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检索条件"主题词=Filtering algorithms"

共 10310 条记录，以下是71-80 订阅

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An evaluation of nonlinear filtering algorithms for integrating GNSS and inertial measurements

An evaluation of nonlinear filtering algorithms for integrat...

引用

IEEE Symposium on Position Location and Navigation (PLANS)

作者： Robin Schubert Norman Mattern Gerd Wanielik Professorship of Communications Engineering Chemnitz University of Technology Chemnitz Germany

The quality of dead reckoning positioning algorithms which are integrating GNSS and INS measurements is a crucial factor for advanced vehicular safety systems. Apart from sensor performances, the most important factor determining this quality is the filter algorithm. Thus, this paper aims to compare and evaluate different nonlinear filtering approaches, focusing on their performance in GNSS/INS integration. The presented approach is based on an advanced motion model which assumes a constant turn rate and longitudinal acceleration of the vehicle. Using this approach, the unscented Kalman Filter is compared to the standard extended Kalman filter. For the evaluation, experimental data obtained by a DGPS receiver with RTK capabilities are used. With this approach, the filter assessment is performed in different scenarios, including urban areas.

关键词： filtering algorithms Satellite navigation systems Filters Dead reckoning Position measurement Safety Performance evaluation Acceleration Vehicles Global Positioning System

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Adaptive filtering algorithms for promoting sparsity

Adaptive filtering algorithms for promoting sparsity

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： B.D. Rao Bongyong Song Department of Electrical and Computer Engineering La Jolla CA USA

We provide a mathematical framework for developing adaptive filtering algorithms for exploiting/enforcing sparsity. The approach is based on minimizing a regularized mean squared error criterion with sparsity being promoted by the regularizing term which consists of a diversity measure. A steepest descent algorithm (SDA) is developed to minimize the regularized cost function. Then we extend the algorithm to the adaptive environment and develop a class of algorithms, which we term the pLMS algorithm class and which incudes important variants - pLLMS (leaky pLMS) and pNLMS (normalized pLMS). The framework is quite general and encompasses a broad range of adaptive algorithms with the pNLMS having similarity with the proportionate normalized least-mean-squares (PNLMS) algorithm. Computer simulations have been conducted using the echo canceller application as an example of a sparse environment. The simulations clearly show the ability of the developed algorithms to exploit the inherent sparsity structure, thereby outperforming conventional algorithms like the NLMS algorithm in this application.

关键词： Adaptive filters filtering algorithms Matching pursuit algorithms Least squares approximation Cost function Echo cancellers Adaptive algorithm Algorithm design and analysis Pursuit algorithms Vectors

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Tag and Resource-Aware Collaborative filtering algorithms for Resource Recommendation

Tag and Resource-Aware Collaborative Filtering Algorithms fo...

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International Conference on Information Technology: New Generations (ITNG)

作者： Gayatri Gadepalli Elke Rundensteiner David Brown Kajal Claypool Department of Computer Science Worcester Polytechnic Institute Worcester MA USA USA

ISBN: (纸本)9781424462704

Recommender systems suggest resources to users based on collaborative filtering techniques, typically by exploiting correlations between individual user ratings of the resources they are interested in. Tags are a new form of metadata increasingly used in social bookmarking sites by users to annotate bookmarked resources. Our goal is to harness the implicit knowledge contained in these tags to improve the quality of recommendations to users. We use both tag and resource-interest knowledge in our user-based collaborative filtering algorithms to profile users and compute similarity between them. Sparsity is a challenge which occurs in a Social Recommendation System when the number of tags and resources to profile a user are inadequate to provide good quality recommendations. To address this problem, we designed a Tripartite Nearest Neighbor Algorithm (TRNNA) which combines three views of the data: the tags (TNNA), the resources (RNNA) and the collection of tags for a resource (Resource Vector of Tags or RVTA). TRNNA computes distance between users based on Cosine Similarity, which in turn is used to provide a high quality recommendation of resources. Our empirical evaluation, based on a user study in which research papers were recommended to participants and relevance of recommendation was evaluated, indicates that TRNNA and RNNA provide better recommendation than TNNA and RVTA.

关键词： filtering algorithms Nearest neighbor searches Tagging Collaborative work International collaboration Algorithm design and analysis Frequency Information technology Computer science Information filtering

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Frequency-Domain Adaptive filtering algorithms for Nonstationary Environments

Frequency-Domain Adaptive Filtering Algorithms for Nonstatio...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Yishu Peng Sheng Zhang Zhengchun Zhou School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan P. R. China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In nonstationary environments, existing frequency-domain adaptive filtering algorithms would exhibit poor tracking performance. To solve this issue, this paper focuses on developing new frequency-domain adaptive filtering algorithms based on single data. Using the circular matrix of the regression vector, we first establish a model and cost function suitable for a nonstationary system. Next, with resort to the stochastic gradient descent and power normalized methods, the frequency-domain least mean-square algorithm based on single data (SFDLMS) and its normalized version (named SFDNLMS) are derived. Even in the presence of correlated input signals, the proposed SFDNLMS algorithm can provide fast tracking/convergence performance. The transient and steady-state behavior is also studied. Finally, experiment results illustrate the advantages of the proposed algorithms and the reliability of the theoretical analysis.

关键词： Frequency-domain analysis Image processing Stochastic processes filtering algorithms Reliability theory Cost function Vectors

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Adaptive filtering algorithms for stereophonic acoustic echo cancellation

Adaptive filtering algorithms for stereophonic acoustic echo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： J. Benesty F. Amand A. Gilloire Y. Grenier Telecom Paris Paris France LAA/TSS/CMC CNET Lannion France TELECOM PARIS Department Paris France

It is likely that stereophonic (and more generally, multichannel) sound pick-up, transmission and diffusion will be implemented in future teleconference systems to provide the users with enhanced quality. Therefore, adequate solutions must be found to solve the problem of stereophonic acoustic echo which will occur in such systems. We explain in this paper the difference between the mono and two-channel systems and the behavior of the two-channel classical adaptive algorithms in comparison with the same algorithms in the mono-channel case. Also, we outline a new NLMS-like algorithm derived from the two-channel RLS algorithm as a first member of a family of improved two-channel adaptive filters.

关键词： Adaptive filters filtering algorithms Echo cancellers Microphones Teleconferencing Adaptive algorithm Telecommunications Electronic mail MONOS devices Resonance light scattering

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The investigation of new adaptive filtering algorithms for telecommunications echo cancellation implemented in TMS32010 fixed point assembly code

The investigation of new adaptive filtering algorithms for t...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： M.N. Kloos W.K. Jenkins Motorola Inc. Schaumburg IL USA Department of Electrical and Computer Engineering and The Coordinated Science Laboratory University of Illinois Urbana IL USA

Current state-of-the-art echo cancellers for telecommunications typically employ the least mean square (LMS) adaptive algorithm developed in the late 1950's and early 1960's. The canceller is usually implemented as a nonrecursive, or finite impulse response (FIR), filter structure. More recently, interest has grown in recursive, or infinite impulse response (IIR), adaptive filter structures for the echo cancellation application. Floating-point computer simulations have shown that adaptive IIR filters require less computation to match certain transfer relations than do adaptive FIR filters. However, it is a practical question as to how well these algorithms perform when implemented in a fixed-point environment, which is typically the case in practical situations. The authors examine the performance of several IIR adaptive filtering algorithms as implemented on a Texas Instruments. TMS32010 digital signal processor for echo cancellation in telecommunication environments.< >

关键词： Adaptive filters filtering algorithms Signal processing algorithms Echo cancellers Finite impulse response filter IIR filters Least squares approximation Adaptive algorithm Application software Computer simulation

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Particle filtering algorithms for Tracking Multiple Sound Sources using Microphone Arrays

Particle Filtering Algorithms for Tracking Multiple Sound So...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mitsuru Kawamoto Futoshi Asano Hideki Asoh Kiyoshi Yamamoto Japan Science and Technology Corporation-CREST Japan CREST JST National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan

A particle filtering algorithm using the parameters in the EM (expectation-maximization) algorithm is proposed for tracking multiple sound sources. Differently from the conventional EM based algorithms, the proposed algorithm can track multiple sound sources without knowing their starting points. Moreover, an idea of the group tracking is applied to the particle filtering algorithm so that better tracking performances can be obtained. Experimental results show the validity of the proposed algorithm.

关键词： Microphone arrays filtering algorithms Particle tracking Radar tracking Particle filters Sonar navigation Frequency Acoustic noise Covariance matrix Speech

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Particle filtering algorithms for single-channel blind separation of convolutionally coded signals

Particle filtering algorithms for single-channel blind separ...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Canhui Liao Shilong Tu Shidong Zhou State Key Lab. on Microwave & Digital Commun. Tsinghua Univ. Beijing China Southwest Electron. & Telecommun. Technol. Res. Inst. Chengdu China

In this paper, we propose a particle filtering based algorithm for single-channel blind separation (SCBS) of two convolutionally coded signals. We utilize the convolution code to enhance the performance of separation. By modifying the generalized state-space representation of the SCBS model, we obtain an approach for joint separation and decoding of two source signals. Two conventional algorithms, which execute decoding after signal separation, are then discussed and compared with the proposed algorithm. Computer simulations show that the proposed algorithm makes a significant improvement in symbol error rate (SER) performance over the two conventional algorithms.

关键词： filtering algorithms Convolutional codes Convolution Signal processing algorithms Decoding Source separation Computer simulation Signal processing Digital communication Particle filters

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Online adaptive Kalman filtering algorithms for sensor signals with coloured noise

Online adaptive Kalman filtering algorithms for sensor signa...

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IEEE Conference on Decision and Control

作者： P. Wojcik Advanced Technologies Department Alberta Research Council Calgary AB Canada

Two algorithms are presented for online estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and coloured measurement noise are assumed. The proposed adaptive Kalman filtering algorithms have been tested for errors of the pure signal estimation. Although the tests have been performed for stationary signals, the algorithms can also be used successfully for time-varying sensor signals when the signal-to-noise ratio varies in comparison to the length of the adaptation step.< >

关键词： Kalman filters filtering algorithms Colored noise Signal to noise ratio Noise measurement Nonlinear filters White noise Testing Signal design Signal processing

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Two-dimensional block adaptive filtering algorithms

Two-dimensional block adaptive filtering algorithms

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： W.B. Mikhael S.M. Ghosh Department of Electrical Engineering University of Central Florida Orlando FL USA

A technique for 2-D system identification which processes 2-D signals using 2-D blocks is proposed. Two algorithms which perform 2-D FIR (finite impulse response) adaptive filtering using 2-D error blocks or windows are presented. The first algorithm uses a convergence factor that is constant for each 2-D coefficient at each window iteration. This algorithm is termed the two-dimensional block least mean square algorithm (TDBLMS). A novel 2-D adaptive fast LMS algorithm which processes 2-D signals is presented. In this algorithm, a convergence factor is obtained that is the same for all 2-D coefficients at a particular window iteration, but is updated at each window iteration. This algorithm is called the two-dimensional optimum block algorithm (TDOBA). The convergence properties of the TDBLMS and TDOBA are investigated and compared using computer simulations for both disjoint and overlapping windows. It is shown that the TDOBA clearly outperforms the TDBLMS algorithm with respect to convergence speed and accuracy of adaptation.< >

关键词： Adaptive filters filtering algorithms Convergence System identification Adaptive algorithm Transfer functions Two dimensional displays Finite impulse response filter Least mean square algorithms Signal processing

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