检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

768 篇 会议
15 篇 期刊文献

馆藏范围

783 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

615 篇 工学
- 329 篇 电气工程
- 187 篇 计算机科学与技术...
- 175 篇 信息与通信工程
- 126 篇 软件工程
- 94 篇 测绘科学与技术
- 93 篇 航空宇航科学与技...
- 48 篇 仪器科学与技术
- 41 篇 控制科学与工程
- 33 篇 电子科学与技术（可...
- 26 篇 光学工程
- 21 篇 机械工程
- 10 篇 建筑学
- 10 篇 土木工程
- 7 篇 船舶与海洋工程
- 7 篇 安全科学与工程
- 6 篇 水利工程
- 6 篇 化学工程与技术
- 6 篇 生物医学工程（可授...
- 5 篇 核科学与技术
- 5 篇 生物工程
404 篇 理学
- 338 篇 物理学
- 63 篇 数学
- 24 篇 统计学（可授理学、...
- 13 篇 海洋科学
- 9 篇 化学
- 9 篇 生物学
- 7 篇 地球物理学
104 篇 医学
- 103 篇 临床医学
16 篇 管理学
- 8 篇 管理科学与工程(可...
- 8 篇 图书情报与档案管...
7 篇 军事学
3 篇 经济学
2 篇 法学
2 篇 教育学

主题

90 篇 signal processin...
75 篇 target detection
73 篇 sensors
64 篇 target tracking
63 篇 detection and tr...
60 篇 radar
54 篇 signal to noise ...
44 篇 filtering (signa...
42 篇 object detection
37 篇 data association
37 篇 data fusion
33 篇 signal detection
33 篇 signal processin...
31 篇 error analysis
31 篇 electronic filte...
29 篇 feature extracti...
29 篇 radar signal pro...
26 篇 data processing
26 篇 clutter
24 篇 data modeling

机构

19 篇 univ connecticut...
10 篇 cyberrnd inc cul...
7 篇 univ calif san d...
6 篇 numerica corp lo...
6 篇 univ connecticut...
5 篇 key laboratory o...
5 篇 natl. univ. of d...
5 篇 numerica corp lo...
4 篇 qinetiq ltd malv...
4 篇 univ connecticut...
4 篇 raytheon co walt...
4 篇 national key lab...
4 篇 national laborat...
3 篇 beijing key labo...
3 篇 nanjing research...
3 篇 mcmaster univ de...
3 篇 numerica corp ft...
3 篇 sri int menlo pk...
3 篇 mitre corp bedfo...
3 篇 radar research l...

作者

19 篇 kirubarajan t
18 篇 bar-shalom y
17 篇 bar-shalom yaako...
15 篇 willett peter
15 篇 kirubarajan t.
12 篇 blair wd
9 篇 tharmarasa r.
8 篇 sinha a
8 篇 drummond oliver ...
7 篇 drummond oe
7 篇 willett p
6 篇 huang jim
6 篇 daum fred
5 篇 maskell s
5 篇 romeo kevin
5 篇 barshalom y
5 篇 danford scott
4 篇 pattipati kr
4 篇 paffenroth randy
4 篇 brown gc

语言

769 篇 英文
9 篇 其他
3 篇 中文
2 篇 土耳其文

检索条件"任意字段=Signal and Data Processing of Small Targets 1995 Conference"

共 783 条记录，以下是201-210 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Application of Rich Feature Descriptors to small Target Detection in Wide-Area Persistent ISR Systems

Application of Rich Feature Descriptors to Small Target Dete...

引用

conference on signal and data processing of small targets

作者： Miller, Christopher W. Edelberg, Jason. A. Wilson, Michael. L. Novak, Kyle. M. US Naval Res Lab Code 56644555 Overlook Ave SW Washington DC 20375 USA Tekla Res Dumfires VA USA

ISBN: (纸本)9781628410297

One of the desired capabilities for wide-area persistent ISR systems is to reliably locate and subsequently track the movement of targets within the field of view. Current wide-area persistent ISR systems are characterized by large pixel overall counts and very large fields of view. This leads to a large ground sample distance with few pixels-on-target. Locating targets under these constraints is extremely difficult due to the fact that the targets present very little detailed structure. In this paper we will present the application of rich image feature descriptors combined with advanced statistical target detection methodologies to the airborne ISR problem. We will demonstrate that these algorithms can reliably locate targets in the scene without relying on the target's motion to form a detection. This is useful in ISR application where it is desirable to be able to continuously track a target through stops and maneuvers.

关键词： Wide area surveillance automated target recognition support vector machines

来源：评论

学校读者我要写书评

暂无评论

CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOWN NON-targets FOR PLDA-SVM SCORING

CONSTRUCTION OF DISCRIMINATIVE KERNELS FROM KNOWN AND UNKNOW...

引用

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

作者： Rao, Wei Mak, Man-Wai Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479928934

Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers and claimants only. We have previously demonstrated that better performance can be achieved by incorporating the information of background speakers in the scoring process via speaker-dependent SVMs. This is achieved by defining a PLDA score space with dimension equal to the number of training i-vectors for each target speaker. The new protocol in NIST 2012 SRE permits systems to use the information of other target-speakers (called known non-targets) in each verification trial. In this paper, we exploit this new protocol to enhance the performance of PLDA-SVM scoring by using the score vectors of both known and unknown non-targets as the impostor class data to train the speaker-dependent SVMs. Because some target speakers have one enrollment utterance only, which results in severe imbalance in the speaker-and impostor-class data for SVM training. This paper shows that if the enrollment utterance is sufficiently long, a number of target-speaker i-vectors can be generated by an utterance partitioning and resampling technique, resulting in much better scoring SVMs. Results on NIST 2012 SRE demonstrate the advantages of pooling the known and unknown non-targets for training the SVMs and that the resampling techniques can help the SVM training algorithm to find better decision boundaries for those speakers with only a small number of enrollment utterances.

关键词： I-vectors probabilistic linear discriminant analysis empirical kernel maps likelihood ratio kernels NIST 2012 SRE

来源：评论

学校读者我要写书评

暂无评论

Approximate Calculation of Marginal Association Probabilities using a Hybrid data Association Model

Approximate Calculation of Marginal Association Probabilitie...

引用

conference on signal and data processing of small targets

作者： Baum, Marcus Willett, Peter Bar-Shalom, Yaakov Hanebeck, Uwe D. Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (纸本)9781628410297

The calculation of marginal association probabilities is the major computational bottleneck in the Joint Probabilistic data Association Filter (JPDAF). In this paper, we investigate approximations for the marginal associations that simplify the (computational complex) original association model in order to obtain efficient algorithms. In this context, we first discuss the Bakhtiar-Alavi algorithm and the Linear Multitarget Integrated Probabilistic data Association (LMIPDA) algorithm. Second, we propose a fast novel approximation that exploits systematic combinations of the JPDAF measurement model with the Probabilistic Multi-Hypothesis Tracker (PMHT) measurement model. The discussed methods are evaluated by means of a tracking scenario with a high number of closely-spaced targets.

关键词： Joint Probabilistic data Association Filter (JPDAF) marginal association probabilities Probabilistic Multi-Hypothesis Tracker (PMHT)

来源：评论

学校读者我要写书评

暂无评论

A Novel signal Reconstructing Method for Radar targets 2

A Novel Signal Reconstructing Method for Radar Targets

引用

2nd IEEE China Summit / International conference on signal and Information processing (IEEE ChinaSIP)

作者： Duan, Jia Zhang, Lei Wu, Yifeng Xing, Mengdao Wu, Min Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9781479954032

In this paper, a novel signal reconstructing method for radar targets is proposed based on the attributed scattering center model. By extracting the attributed parameters, the large amount of target data can be represented by small amounts of attributed parameters. In this way, the data amount has been compressed sharply, which releases the computer memory for storage. After extraction, a target discriminating method is presented by applying a CFAR threshold to the energy of extracted attributed scattering centers, by which, weak distributed scattering centers with relatively high energy in total can be discriminated from noise under low SNRs. Experimental results validate the effectiveness of the signal reconstructing capability of the proposal.

关键词： signal reconstruction attributed scattering center radar images

来源：评论

学校读者我要写书评

暂无评论

Advanced EMI Models for Survey data processing: targets Detection and Classification

Advanced EMI Models for Survey Data Processing: Targets Dete...

引用

conference on Detection and Sensing of Mines, Explosive Objects, and Obscured targets XIX

作者： Shubitidze, F. Sigman, J. B. Wang, Y. Miller, J. Keranen, J. Shamatava, I. Barrowes, B. E. O'Neill, K. Dartmouth Coll Thayer Sch Engn HB 8000 Hanover NH 03755 USA White River Technol Inc Etna NH 03750 USA CUSA ERDC Cold Regions Res & Engn Lab Hanover NH 03755 USA

ISBN: (纸本)9781628410099

One of the most challenging aspects of survey data processing is target selection. The fundamental input for the classification is dynamic data collected along survey lines. These data are different from the static data obtained in cued mode and used for target classification. Survey data are typically collected using just one transmitter loop (the Z-axis loop) and feature short data point collection times and short decay transience. The collection intervals for each data point are typically 0.1 s, and the signal repetition rates are typically 90 or 270 Hz (in other words, the transient decay times are 2.7 ms or 0.9 ms). Reliable classification requires multiple side/angle illumination;i.e., to conduct reliable classification it is necessary to combine and jointly invert multiple data points. However, picking data points that provide optimal information for classifying targets is a difficult task. The traditional method plots signal amplitudes on a 2D map and picks peaks of signal level without properly accounting for the underlying physics. In this paper, the joint diagonalization is applied to survey data sets to improve data pre-processing and target picking The JD technique is an EMI data analysis and target classification technique and is applicable for all next-generation multi-static array EMI sensors. The method extracts multi-static response data matrix eigenvalues. The eigenvalues are main characteristics of the data. Recent studies have demonstrated that the method has great potential to quickly estimate the number of potential targets and moreover classify these targets at the data pre-processing stage, in real time and without the need for a forward model. Another advantage of JD is that it provides the ability to separate signal from noise making it possible to de-noise data without distorting the signal due to the targets. In this paper the JD technique is used to process dynamic data collected at South West Proving Ground and Aberdeen Proving Ground

关键词： Dynamic systems OPTEMA UXO sensors small targets advanced models classification discrimination ONVMS modeling data collection deep targets

来源：评论

学校读者我要写书评

暂无评论

Dual-Sided Subspace Mappings for Main-Beam Multi-Target Super-Resolution in Clutter and Interference

Dual-Sided Subspace Mappings for Main-Beam Multi-Target Supe...

引用

IEEE Radar conference - From Sensing to Information

作者： Fernandez, Manuel F. Yu, Kai-Bor Independent Researcher Syracuse NY USA

ISBN: (纸本)9781479920358

The need for localizing multiple main-beam targets in the presence of interference, clutter and jammers arises in scenarios such as: air defense, where a radar detection may correspond to one or multiple targets (e.g., a missile launched from an airborne platform);ballistic missile defense, where the incoming missile complex involves a large number of objects;cruise missile defense for low angle target tracking in multipath;etc. This paper presents a super-resolution approach for the sub-beamwidth localization of multiple main-beam targets in the presence of clutter and interference, doing so with a single snapshot of sensor array data while placing nulls at specified locations. Such process first maps array data into compact subspaces containing the information of interest (e.g., a cluster of receive beams with imbedded nulls). Super-resolution is then achieved via small-matrix operations on the subspace data. The ensuing result is a mapping of array data into a compressed "information domain," yielding an effective, practical Sensor-to-Information process that is more accurate, robust and versatile than current super-resolution methods.

关键词： Array signal processing Arrays Clutter Noise signal resolution Vectors

来源：评论

学校读者我要写书评

暂无评论

Multiset Singular Value Decomposition for Joint Analysis of Multi Modal data: Application to Fingerprint Analysis

Multiset Singular Value Decomposition for Joint Analysis of ...

引用

conference on signal and data processing of small targets

作者： Emge, Darren K. Adali, Tuelay Edgewood Chem Biol Ctr Dept Army RDCB DRD L-E55605183 Blackhawk Rd Apg South MD 21010 USA Univ Maryland Dept CSEE Baltimore MD 21250 USA

ISBN: (纸本)9781628410297

As the availability and use of imaging methodologies continues to increase, there is a fundamental need to jointly analyze data that is collected from multiple modalities. This analysis is further complicated when, the size or resolution of the images differ, implying that the observation lengths of each of modality can be highly varying. To address this expanding landscape, we introduce the multiset singular value decomposition (MSVD), which can perform a joint analysis on any number of modalities regardless of their individual observation lengths. Through simulations, the inter modal relationships across the different modalities which are revealed by the MSVD are shown. We apply the MSVD to forensic fingerprint analysis, showing that MSVD joint analysis successfully identifies relevant similarities for further analysis, significantly reducing the processing time required. This reduction, takes this technique from a laboratory method to a useful forensic tool with applications across the law enforcement and security regimes.

关键词： Multimodal data Fusion Multiset Singular Value Decomposition SVD Generalized SVD

来源：评论

学校读者我要写书评

暂无评论

Particle Filter for Long Range Radar in RUV

Particle Filter for Long Range Radar in RUV

引用

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)9781628410297

In this paper we present an approach for tracking with a high-bandwidth active radar in long range scenarios with 3-D measurements in r-u-v coordinates. The 3-D low-process-noise scenarios considered are much more difficult than the ones we have previously investigated where measurements were in 2-D (i.e., polar coordinates). We show that in these 3-D scenarios the extended Kalman filter and its variants are not desirable as they suffer from either major consistency problems or degraded range accuracy, and most flavors of particle filter suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and 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 3D metrology Radar Filtering (signal processing) Sensors Statistical analysis 3D acquisition Detection and tracking algorithms Error analysis

来源：评论

学校读者我要写书评

暂无评论

A Combined STAP/DPCA Algorithm for Enhanced Endoclutter Target Detection

A Combined STAP/DPCA Algorithm for Enhanced Endoclutter Targ...

引用

conference on Radar Sensor Technology XVIII

作者： Medl, Thomas Gen Atom Aeronaut Syst Inc GA ASI San Diego CA 92127 USA

ISBN: (纸本)9781628410143

Displaced Phase Center Antenna (DPCA) and Space-Time Adaptive processing (STA-P) are two general methods to cancel clutter in order to detect small, slowly moving targets that may be obscured by clutter. To detect these targets, the radar detection threshold needs to be as low as possible to ensure some minimum probability of detection (Pd). Unfortunately, lowering the radar threshold naturally results in a higher false alarm rate. Although there are standard methods such as M of N to reduce the false alarms, new techniques can potentially drive the false alarm rate down even further. Many "theoretical" papers have shown that STAP can be designed to outperform DPCA because of its potential additional "degrees-of-freedom". However, in "practice," this isn't always the case. For example, it is well known that STAP can have training issues in heterogeneous clutter. Typically, a radar signal processor will implement one method or the other to detect these small endoclutter targets. The technique being explored here is a two-fold approach in which the existing STAP code first processes the data in order to find a list of candidate targets. Next, a DPCA technique is also used to find a separate list of candidate detections from the same data. Although the algorithms are working on the same data, the processing is "independent" between them so the target lists are different. After both techniques have finished processing, the modified radar signal processing code "intelligently" combines the two detection lists into a single detection list. It will be shown that the combined list of detections from the two methods results in better detection performance than either method used separately.

关键词： endoclutter detection clutter suppression STAP DPCA SAR MTI data mining Weka

来源：评论

学校读者我要写书评

暂无评论

IMM Filtering on Parametric data for Multi-Sensor Fusion

IMM Filtering on Parametric Data for Multi-Sensor Fusion

引用

conference on signal and data processing of small targets

作者： Shafer, Scott Owen, Mark SPAWAR Syst Ctr Pacific San Diego CA USA

ISBN: (纸本)9781628410297

In tracking, many types of sensor data can be obtained and utilized to distinguish a particular target. Commonly, kinematic information is used for tracking, but this can be combined with identification attributes and parametric information passively collected from the targets emitters. Along with the standard tracking process (predict, associate, score, update, and initiate) that operates in all kinematic trackers, parametric data can also be utilized to perform these steps and provide a means for feature fusion. Feature fusion, utilizing parametrics from multiple sources, yields a rich data set providing many degrees of freedom to separate and correlate data into appropriate tracks. Parametric radar data can take on many dynamics to include: stable, agile, jitter, and others. By utilizing a running sample mean and sample variance a good estimate of radar parametrics is achieved. However, when dynamics are involved, a severe lag can occur and a non-optimal estimate is achieved. This estimate can yield incorrect associations in feature space and cause track fragmentation or miscorrelation. In this paper we investigate the accuracy of the interacting multiple model (IMM) filter at estimating the first and second moments of radar parametrics. The algorithm is assessed by Monte Carlo simulation and compared against a running sample mean/variance technique. We find that the IMM approach yields a better result due to its ability to quickly adapt to dynamical systems with the proper model and tuning.

关键词： Interacting Multiple Model IMM sensor fusion radar pulse repetition interval pulse width RF SR

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共79页 << < 17 18 19 20 21 22 23 24 25 26 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：