
	Where results make sense

Topic: Fast Kalman filter

Related Topics

Kalman filter

Fisher information matrix

Autopilot

In the News (Wed 30 Dec 09)

Northern Trust

Marvin Harrison

Peter Chernin

Gary Locke

Match Play

Penelope Cruz

Mickey Rourke

AR Rahman

Danny Boyle

Hugh Jackman

	Kalman filter - Wikipedia, the free encyclopedia
		The Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete and noisy measurements.
		The Kalman filter exploits the dynamics of the target, which govern its time evolution, to remove the effects of the noise and get a good estimate of the location of the target at the present time (filtering), at a future time (prediction), or at a time in the past (interpolation or smoothing).
		It was during a visit of Kalman to the NASA Ames Research Center that he saw the applicability of his ideas to the problem of trajectory estimation for the Apollo program, leading to its incorporation in the Apollo navigation computer.
	www.wikipedia.org /wiki/Kalman_filter (2458 words)

	Encyclopedia topic: Kalman filter (Site not responding. Last check: 2007-09-19)
		The Kalman filter is an efficient recursive filter (additional info and facts about recursive filter) which estimates the state of a dynamic system from a series of incomplete and noisy (additional info and facts about noisy) measurements.
		Kalman filtering is an important topic in control theory (additional info and facts about control theory) and control system (A system for controlling the operation of another system) s engineering.
		Kalman filters are based on linear algebra (The part of algebra that deals with the theory of linear equations and linear transformation) and the hidden Markov model (additional info and facts about hidden Markov model).
	www.absoluteastronomy.com /encyclopedia/k/ka/kalman_filter.htm (2559 words)

	Fast Kalman filter (Site not responding. Last check: 2007-09-19)
		The fast Kalman filter (FKF) of Antti Lange (1941-) is an extension of the Helmert-Wolf blocking (HWB) method from Geodesy to real-time applications of Kalman filtering (KF).
		Kalman's observability condition), the number of the measurements should generally be much larger than the number of all the state parameters to be estimated at a time.
		Ultra-reliable operational Kalman filtering requires continuous fusion of real-time data where its optimality crucially depends on the full use of all error variances and covariances of the measurements and estimated state and calibration parameters.
	www.abitabouteverything.com /files/f/fa/fast_kalman_filter.html (1213 words)

	Kalman filter based equalization for digital multicarrier communications systems - US Patent 6295326 (Site not responding. Last check: 2007-09-19)
		an equalizer configured to determine, using a Kalman filter, a minimum mean square estimate of the data received from the communications channel and to use the minimum mean square estimate to remove the distortion introduced by transmitting the analog data on the communications channel.
		According to an embodiment of the invention, a Kalman filtering technique with an increased length state, i.e., a smoother, is used to remove substantially all ISI from the received digital encoded data in the time domain by obtaining an MMSE of the original data.
		Kalman filter based equalization is implemented as follows according to one embodiment of the invention.
	www.patentstorm.us /patents/6295326.html (6012 words)

	Encyclopedia: Fast Kalman filter
		The fast Kalman filter (FKF) of Antti Lange (1941-) is an extension of the Helmert-Wolf blocking
		The FKF applies only to systems with sparse matrices (Lange, 2001), since HWB is an inversion method to solve sparse linear equations (Wolf, 1978).
		Antti Lange (born December 11, 1941 in Helsinki, Finland) is a Finnish mathematician and statistician of the Finnish Meteorological Institute.
	www.nationmaster.com /encyclopedia/Fast-Kalman-filter (299 words)

	Kalman filter - Encyclopedia, History, Geography and Biography (Site not responding. Last check: 2007-09-19)
		For example, in a radar application,where one is interested in tracking a target, information about the location,speed, and acceleration of the target is measured with a great deal ofcorruption by noise at any time instant.
		The Kalman filter exploitsthe dynamics of the target, which govern its time evolution, to removethe effects of the noise and get a good estimate of the location ofthe target at the present time (filtering), at a future time (prediction),or at a time in the past (interpolation or smoothing).
		This is justified because, as an optimal estimator, the Kalman filter makes best use of the measurements, therefore the PDF for $\mathbf{x}_k$ given the measurements $\mathbf{Z}_k$ is the Kalman filter estimate.
	www.arikah.net /encyclopedia/Kalman_gain (3053 words)

	Improved Kalman Filter Initialisation using Neurofuzzy Estimation
		It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs[7, 9, 10] but unfortunately their performance is extremely sensitive to state initialisation accuracy (Figure 1).
		To demonstrate that a neurofuzzy initialised extended Kalman filter (NF-EKF) is often superior to an EKF initialised using raw observations, the recession-rate of a tracked feature was estimated.
		A recursive neurofuzzy estimator that exploits the positive properties of both the finite history neurofuzzy estimator and the Kalman filter is under development[4, 11].
	www.ecs.soton.ac.uk /publications/rj/1995-1996/isis/jmr94r/iee95.htm (2234 words)

	Abstract: M2000-075 (Site not responding. Last check: 2007-09-19)
		The requirements to filtering all unwanted components of radar signal clutter in data processing process, directing attention away from the real target, are set up.
		As the result of the analyse of different possibilities of filtering we have found that the way out of the situation is a new approach to Kalman filtering called Fast Kalman Filtering (FKF), developed by Antti Lange in 1989 for the statistical calibration of measuring systems.
		With the method, known as the FKF, all the calibration process with additional error estimations using the minimum norm quadratic unbiased estimation theory, takes advantage of the large matrix handling technology.
	www.me.gatech.edu /mechatronics2000/Abstracts/075.htm (235 words)

	ECS EPrints Service - Improved kalman filter initialisation using neurofuzzy estimation (Site not responding. Last check: 2007-09-19)
		It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs but unfortunately their performance is extremely sensitive to state initialisation accuracy.
		When a filter diverges, it must be re-initialised but because the observations are extremely poor, re-initialised states will have poor estimates.
		Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re-started after divergence.
	www.bib.ecs.soton.ac.uk /records/234 (229 words)

	Citations: Collective localization: a distributed kalman filter approach to localization of groups of mobile robots - ... (Site not responding. Last check: 2007-09-19)
		A Kalman filter based implementation of a cooperative navigation schema is described in [10] In this work the effect of the orientation uncertainty in both the state propagation and the relative position measurements is ignored resulting in a simplified distributed algorithm.
		In, 11] a Kalman filter pose estimator is presented for a group of simultaneously moving robots.
		The Kalman filter is decomposed into a number of smaller communicating filters, one for every robot, processing sensor data collected by its host robot.
	citeseer.lcs.mit.edu /context/1768335/0 (1830 words)

	Citations: Decoupled extended Kalman filter training of feedforward layered networks - Puskorius, Feldkamp ... (Site not responding. Last check: 2007-09-19)
		More recently, Wan Nelson proposed a dual Kalman filtering method for feedforward neural network training [121] Using the same idea, Williams [123] and Suykens [113] independently formulated the training of a recurrent neural network as a state estimation problem and applied the extended....
		THE UNSCENTED KALMAN FILTER The inherent flaws of the EKF are due to its linearization approach for calculating the mean and covariance of a random variable which undergoes a nonlinear transformation.
		The Unscented Kalman Filter for Nonlinear Estimation - Wan, van der Merwe (2000)
	sherry.ifi.unizh.ch /context/120136/0 (4215 words)

	Kalman filter preprocessor - Patent 4511219 (Site not responding. Last check: 2007-09-19)
		An extended Kalman filter algorithm is a computerized technique developed in the general area of estimation theory for nonlinear systems.
		Typically, the Kalman filter algorithm is used with space navigation systems and usually involves the use of a covariance matrix and weighting coefficients.
		At the present time, computer usage of the extended Kalman filter algorithm requires the calculation of three preliminary functions in order to be able to utilize the algorithm with data that is collected optically.
	www.freepatentsonline.com /4511219.html (2956 words)

	Apparatus and method for calibrating a sensor system using the Fast Kalman Filtering formula - Patent 5506794
		An optimal recursive filter is one for which there is no need to store all past measurements for the purpose of computing present estimates of the state parameters.
		However, the Kalman Recursions would now require the inversions of the very large matrices in equations (6) or (11) because measurements must be processed in large data batches in order to create observability for the calibration parameters.
		The advantages over an prior art centralized Kalman filtering technique are an increased total system throughput by parallel operation of local filters and a further increase of system throughput by using the local filters for data compression.
	www.freepatentsonline.com /5506794.html (3958 words)

	Application of a Kalman Filter at UKIRT
		This filter is primarily intended to reduce the quantization errors, but an unexpected side effect was that it significantly improved the performance of the periodic filter.
		The conclusion we drew was that the errors remaining after the periodic filter were fairly Gaussian and noise like and so were correctly rejected by the Kalman filter.
		The final advantage of the filter is that it enabled us to increase the loop gain of the system by a factor of 2, thereby improving the frequency response in the presence of other disturbances.
	www.adass.org /adass/proceedings/adass99/P3-07 (1209 words)

	Graphical Models
		The Kalman filter has been proposed as a model for how the brain integrates visual cues over time to infer the state of the world, although the reality is obviously much more complicated.
		The main point is not that the Kalman filter is the right model, but that the brain is combining bottom up and top down cues.
		The mean squared error of the filtered estimate is 4.9; for the smoothed estimate it is 3.2.
	www.cs.ubc.ca /~murphyk/Bayes/bayes.html (6598 words)

	Citations: Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks - Puskorius, ... (Site not responding. Last check: 2007-09-19)
		230 The extended Kalman filter and smoother (EKFS) algorithm is a forward backward algorithm and can be derived as an approximation to posterior mode estimation for Gaussian error sequences [20] Its application to our framework amounts to approximating x t T where x t T is the....
		In this paper we prefer to focus on aspects of the learning procedures which are mainly connected with the complexity of the class of functions to be learnt, in a perspective which is much closer to that of the theory of Computational Learning [134, 135] We deal....
		The extended Kalman filter method was used for training due to its speed and capability of escaping local minima.
	citeseer.lcs.mit.edu /context/73348/0 (5782 words)

	A GOD GIVEN INVENTION:
		If my formula is employed exactly as a Kalman Filter then all the important results that Professor R. Kalman had already verified at the end of the 1950's apply.
		The same panel complained that it is computationally impossible for the Kalman Filter to be applied to these large problems.
		However, they did hope to see light at the other end of the tunnel, and concluded that perhaps sometime in the future it would be possible with the aid of a thousand super computers.
	personal.fimnet.fi /business/terhikki.lange/FKFdream.html (1585 words)

	Articles - Fast Kalman filter (Site not responding. Last check: 2007-09-19)
		It would then be impossible to combine information on recursive state estimates in an optimal fashion for a Kalman filter.
		Consequently, the stability of such suboptimal Kalman filtering may severely suffer even though the observability and controllability conditions were satisfied.
		Lange, A. (2003): "Optimal Kalman Filtering for ultra-reliable Tracking", ESA CD-ROM WPP-237, Atmospheric Remote Sensing using Satellite Navigation Systems, Special Symposium of the URSI Joint Working Group FG, 13-15 October 2003, Matera, Italy.
	www.voiprealfan.com /articles/Fast_Kalman_filter (1221 words)

	Fast Implementations of the Kalman Bucy Filter for Satellite Data Assimilation (ResearchIndex)
		Fast Implementations of the Kalman Bucy Filter for Satellite Data Assimilation
		Abstract: We present practical data assimilation algorithms based on the Kalman Bucy filter (KBf) for combining satellite altimetry data with the nonlinear ocean circulation models.
		10 An approximate Kalman filter for ocean data assimilation: an..
	citeseer.ist.psu.edu /587761.html (318 words)

	1.10 Assimilation of satellite data in 3-D CTMs using sub-optimal Kalman filter (2000 - 11middleatmo) (Site not responding. Last check: 2007-09-19)
		A sequential assimilation approach based on the sub-optimal Kalman filter was developed and implemented for use with general global chemistry-transport models.
		This method allows fast assimilation and mapping of satellite observations and provides estimates of analysis errors.
		Additionally, a number of diagnostic parameters are provided that allow assessment of uncertanties of the utilized model, such as biases and model error growth rate.
	ams.confex.com /ams/annual2000/techprogram/paper_6204.htm (181 words)

	A parameterization of the Kalman filter track reconstruction in the ALICE TPC (Site not responding. Last check: 2007-09-19)
		Therefore, a fast simulation is necessary for the study of physics signals requiring a very large number of events.
		We have implemented and tested a parameterization of the Kalman filter tracking in the TPC to describe the tracking efficiency, the resolution on track parameters, the covariance matrix and the dE/dx.
		As an example, we used this tool to perform the tracking in the ITS using the Kalman filter without including the TPC in the simulation.
	www.pd.infn.it /alipd/abstracts/abs5.html (132 words)

	Recent Dissertations of Civil Engineering
		The Kalman filter, which incorporates both model and measurement uncertainties to achieve optimal estimate of state variables with minimum error covariance matrix, becomes a natural choice to address the problem of simultaneous identification of system and excitation characteristics from measurement alone.
		An extended Kalman filter algorithm is implemented for structural identification problems formulated in the frequency domain.
		In this dissertation, a Kalman filter is applied to problems formulated in the frequency domain to estimate the dynamic characteristics (including confidence intervals), as well as the input excitation more reliably.
	www.ce.jhu.edu /abstracts.htm (19404 words)

	Kalman Filter
		PICs are not capable of handling the large floating point aritnmetic and are not fast enough for large systems.
		If you are using a one or two state filter you may use it.
		You may use a simple adaptive filter that may not require significant matrix inversions.
	www.edaboard.com /ftopic105648.html (67 words)

	Multichannel Recursive-Least-Squares Algorithms (ResearchIndex) (Site not responding. Last check: 2007-09-19)
		In those fields, multichannel FIR adaptive filters are extensively used.
		For the learning of FIR adaptive filters, recursive-least-squares (RLS) algorithms are known to produce a faster convergence speed than stochastic gradient descent techniques, such as the basic least-mean-squares (LMS) algorithm or even the fast convergence Newton-LMS, the...
		4 Application of fast Kalman estimation to adaptive equalizati..
	citeseer.csail.mit.edu /720884.html (510 words)

	EURASIP Journal on Applied Signal Processing (Site not responding. Last check: 2007-09-19)
		It is demonstrated that out of all the formulas that constitute this fast Recursive Least Squares (RLS) scheme only three generate an amount of finite-precision error that consistently propagates in the subsequent iterations and eventually makes the algorithm fail after a certain number of recursions.
		On the basis of the previous analysis a method of stabilization of the fast-Kalman algorithm is developed and is presented here, a method that allows for the fast-Kalman algorithm to follow very difficult signals such as music, speech, environmental noise, and other nonstationary ones.
		Keywords and phrases: Kalman filtering, recursive least squares filtering, adaptive algorithms, quantization error in fast-Kalman algorithm, finite-precision error in RLS algorithms.
	www.hindawi.com /journals/asp/volume-2001/S1110865701000014.html (260 words)

	Advanced Financial Engineering Mathematics applied to algorithmic trading of stocks and commodities by Dennis Meyers ...
		The EPFFT super fast DLL takes the FFT at each price bar, filters the noisy price series using a unique noise filter in the frequency domain, and creates a one bar ahead noise filtered projected price.
		The Goertzel Discrete Fourier Transform (GDFT) super fast DLL finds the N cycles(frequencies) with the highest amplitudes at each price bar, and creates a x bar ahead (x is user selectable) noise filtered projected momentum curve.
		The five parameter parabolic adds a noise filter and changeable starting stop value that minimizes the whipsaw losses that can occur with the regular parabolic indicator.
	www.meyersanalytics.com (985 words)

	MathGroup Archive: November 2002 [00209]
		Dear Roy, I have used the TimeSeries Kalman Filter functions about 3 Years ago, but not extensively.
		I cannot tell you whether it is fast since my problems were rather small.
		Of course, it is possible to conduct likelihood estimation, but I cannot tell whether this is fast.
	forums.wolfram.com /mathgroup/archive/2002/Nov/msg00209.html (355 words)

	R help 2000: Re: [R] state-space models and kalman filter (Site not responding. Last check: 2007-09-19)
		In reply to: Elliot Williams: "Re: [R] state-space models and kalman filter"
		If it is gone try his book ot the paper in J.Time Series.
		fast but they seem to work OK as an input to nlm(the R I use is
	www.r-project.org /nocvs/mail/r-help/2000/3340.html (349 words)

Try your search on: Qwika (all wikis)