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Kalman filter alternate form extended to include multiple simultaneous correlated measurements, testing with ballistic model and using square root formulation for trajectory determination
Elastoplastic stress-strain behavior at notch roots in sheet specimens under constant amplitude loading
Automated generation of distributed-lumped-active network design charts by digital computer root- locus technique
Algorithms for generating and implementing square root function using high speed multipliers
Spectral signature indicators of root disease impacts on forest stands
Square root filters algorithms based on covariance and invariance matrices extended to include process noise effects
Multiparameter systems with dominant complex roots constrained by degrees of freedom to construct reduced characteristic equation
Feedback control system characteristic equation generalized root locus following technique using straight line approximation
Polynomials with interval coefficients, presenting Newton Raphson algorithm for real roots error bounds
Examination of simple coordinate transformations in Z-plane and W-plane for root locus analysis of sampled data systems
Approximate root locus method in S plane for sampled data systems, mapping constant frequency and constant damping ratio loci onto W plane
The development and testing of airborne sensors that might be effective in discrimination root rot infected trees from healthy ones are outlined. The sensing device is composed of a thermal infrared radiometer and an instant replay video scan system.
A square root formulation is presented for the discrete combined state parameter estimation problem with linear plant dynamics, Gaussian random disturbances, and constant but uncertain parameters. The estimator is a combination of the classical Kalman filter and a maximum likelihood algorithm which maximizes the parameter log-likelihood function using a first order search routine.
Formulas are derived for the root-mean-square (rms) displacement, slope, and curvature errors in an azimuth-elevation image trace of an elongated object in space, as functions of the number and spacing of the input data points and the rms elevation error in the individual input data points from a single observation station. Also, formulas are derived for the total rms displacement, slope, and curvature error vectors in the triangulation solution of an elongated object in space due to the rms displacement, slope, and curvature errors, respectively, in the azimuth-elevation image traces from different observation stations. The total rms displacement, slope, and curvature error vectors provide useful measure numbers for determining the relative merits of two or more different triangulation procedures applicable to elongated objects in space.
The Dyer-McReynolds (1969) discrete square-root filtering algorithm is extended to accommodate continuous dynamics. Differential equations are given to represent the time evolution of the filter data array. These equations are nonlinear, but it is shown that the nonlinearities act to enhance the stability of the solution.
As the application of sequential filters has grown, the need has arisen to evaluate the sensitivity of these filters to mismodeling of the random process. A numerically reliable square-root algorithm is developed for calculating the covariances of estimates from a sequential filter which incorrectly models process noise. Mismodeling is restricted to the correlation time and standard deviation of a random process represented as a first-order Gauss-Markov sequence. Using a computer program which employs this algorithm, a sensitivity analysis was performed for several types of earth-based tracking data from an interplanetary spacecraft.
The Dyer-McReynolds square-root information filter (SRIF) is rederived, using recursive least-square arguments. The result is applied to a system composed partly of biases. The filter sensitivity matrix, computed covariance, and consider covariance for this augmented system are reviewed. A new computationally attractive representation for the smoothed estimates, in terms of a smoothed sensitivity matrix and a smoothed computed covariance is presented.