发布求助
文献互助 智能选刊 最新文献

2006 IEEE Nonlinear Statistical Signal Processing Workshop最新文献

筛选
英文 中文
Performance Analysis of a Suprathreshold Stochastic Resonance Based Nonlinear Detector 基于超阈值随机共振的非线性检测器性能分析
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378809
Vijayendra Mohan Roy, G. V. Anand
{"title":"Performance Analysis of a Suprathreshold Stochastic Resonance Based Nonlinear Detector","authors":"Vijayendra Mohan Roy, G. V. Anand","doi":"10.1109/NSSPW.2006.4378809","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378809","url":null,"abstract":"In this paper we introduce a nonlinear detector based on the phenomenon of suprathreshold stochastic resonance (SSR). We first present a model (an array of 1-bit quantizers) that demonstrates the SSR phenomenon. We then use this as a pre-processor to the conventional matched filter. We employ the Neyman-Pearson(NP) detection strategy and compare the performances of the matched filter, the SSR-based detector and the optimal detector. Although the proposed detector is non-optimal, for non-Gaussian noises with heavy tails (leptokurtic) it shows better performance than the matched filter. In situations where the noise is known to be leptokurtic without the availability of the exact knowledge of its distribution, the proposed detector turns out to be a better choice than the matched filter.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116588232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Sigma-Point Filters: An Overview with Applications to Integrated Navigation and Vision Assisted Control 西格玛点滤波器:综合导航和视觉辅助控制的应用概述
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378854
E. Wan
{"title":"Sigma-Point Filters: An Overview with Applications to Integrated Navigation and Vision Assisted Control","authors":"E. Wan","doi":"10.1109/NSSPW.2006.4378854","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378854","url":null,"abstract":"In this presentation, we first provide an overview of Sigma-Point filtering methods, which include the Unscented Kalman Filter (UKF), Central Difference Kalman Filter (CDKF), and several variants with hybrid extensions to sequential Monte Carlo filtering (e.g., particle filtering). In the second half, we focus on recent applications to integrated navigation systems (INS), which provide state-estimation by combining GPS and inertial measurements. In addition, we present new work on using video data to extract the equivalent state-information (i.e., replacing the INS) for use in closed-loop control of an Unmanned Aerial Vehicle (UAV).","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131404034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 37
Joint Driver Intention Classification and Tracking of Vehicles 联合驾驶员意图分类与车辆跟踪
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378828
J. Gunnarsson, L. Svensson, E. Bengtsson, L. Danielsson
{"title":"Joint Driver Intention Classification and Tracking of Vehicles","authors":"J. Gunnarsson, L. Svensson, E. Bengtsson, L. Danielsson","doi":"10.1109/NSSPW.2006.4378828","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378828","url":null,"abstract":"In this paper we present and validate a new modelling frame-work for joint driver intention classification and tracking of vehicles, a framework derived for automotive active safety systems. Such systems require reliable predictions of the traffic situation to act in time when a dangerous situation occur. Our proposal has two main benefits. First, it incorporates the intention of the driver into the vehicle motion model and thereby improves the prediction capability. The result is a multiple motion model where each model corresponds to a specific driver intent. Second, the connection between different driver plans and corresponding motion model enables a formal classification of the most likely driver intention. To validate our concept, we apply the motion model on real data using a particle filter implementation. Initial studies indicate promising performance.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
Online Target Tracking and Sensor Registration using Sequential Monte Carlo Methods 基于时序蒙特卡罗方法的在线目标跟踪和传感器配准
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378819
Jack Li, W. Ng, S. Godsill
{"title":"Online Target Tracking and Sensor Registration using Sequential Monte Carlo Methods","authors":"Jack Li, W. Ng, S. Godsill","doi":"10.1109/NSSPW.2006.4378819","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378819","url":null,"abstract":"In tracking applications, the target state (e.g., position, velocity) can be estimated by processing the measurements collected from all deployed sensors at a central node. The estimation performance significantly relies on the accuracy of the sensor positions/rotations when data fusion is conducted. Since in practice precise knowledge of this sensor information is seldom available, in this paper we propose a Sequential Monte Carlo (SMC) approach to jointly estimate the target state and resolve the sensor position uncertainty.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132694189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Distributed Self Localisation of Sensor Networks using Particle Methods 基于粒子方法的传感器网络分布式自定位
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378845
N. Kantas, Sumeetpal S. Singh, A. Doucet
{"title":"Distributed Self Localisation of Sensor Networks using Particle Methods","authors":"N. Kantas, Sumeetpal S. Singh, A. Doucet","doi":"10.1109/NSSPW.2006.4378845","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378845","url":null,"abstract":"We describe how a completely decentralized version of Recursive Maximum Likelihood (RML) can be implemented in dynamic graphical models through the propagation of suitable messages that are exchanged between neighbouring nodes of the graph. The resulting algorithm can be interpreted as a generalization of the celebrated belief propagation algorithm to compute likelihood gradients. This algorithm is applied to solve the sensor localisation problem for distributed trackers forming a sensor networks. An implementation is given for dynamic nonlinear model without loops using Sequential Monte Carlo (SMC) or particle","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131820238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Sequential Learning Methods on RBF with Novel Approach of Minimal Weight Update 基于最小权值更新的RBF序列学习方法
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378851
V. Asirvadam, S. McLoone
{"title":"Sequential Learning Methods on RBF with Novel Approach of Minimal Weight Update","authors":"V. Asirvadam, S. McLoone","doi":"10.1109/NSSPW.2006.4378851","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378851","url":null,"abstract":"This paper investigates sequential learning method with new form of weight update applied on a decomposed form of training algorithms using Radial Basis Function (RBF) network. Adding each basis function to the hidden layer during the course of training facilitate the weight update to be decomposed on neuron by neuron basis. A new form weight update is introduced where the weight update is based on minimal displacement of the current input elements to the elements of the nearest centre of the Gaussian neuron.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115143021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Mixture Tracking of Multiple Fingers Image in Omnidirection Camera for Human Friendly Interface 面向人机友好界面的全向相机多指图像混合跟踪
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378846
N. Ikoma, Masahide Sakata, M. Doi
{"title":"Mixture Tracking of Multiple Fingers Image in Omnidirection Camera for Human Friendly Interface","authors":"N. Ikoma, Masahide Sakata, M. Doi","doi":"10.1109/NSSPW.2006.4378846","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378846","url":null,"abstract":"Aiming at human friendly user interface using omnidirection camera by holding it with fingers, we propose a method to track fingers in the image using particle filters. Mixture tracking is employed as a particle filtering method since it can track multiple modes of posterior distribution effectively leading to the ability to track multiple fingers in the image. We also propose a sequential labeling technique for clustering the particles in the framework of mixture tracking. This effectively classifies the particles into each finger in the image. Tracking performance has been demonstrated in real images of omnidirection camera.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125968022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Model-Based Processing for a Short Towed Array 基于模型的短拖曳阵列处理
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378816
E. Sullivan, J. D. Holmes, W. M. Carey
{"title":"Model-Based Processing for a Short Towed Array","authors":"E. Sullivan, J. D. Holmes, W. M. Carey","doi":"10.1109/NSSPW.2006.4378816","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378816","url":null,"abstract":"Model-based signal processing techniques for a short towed array are experimentally demonstrated for both broad band and narrow-band signals. It is shown that accurate bearing and horizontal wavenumber estimations, for frequencies ranging from 220 Hz to 1228 Hz, can be made for acoustic apertures as short as 0.5 wavelength. The data were the result of an experiment carried out in Nantucket sound, where an autonomous underwater vehicle towed a hydrophone array for the purpose of forming a long synthetic aperture. The AUV towed the array outward from a fixed narrow-band source on a straight radial track out to 4 km. Processing of the signal over the entire 4 km synthetic aperture, based on a normal-mode propagation mode, allowed for characterization of the channel. During the experiment a ferry passed through the area. This \"target of opportunity\" provided a basis for testing model-based passive synthetic aperture bearing estimation routines.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130826882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
SMC Samplers for Bayesian Optimal Nonlinear Design 用于贝叶斯最优非线性设计的SMC采样器
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378829
Hendrik Kuck, N. de Freitas, A. Doucet
{"title":"SMC Samplers for Bayesian Optimal Nonlinear Design","authors":"Hendrik Kuck, N. de Freitas, A. Doucet","doi":"10.1109/NSSPW.2006.4378829","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378829","url":null,"abstract":"Experimental design is a fundamental problem in science. It arises in the planning of medical trials, sensor network deployment and control as well as in costly data gathering in physics, chemistry and biology. Bayesian decision theory provides a principled way of treating this problem, but leads to an intractable joint optimization and integration problem. Here, we propose a viable solution to this hard computational problem using sequential Monte Carlo samplers.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 37
A Risk Sensitive Estimator for Nonlinear Problems using the Adaptive Grid Method 基于自适应网格法的非线性问题风险敏感估计
2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI: 10.1109/NSSPW.2006.4378806
S. Bhaumik, M. Srinivasan, S. Sadhu, T. Ghoshal
{"title":"A Risk Sensitive Estimator for Nonlinear Problems using the Adaptive Grid Method","authors":"S. Bhaumik, M. Srinivasan, S. Sadhu, T. Ghoshal","doi":"10.1109/NSSPW.2006.4378806","DOIUrl":"https://doi.org/10.1109/NSSPW.2006.4378806","url":null,"abstract":"An Adaptive Grid Method based on the well-known point-mass approximation has been developed for computation of risk-sensitive state estimates in non-linear non-Gaussian problems. Risk-sensitive estimators, believed to have increased robustness compared to their risk neutral counterparts, admit closed form expressions only for a very limited class of models including linear Gaussian models. The Extended Risk-Sensitive Filter (ERSF) which uses an EKF-like approach fails to take care of non-Gaussian problems or severe non-linearities. Recently, a particle-filter based approach has been proposed for extending the range of applications of risk-sensitive techniques. The present authors have developed the adaptive grid risk-sensitive filter (AGRSF), which was partially motivated by the need to validate the particle filter based risk-sensitive filter and uses a set of heuristics for the adaptive choice of grid points to improve the numerical efficiency. The AGRSF has been cross-validated against closed-form solutions for the linear Gaussian case and against the risk-sensitive particle filter (RSPF) for fairly severe non-linear problems which create a multi-modal posterior distribution. Root mean square error and computational cost of the AGRSF and the RSPF have been compared.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信