Open Access
ARTICLE
Maruthi R. Akella1, Sofokli Cakalli2
CMES-Computer Modeling in Engineering & Sciences, Vol.111, No.2, pp. 119-127, 2016, DOI:10.3970/cmes.2016.111.119
Abstract The planar motion of a particle within an arbitrary potential field is considered. The particle is additionally subject to an external force wherein the applied thrust-acceleration is constrained to remain normal to the velocity vector. The system is thus non-conservative but since the thrust force is non-working, the total energy is a conserved quantity. Under this setting, a major result of fundamental importance is established in this paper: that the flight direction angle (more precisely, the sine of the angle between the position and velocity vectors) is shown to always satisfy a linear first-order differential equation with variable coefficients that… More >
Open Access
ARTICLE
Xiaoli Bai1, John L. Junkins2
CMES-Computer Modeling in Engineering & Sciences, Vol.111, No.2, pp. 129-146, 2016, DOI:10.3970/cmes.2016.111.129
Abstract This paper presents Modified Chebyshev-Picard Iteration (MCPI) methods for long-term integration of the coupled orbit and attitude dynamics. Although most orbit predictions for operational satellites have assumed that the attitude dynamics is decoupled from the orbit dynamics, the fully coupled dynamics is required for the solutions of uncontrolled space debris and space objects with high area-to-mass ratio, for which cross sectional area is constantly changing leading to significant change on the solar radiation pressure and atmospheric drag. MCPI is a set of methods for solution of initial value problems and boundary value problems. The methods refine an orthogonal function approximation… More >
Open Access
ARTICLE
Dylan Conway1, Daniele Mortari2
CMES-Computer Modeling in Engineering & Sciences, Vol.111, No.2, pp. 147-169, 2016, DOI:10.3970/cmes.2016.111.147
Abstract This paper presents a new method to estimate position from line-ofsight measurements to known targets when attitude is known. The algorithm has two stages. The first produces a closed-form unbiased estimate for position that does not account for the measurement error covariance. The second stage is iterative and produces an estimate of position that explicitly accounts for the measurement error covariance and the coupling between measurement error and sensor-to-target distance. The algorithm gives an accurate estimate of both position and the corresponding position error covariance and has a low computational cost. The computational complexity is O(n) for n point-targets and… More >
Open Access
ARTICLE
C. Frueh1
CMES-Computer Modeling in Engineering & Sciences, Vol.111, No.2, pp. 171-201, 2016, DOI:10.3970/cmes.2016.111.171
Abstract In recent years, probabilistic tracking methods have been becoming increasingly popular for solving the multi-target tracking problem in Space Situational Awareness (SSA). Bayesian frameworks have been used to describe the objects' of interest states and cardinality as point processes. The inputs of the Bayesian framework filters are a probabilistic description of the scene at hand, the probability of clutter during the observation, the probability of detection of the objects, the probability of object survival and birth rates, and in the state update, the measurement uncertainty and process noise for the propagation. However, in the filter derivation, the assumptions of Poisson… More >
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