Linear filtering and piecewise linear correlation functions.
Optimal linear mean square filtering for piecewise linear correlation functions
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Optimal linear mean square filtering for piecewise linear correlation functions
Optimum realizable linear filter role in some communication problems, considering detection and continuous estimation
Angle modulated signal with arbitrary phase function applied to symmetrical, narrow, band pass filter in linear FM receiver
Optimum linear filter for message extraction from additive white noise
Multivariable linear filter theory applied to space vehicle guidance
Linearly filtered angle modulated signals distortion and crosstalk analysis applied to filter input-output relation, noting satellite communication FM reception
Linear filtering of integrated signal observed in white noise
Optimal linear filter derivation using Pontryagin maximum principle and gradient matrices for optimal filter coefficients
Optimum linear filtering of integrated signal in white noise, deriving expression for minimum mean square error
Optimal linear filter for continuous time-varying dynamic system, using measurements containing additive white noise
Digital "fader" or "ramp shaper" circuits replace linear filters in suppressing switching transients and instabilities within servocontrol systems. Circuits can be optimized to introduce no attenuation, transport delay, or phase lags in new output signal.
Differential equation technique of representing dynamic systems for optimal control problems, and fundamental role of optimum linear filter
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
Performance criterion for linear statistical filters with random inputs
State of randomly excited dynamical system estimated from noise measurements having finite duration, and optimal linear filter synthesized from conditional probability theory
Optimum linear separation of random message from additive white noise of given spectral height realized as unity feedback system
This paper presents the development and performance of a special algorithm for estimating the attitude and angular rate of a spacecraft. The algorithm is a pseudo-linear Kalman filter, which is an ordinary linear Kalman filter that operates on a linear model whose matrices are current state estimate dependent. The nonlinear rotational dynamics equation of the spacecraft is presented in the state space as a state-dependent linear system. Two types of measurements are considered. One type is a measurement of the quaternion of rotation, which is obtained from a newly introduced star tracker based apparatus. The other type of measurement is that of vectors, which permits the use of a variety of vector measuring sensors like sun sensors and magnetometers. While quaternion measurements are related linearly to the state vector, vector measurements constitute a nonlinear function of the state vector. Therefore, in this paper, a state-dependent linear measurement equation is developed for the vector measurement case. The state-dependent pseudo linear filter is applied to simulated spacecraft rotations and adequate estimates of the spacecraft attitude and rate are obtained for the case of quaternion measurements as well as of vector measurements.
Proof of relationship between state estimates and error covariance matrices in state-space representations in linear filtering