Minimal parameter solution of the orthogonal matrix differentialequation

The straightforward solution of the first-order differential equation satisfied by all nth-order orthogonal matrices requires n² integrations to obtain the matrix elements. There are, however, only n(n-1)/2 independent parameters which determine an orthogonal matrix. The questions of choosing them, finding their differential equation, and expressing the orthogonal matrix in terms of these parameters are considered in the present work. Several possibilities which are based on attitude determination in three dimensions are examined. It is concluded that not all 3-D methods have useful extensions to other dimensions, and that the 3-D Gibbs vector (or Cayley parameters) provide the most useful extension. An algorithm is developed using the resulting parameters, which are termed extended Rodrigues parameters, and numerical results are presented of the application of the algorithm to a fourth-order matrix     

On the connection between estimability and observability

It is shown that when a linear dynamic system is stochastically autonomous (that is, when the system is not excited by a random signal), its estimability property as defined by Y. Baram and T. Kailath (ibid., vol.33, p.1116-21, Dec. 1988) reduces to the classical observability property     

A unidimensional convergence test for matrix iterative processes applied to Strapdown Navigation

The investigation of the convergence properties of matrix iterative processes usually involves test matrices of high order. This fact may prohibit an analytic approach to the problem. In this paper a method is presented which converts the multidimensional test procedure into a scalar one. The method is presented in conjunction with the problem of matrix orthogonalization which exists in Strapdown Inertial Navigation. Three examples are presented in which the convergence of matrix orthogonalization techniques is investigated. The examples demonstrate the use of the unidimensional convergence test in determining the order of the processes and in finding sufficient conditions for convergence. Numerical results are presented.     

Strapdown SAP gyro in a back-to-back configuration

In a recent paper [1] a back-to-back configuration of two strapdown single axis electrically rebalanced rate integrating gyros was introduced. It was shown that, theoretically, some of the errors due to cross-coupling between the components of the vehicular angular rates and accelerations drop out. In this paper the same idea is applied to a second class of gyros used in strapdown systems, namely: the single axis platform (SAP) gyro. The error expression for the SAP is derived and the cancellation of the error due to output axis angular rate is shown when the back-to-back configuration is used.     

Eigenfactor solution of the matrix Riccati equation--A continuous square root algorithm

This paper introduces a new algorithm for solving the matrix Riccati equation. Differential equations for the eigenvalues and eigenvectors of the solution matrix are developed in which their derivatives are expressed in terms of the eigenvalues and eigenvectors themselves and not as functions of the solution matrix. The solution of these equations yields, then, the time behavior of the eigenvalues and eigenvectors of the solution matrix. A reconstruction of the matrix itself at any desired time is immediately obtained through a trivial similarity transformation. This algorithm serves two purposes. First, being a square root solution, it entails all the advantages of square root algorithms such as nonnegative definiteness and accuracy. Secondly, it furnishes the eigenvalues and eigenvectors of the solution matrix continuously without resorting to the complicated route of solving the equation directly and then decomposing the solution matrix into its eigenvalues and eigenvectors. The algorithm which handles cases of distinct as well as multiple eigenvalues is tested on several examples. Through these examples it is seen that the algorithm is indeed more accurate than the ordinary one. Moreover, it is seen that the algorithm works in cases where the ordinary algorithm fails and even in cases where the closed-form solution cannot be computed as a result of numerical difficulties.