Conferences related to Kalman filters

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2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE 23rd International Conference on Information Fusion (FUSION)

The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.


2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

ISIE focuses on advancements in knowledge, new methods, and technologies relevant to industrial electronics, along with their applications and future developments.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Kalman filters

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


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Most published Xplore authors for Kalman filters

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No authors for "Kalman filters"


Xplore Articles related to Kalman filters

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An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications

Handbook of Position Location: Theory, Practice, and Advances, None

This chapter investigates the implementation of linear and nonlinear Kalman filters for localization, target tracking, and navigation. It formulates the positioning problem in the estimation context and presents a deterministic derivation for Kalman filters. The chapter introduces several types of Kalman filters used for localization, which include extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and ...


Neural network aided adaptive federal kalman filtering for intelligent integrated navigation application - [Not available for publication]

The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576), 2004

None


Correction to "Gradient-Based Manipulation of Nonparametric Entropy Estimates'' [Jul 04 828-837]

IEEE Transactions on Neural Networks, 2007

In the above titled paper (ibid., vol. 15, no. 4, pp. 828-837, Jul 04), the M-step (22) was incorrect. The corrected M-step is presented here.


Speech signal recovery in white noise using an adaptive Kalman filter

2000 10th European Signal Processing Conference, 2000

This paper deals with the problem of Adaptive Noise Cancellation (ANC) when only a corrupted speech signal with an additive Gaussian white noise is available for processing. All the approaches based on the Kalman filter proposed in the past, in this context, operate in two steps: they first estimate the noise variances and the parameters of the signal model and ...


Adaptive tracking of narrowband HF channel response

Radio Science, 2003

Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to ...


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Educational Resources on Kalman filters

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IEEE-USA E-Books

  • An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications

    This chapter investigates the implementation of linear and nonlinear Kalman filters for localization, target tracking, and navigation. It formulates the positioning problem in the estimation context and presents a deterministic derivation for Kalman filters. The chapter introduces several types of Kalman filters used for localization, which include extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and constrained Kalman filter (CKF). Implementation examples for localization, target tracking, and navigation of these Kalman filters are offered, and their associated MATLAB codes are presented. In general, an estimation algorithm predicts the quantities of interest via direct or indirect observations. The chapter mainly presents the estimation algorithm for both target tracking and navigation applications. Such applications include vehicular navigation, aircraft tracking and navigation, satellite orbit and attitude determination, etc. Finally, the chapter offers relevant and more advanced topics to the reader.

  • Neural network aided adaptive federal kalman filtering for intelligent integrated navigation application - [Not available for publication]

    None

  • Correction to "Gradient-Based Manipulation of Nonparametric Entropy Estimates'' [Jul 04 828-837]

    In the above titled paper (ibid., vol. 15, no. 4, pp. 828-837, Jul 04), the M-step (22) was incorrect. The corrected M-step is presented here.

  • Speech signal recovery in white noise using an adaptive Kalman filter

    This paper deals with the problem of Adaptive Noise Cancellation (ANC) when only a corrupted speech signal with an additive Gaussian white noise is available for processing. All the approaches based on the Kalman filter proposed in the past, in this context, operate in two steps: they first estimate the noise variances and the parameters of the signal model and secondly estimate the speech signal. We propose a new method to estimate the noise variances. This estimation is made by reformulating and adapting the Mehra approach.

  • Adaptive tracking of narrowband HF channel response

    Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.

  • MIMO channel estimation and equalization using three-layer neural networks with feedback

    This paper describes a channel estimation and equalization algorithm using three-layer artificial neural networks (ANNs) with feedback for multiple input multiple output wireless communication systems. An ANN structure with feedback was designed to use different learning algorithms in the different ANN layers. This actually forms a Turbo iteration process between the different algorithms which effectively improves the estimation performance of the channel equalizer. Simulation results show that this channel equalization algorithm has better computational efficiency and faster convergence than higher order statistics based algorithms.

  • Emendations to "Particle Velocity Estimation using Kalman Filtering"

    None

  • Elimination of cliks and background noise from archive gramophone recordings using the "two track mono" approach

    Old gramophone recordings are corrupted with a wideband noise (granulation noise) and impulsive disturbances (cliks, pops, record scratches) — both caused by aging and/or mishandling of the vinyl material. The paper presents an improved method of gramophone noise reduction which makes use of two signals obtained when a mono record is played back using the stereo equipment.

  • Localized adaptive inflation in ensemble data assimilation for a radiation belt model

    In this work a one-dimensional radial diffusion model for phase space density, together with observational satellite data, is used in an ensemble data assimilation with the purpose of accurately estimating Earth's radiation belt particle distribution. A particular concern in data assimilation for radiation belt models are model deficiencies, which can adversely impact the solution of the assimilation. To adequately address these deficiencies, a localized adaptive covariance inflation technique is implemented in the data assimilation to account for model uncertainty. Numerical results from identical-twin experiments, where data is generated from the same model, as well as the assimilation of real observational data, are presented. The results show improvement in the predictive skill of the model solution due to the proper inclusion of model errors in the data assimilation.

  • Neural recurrent estimator to gray scale image restoration based on 2D Kalman filtering

    None



Standards related to Kalman filters

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No standards are currently tagged "Kalman filters"