Showing papers on "Alpha beta filter published in 1969"

A cellular computer to implement the kalman filter algorithm

Abstract: The subject of this thesis is the development of the design for a specially-organized, general-purpose computer which performs matrix operations efficiently. The content of the thesis is summarized as follows: First, a review of the relevant work which has been done with microcellular and macrocellular techniques is made, Second, the discrete Kalman filter is described as an example of the type of problem for which this computer is efficient. Third, a detailed design for a cellular, array-structured computer is presented. Fourth, a computer program which simulates the cellular computer is described. Fifth, the recommendation is made that one cell and the associated control circuits be constructed to determine the feasibility of producing a hardware realization of the entire computer. A CELLL1TAR COMPUTER TO IMPLEMENT THE JLimAN FILTER ALGORITHM

An Evaluation of a Pilot Model Based on Kalman Filtering and Optimal Control

Abstract: A pilot model based on Kalman filtering and optimal control is given which, because of its structure, provides for estimation of the plant state variables, the forcing functions, the time delay, and the neuromuscular lag. The inverse filter and control problem is considered where the noise and cost function parameters yield a frequency response which is in close agreement with that found experimentally. A good correspondence with sine-wave tracking is shown including "eyes closed" tracking.

On the relative performance of the Kalman and Wiener filters

Abstract: Upper and lower bounds on the error covariance matrices of the Kalman and Wiener filters for linear finite state time-invariant system are derived. These bounds yield a measure of the relative estimation accuracy of these filters and provide a practical tool for determining when the implementational complexity of a Kalman filter can be justified. The calculation of these bounds requires little more than the determination of the corresponding Wiener filter.

On the linear smoothing problem

Abstract: The solution to the smoothing problem for a linear discrete-time system can be obtained directly from Kalman filtering theory by first converting it into a special case of the standard linear filtering problem. This conversion is accomplished by suitably defining a new state vector which contains all the relevant information about the past history of the system.

Design algorithms for minimal-order Luenberger observers

Abstract: A simple method for designing an (n — m)-order Luenberger observer for an nth-order linear deterministic system with m independent outputs is outlined and compared with the observer design method recently suggested by Cumming.

Reduced order Kalman filter

Abstract: The order of the Kalman filter equations for a wide class of aerospace navigation problems is reduced, yielding an optimal sequential linear filter with a substantial decrease in computer requirements. A theorem is proved generalizing the Kalman filter to handle step-wise correlated noise. An illustrative example is then presented in which computer computation time and storage requirements are reduced by more than half with negligible increase in programming complexity.

Kalman Filtering and Its Application to Reliability

Abstract: The basic ideas of Kalman recursive filtering is explained in such a manner that the application of these ideas to reliability may be seen. The Bayesian viewpoint is adopted, and the explanation of Kalman filtering is broken into two parts: 1) the combination of an old estimate with data, and 2) the updating of estimates via the system model.

On the optimum colored noise Kalman filter

Abstract: A set of equations is presented for designing an optimum Kalman filter for a continuous linear dynamic system with colored measurement noise only. Included are the optimum Kalman filter variance, gain, and mechanization equations in a form which can be computerized easily and requires a minimum of engineering effort.

A Simple Development of the Wiener-Hopf Equation and the Derived Kalman Filter

Abstract: The Wiener-Hopf equation is developed using only a knowledge of elementary calculus and the definition of ensemble average from statistics. Then using the Leibniz equation for differentiating an integral, the Kalman filter and its relation to orthogonal projection is presented.

On the Extraction of Signals from Environments Containing Additive-Noise and Multiplicative Characteristics

Abstract: The purpose of this paper is to present an applications-oriented discussion of the Kalman filter theory. The subject has received extensive treatment in the fields of orbit estimation and deterministic control system theory. This paper will emphasize the extraction of information from an additive-noise environment, i.e., the classical observation problem and its relation to the estimation theory. This subject is chosen in order to make the discussion concrete, and because of the general lack of application of the newer Kalman theory to this important area. Engineering application of the filter theory is discussed by working through a filter design that involves both compensation and estimation.