position of Airborne Target on SPx-Radar-Simulator ...

引用

13th International conference on Information and Communication Technology and System (ICTS)

作者： Sahal, Mochammad Said, Zaidan Adenin Kadir, Rusdhianto Effendi Abdul Hidayat, Zulkifli Bilfaqih, Yusuf Alkaff, Abdullah Inst Teknol Sepuluh Nopember Dept Elect Engn Lab Syst & Cybernet Surabaya Indonesia

ISBN: (纸本)9781665440592

Radar has various functions, one of which is tracking the position of targets in the air. In tracking the target position, there are obstacles, namely the uncertainty of data associations. To overcome the uncertainty of data associations, the concept of data association can be used where one of the algorithms that use this concept is the Probabilistic data Association Filter (PDAF). A single target position tracking system in the air will be tested on radar using the PDAF with our proposed maintenance track algorithm. the data used for testing comes from simulating two motions on the SPx-Radar-Simulator. False alarms originating from interference (clutter) will be generated in the environment around the simulated target. the test results of the target tracking system using the PDAF algorithm that has been designed can track targets well, maintain tracking conditions in an environment that has clutter, and maintain track on multitarget environments. the error between the original data and the prediction on the validated target has a relatively small value, although there is a relatively significant difference in the error of the altitude data when the motion has varying altitude conditions.

关键词： data Association PDAF Target Tracking Radar SPx-Radar-Simulator

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Maximum likelihood angle extractor in the presence of sea-surface multipath

Maximum likelihood angle extractor in the presence of sea-su...

引用

14th conference on signal and data processing of small targets

作者： Sinha, A Blair, WD Kirubarajan, T Bar-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06268 USA

ISBN: (纸本)0819444782

In the presence of sea-surface multipath monopulse radar signals from a low elevation target have three alternative paths in addition to the direct (radar-to-target) path due to reflections from the sea surface. the specular reflection causes significant signal fading. the diffuse reflection causes an approximately constant bias to the in-phase component of the monopulse ratio, which is the standard extractor of the direction of arrival (DOA) in the monopulse processing. the diffuse reflection also causes higher standard deviation to the in-phase component of the monopulse ratio. In this paper we propose a maximum likelihood (ML) angle extraction technique for low elevation targets of known average signal strength having a Rayleigh fluctuation. the results show that this method reduces the error of the estimated angle compared to the conventional monopulse ratio estimator. Subsequently, the ML angle extractor is modified for the unknown average signal strength case. this modified angle extractor has only a small performance degradation compared with the known average signal strength case, but it performs much better than the monopulse ratio based estimator. this angle extractor reduces the root mean square error (RMSE) by more than 50% in the signal processing stage when used in a low flying target (sea skimmer) tracking scenario.

关键词： Radar Reflection signal to noise ratio Specular reflections Error analysis signal processing Detection and tracking algorithms Silicon Analytical research Computer engineering

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Discriminating small extended targets at sea from clutter and other classes of boats in infrared and visual light imagery

Discriminating small extended targets at sea from clutter an...

引用

20th conference on signal and data processing of small targets

作者： van den Broek, Sebastiaan P. Bouma, Henri Degache, Marianne A. C. TNO Def Secur & Safety NL-2509 JG The Hague Netherlands

ISBN: (纸本)9780819471604

Operating in a coastal environment, with a multitude of boats of different sizes, detection of small extended targets is only one problem. A further difficulty is in discriminating detections of possible threats from alarms due to sea and coastal clutter, and from boats that are neutral for a specific operational task. Adding target features to detections allows filtering out clutter before tracking. Features can also be used to add labels resulting from a classification step. Both will help tracking by facilitating association. Labeling and information from features can be an aid to an operator, or can reduce the number of false alarms for more automatic systems. In this paper we present work on clutter reduction and classification of small extended targets from infrared and visual light imagery. Several methods for discriminating between classes of objects were examined, with an emphasis on less complex techniques, such as rules and decision trees. Similar techniques can be used to discriminate between targets and clutter, and between different classes of boats. Different features are examined that possibly allow discrimination between several classes. data recordings are used, in infrared and visual light, with a range of targets including rhibs, cabin boats and jet-skis.

关键词： classification clutter reduction target recognition small targets boats jet-ski

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Simulation assessment of RCS-aided multiple target tracking

Simulation assessment of RCS-aided multiple target tracking

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19th conference on signal and data processing of small targets

作者： Dunham, Darin T. Ehrman, Lisa. M. Blair, W. Dale Frost, Susan A. Vectraxx Inc Glen Allen VA 23059 USA Georgia Inst Technol Sensors & Electromagnet Applicat Lab Georgia Tech Res Inst Atlanta GA 30332 USA Vectraxx Inc Ft Collins CO 80521 USA

ISBN: (纸本)9780819468475

Closely-spaced (but resolved) targets pose a significant challenge for single-frame unique measurement-to-track data association algorithms. this is due to the similarity of the Mahalanobis distances between the closely-spaced measurements and tracks. Contrary to conventional wisdom, adding target feature information (e.g target amplitude) does not necessarily improve the probability of correctly assigning measurements to tracks. In this paper, the theoretical limitations of using radar cross section (RCS) data to aid in measurement-to-track association are reviewed. the results of a hi,h-fidelity simulation assessment of the benefits of RCS-aided measurement-to-track association (using the signal-to-Noise Ratio) are given and other possibilities for RCS-aided tracking are discussed. Namely, we show the analytical results of our investigation into using RCS information to determine the presence of merged measurements.

关键词： signal to noise ratio Target detection Sensors Switches Kinematics Radar Mahalanobis distance signal processing Time metrology Detection and tracking algorithms

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Joint detection and tracking of unresolved targets with a monopulse radar using a particle filter - art. no. 59131T

Joint detection and tracking of unresolved targets with a mo...

引用

17th conference on signal and data processing of small targets

作者： Nandakumaran, N Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this paper, a new algorithm is developed to jointly detect and track more than two targets from a single detected bin. this method involves the use of tracking data in detection. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which is proved to be effective for nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. It in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. that is, we evaluate the likelihoods of different hypotheses of possible combinations of targets being in different detected bins. the hypothesis testing is used to find the correct detection event. the particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data with more than unresolved two targets. Simulation results confirm the possible extraction and tracking of more than two targets jointly.

关键词： U nresolved targets closely-spaced targets monopulse radar multitarget tracking particle filters

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Comparison of tracklet methods with deterministic dynamics and false signals - art. no. 62360Z

Comparison of tracklet methods with deterministic dynamics a...

引用

18th conference on signal and data processing of small targets

作者： Drummond, Oliver E. Dana-Bashian, David CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)0819462926

Track fusion processing is complicated because the estimation errors of a local track and a fusion track for the same target are usually cross-correlated. If these errors are cross-correlated, that should be taken into account when designing the data association processing and the filter used to combine the track data. An approach to dealing with this cross-correlation is to use tracklets. One of the important issues in tracklet fusion performance is whether the dynamics are deterministic, e.g., no filter process noise and no target maneuver. A number of different tracklet methods have been designed. this paper presents a comparison a tracklets-from-tracks approach to a tracklets-from-measurements approach. Tracklet fusion performance is also compared to centralized measurement fusion performance. the emphasis is on performance with targets that exhibit deterministic dynamics and the possibility of measurements caused by false signals.

关键词： target tracking multiple sensor tracking track fusion multiple target tracking sensor data fusion Bayesian methods tracklet distributed tracking estimation and filtering

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small Segment Emphasized Performance Evaluation Metric for Medical Images

Small Segment Emphasized Performance Evaluation Metric for M...

引用

13th International conference on signal processing and Communications (SPCOM)

作者： Ammu, R. Sinha, Neelam Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9781728188959

Automatic image segmentation and quantification are critical steps in medical image analysis. the main challenges in medical image segmentation are due to the imbalance in data distribution and spatial variations of ROI. the ideal segmentation should extract all kinds of segments irrespective of size, shape and position. Commonly used metrics such as accuracy, IOU, Dice similarity coefficient consider all the detected pixels in a similar way. However, the detection of smaller segments is critical in medical analysis since it helps in early treatment of the disease and are also easier to miss. Hence, segmentation evaluation must accord larger weighting to pixels in smaller segments compared to the bigger ones. We propose a novel evaluation metric for segmentation performance, emphasizing smaller segments, by assigning a higher weightage to those pixels. Weighted false positives are also considered in deriving the new metric named, "SSEGEP" (small SEGment Emphasized Performance evaluation metric), (range : 0 (Bad) to 1 (Good)). the proposed approach has been applied to two different publicly available real medical data sets of CT modality consisting of scans of the liver and pancreas of 131 and 107 subjects respectively and the results have been compared with existing evaluation metrics. Statistical significance testing is performed to quantify the relevance of the proposed approach. In comparison to Dice similarity coefficient, SSEGEP resulted in a promising p-value of the order 10(-18) for hepatic tumor. the proposed metric is found to perform better for the images having multiple segments for a single label and where the regions of interest are not localized.

关键词： Pixels

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Assurance regions in tracking

Assurance regions in tracking

引用

20th conference on signal and data processing of small targets

作者： Sworder, David D. Boyd, John E. Hutchins, R. G. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Cub Def Applicat San Diego CA 92186 USA Naval Postgrad Sch Dept EECE Monterey CA 93943 USA

ISBN: (纸本)9780819471604

An assurance region at level p, A(P=p), is an area in motion space that contains the target with assigned probability p. It is on the basis of A(P=p) that an action is taken or a decision made. Common model-based trackers generate a synthetic distribution function for the kinematic state of the target. Unfortunately, this distribution is very coarse, and the resulting A(P=p) lack credibility. It is shown that a, map-enhanced, multiple model algorithm reduces the tracking, error and leads to a compact assurance region.

关键词： nonlinear filters target tracking data fusion

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Particle Filter Tracking for the Banana Problem

Particle Filter Tracking for the Banana Problem

引用

25th SPIE conference on signal and data processing of small targets

作者： Romeo, Kevin Willett, Peter Bar-Shalom, Yaakov Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780819497079

In this paper we present an approach for tracking with a high-bandwidth active sensor in very long range scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems;and most flavors of particle filter suffer from a loss of diversity among particles after resampling. this leads to sample impoverishment and the divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. the regularization is accomplished using a modified version of the Epanechnikov kernel.

关键词： Particles Particle filters Detection and tracking algorithms Filtering (signal processing) Gaussian filters Digital filtering Error analysis Sensors Active sensors Statistical analysis

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A dynamic approach to efficiently clustering and predicting groups for moving targets

A dynamic approach to efficiently clustering and predicting ...

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16th conference on signal and data processing of small targets

作者： Ma, DY Zhang, AD SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (纸本)081945351X

Effective and efficient approaches to monitor and manage maneuvering objects are of great importance in various applications, such as wide battlefields, traffics, and wireless communications. Modern airborne radar sensors can provide wide-area surveillance coverage of ground activities. the huge volume of radar data renders it impractical and inefficient to examine all the activities of individual moving object. Clustering moving objects and predicting motion tendencies of large groups are becoming a crucial issue for optimizing resource distribution and formulating sound decisions. However, most traditional clustering techniques are static-object-oriented and not effective at clustering maneuvering objects. In addition, the radar data intermittence and noise data, which are caused by extraneous objects and stationary clutter background, are major difficulties in clustering and predicting groups. In this paper, we present a dynamic-object-oriented clustering approach to detecting and predicting large group activities over time. We propose a "core member" concept to support dynamic-object-oriented clustering and to mitigate the effects of data intermittence and noise data. In general, some special targets always tend to remain in a constant group and stay near the center of that group. To a large extent, the movement of these targets represents the activity of the entire group. To exploit this characteristic, we consider these special targets to be core members of their own cluster. the movements of the core members can help us detect clusters and predict their future movements. the performance and results of the application of our approach to CASTFOREM data sets are also presented.

关键词： Radar Detection and tracking algorithms Filtering (signal processing) Target detection Electronic filtering Sensors data modeling Surveillance Wireless communications signal detection

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