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检索条件"任意字段=16th Conference on Signal and Data Processing of Small Targets"

共 487 条记录，以下是121-130 订阅

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An adaptive supply-voltage scheme for low power self-timed CMOS digital design 16

An adaptive supply-voltage scheme for low power self-timed C...

引用

16th International conference on VLSI Design/2nd International conference on Embedded Systems Design

作者： Kuang, W Yuan, JS Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)0769518680

this paper combines an adaptive supply-voltage scheme with self-timed CMOS digital design, to achieve low power performance. the supply-voltage automatically tracks the input data rate of the data path so that the supply-voltage can be kept as small as possible while maintaining the speed requirement. this adaptive supply-voltage scheme employs the handshake signals directly to detect the speed of data path without using FIFO buffer. this leads to a very simple logic control whose power loss is negligible. Cadence SPICE simulation shows the effectiveness of this scheme for low power applications based on 0.18 mum CMOS process.

关键词： Capacitance Circuit simulation Clocks Communication system control Delay Dynamic voltage scaling Logic circuits Power dissipation signal detection Timing

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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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Tracking of low-observable small targets against clutter

Tracking of low-observable small targets against clutter

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13th conference on signal and data processing of small targets

作者： Shue, L Venkateswarlu, R Lim, ET Nanyang Technol Univ Ctr Signal Proc Singapore 639798 Singapore

ISBN: (纸本)0819441872

Detection and tracking of low-observable moving targets against heavy clutter in a sequence of infrared images is an important research area. the focus of research in this area is to reliably pick up the most potential targets, track the targets with varying speed and direction, and at the same time reduce the false alarm rate to ala acceptable level. However, there is no single method that works equally well hi all situations. this paper presents an integrated algorithm based on area-correlation tracker (ACT) and Kalman filter for improving ACT performance for targets with varying speed and direction. Divergence and loss of target when the target is stationary are the two typical problems associated with ACT. In our algorithm, we propose to overcome these shortcomings by introducing an online procedure for updating (or not updating in the case of occlusions) the reference template, in conjunction with linear predictions by using a Kalman filter.

关键词： small target image registration area-correlation Kalman filter

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Localization and Backscattering Density Estimation from GPR data with Neural Network 16

Localization and Backscattering Density Estimation from GPR ...

引用

16th International conference on Ground Penetrating Radar (GPR)

作者： Liu, T. Huang, C. L. Su, Y. Xii, W. Natl Univ Def Technol Sch Elect Sci & Engn Changsha 410073 Hunan Peoples R China Acad Xuzhou Engn Xuzhou 221004 Peoples R China

ISBN: (纸本)9781509051816

Anadaptive linear neuron network is employed for reversing the location and back scattering density of objects from ground penetrating radar data. the processing avoids the disadvantage of unknown electromagnetic velocity in a medium for the specific rebar detecting application. Based on the common-offset reflection GPR survey model, the network was derived by reconstructing and compressing the reflected signal matrix. the location and scattering density of the targets under investigation are extracted by fitting the output of the network to the measured data. Finally, experiments with high-resolution configurations confirmed the reliability of the proposed method, and further developments are discussed.

关键词： Ground penetrating radar Neural network Adaptive linear neuron Back scattering density

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LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED small TARGET DETECTION 49

LOCAL INFORMATION GUIDED GLOBAL INTEGRATION FOR INFRARED SMA...

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49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Li, Qiang Mao, Qianchen Liu, Wenjie Wang, Jinbao Wang, Wenmin Wang, Bingshu Northwestern Polytech Univ Sch Software Xian Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of small targets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.

关键词： infrared small target detection local inductive bias information context and cross-level fusion Swin Transformer

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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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Joint detection and tracking of unresolved targets with a joint-bin processing monopulse radar - art. no. 62360T

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

引用

18th conference on signal and data processing of small targets

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

ISBN: (纸本)0819462926

Detection and estimation of multiple unresolved targets with a monopulse radar is limited by the availability of information in monopulse signals. the maximum possible number of targets that can be extracted from the monopulse signals of a single bin is two. Recently two approaches have been proposed in the literature to overcome this limitation. the first is joint-bin processing that exploits target spill-over among adjacent cells by modeling the target returns in the adjacent cells. In addition to making use of the additional information available in target spill-over, it handles a more practical problem where the usual assumption of ideal sampling is relaxed. the second approach is to make use of tracking information in detection through joint detection and tracking with the help of Monte Carlo integration of a particle filter. It was shown that the extraction of even more targets is possible with tracking information. In this paper, a new approach is proposed to combine make the best of these two approaches - a new joint detection and tracking algorithm with multibin processing. the proposed method increases the detection ability as well as tracking accuracy. Simulation studies are carried out. with amplitude comparison monopulse radar for an unresolved target scenario. the relative performances of various methods are also provided.

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

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A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking

A multiple model SNR/RCS likelihood ratio score for radar-ba...

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17th conference on signal and data processing of small targets

作者： Slocumb, BJ Klusman, ME Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. the classical approach uses a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an "auxiliary score" that augments the kinematic score. this paper develops a non-kinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. the formulation requires an estimate of the target mean RCS, and a key challenge is the trackina of the mean RCS through significant "jumps" due to aspect dependencies. A novel multiple model approach is used track through the RCS jumps. three solution are developed: one based on an a-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.

关键词： feature-aided tracking RCS tracking multiple model signal score swerling fluctuation

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Mysterious computational complexity of particle filters

Mysterious computational complexity of particle filters

引用

14th conference on signal and data processing of small targets

作者： Daum, F Huang, J Raytheon Elect Syst Sudbury MA 01776 USA

ISBN: (纸本)0819444782

Particle filtering (PF) is a relatively new method to solve the nonlinear filtering problem, which is very general and easy to code. the main issue with PF is the large computational complexity. In particular, for typical low dimensional tracking problems, the PF requires 2 to 4 orders of magnitude more computer throughput than the EKF, to achieve the same accuracy. It has been asserted that the PF avoids the curse of dimensionality, but there is no formula or theorem that bounds or approximates the computational complexity of the PF as a function of dimension (d). In this paper, we will derive a simple back-of-the-envelope formula that explains why a carefully designed PF should mitigate the curse of dimensionality for typical tracking problems, but that it does not avoid the curse of dimensionality in general. this new theory is related to the fact that the volume of the d dimensional unit sphere is an amazingly small fraction of the d dimensional unit cube, for large d.

关键词： Error analysis Monte Carlo methods Nonlinear filtering Particles Particle filters Filtering (signal processing) Optical spheres Electronic filtering Probability theory Statistical analysis

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data association in target tracking using priority queues

Data association in target tracking using priority queues

引用

17th conference on signal and data processing of small targets

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

ISBN: (纸本)0819459186

data association is one of the main components of target tracking. While, in its simplest form, data association links a list of tracks to a list of measurements or links two lists of measurements (2-D association), the more complex problem involves assignment of multiple number of such lists (S-D association where S > 3). In target tracking, the presence of false detections (false alarms) and the absence of detections from some targets (missed detections) complicate the problem of data association further. In this work, we explore the possibility of applying track ordering in priority queues to solve the association problem more efficiently. the basic component of our algorithm is to form priority queues by permutations of the tracks. Each queue is served on a first-come-first-served basis, i.e., each track is assigned to the best measurement available based on its turn in the queue. It can be shown that the best solution to the 2-D problem can be obtained from one of these queues. However, the solution is computationally expensive even for a moderate number of targets. In this paper we show that due to redundancy only a small fraction of the total number of permutations is required to be evaluated to obtain the best solution.

关键词： Multisensor-multitarget tracking 2-D assignment algorithms priority queue m-best solutions

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