Next: 2.3 Restricted orbit identification Up: 2. Identification penalties Previous: 2.1 Differential corrections as

## 2.2 Linear orbit identification

By orbit identification problem we mean to find an algorithm to determine which couples of orbits, among many included in some catalog, might belong to the same object. We assume that both orbits, for which the possibility of identification is being investigated, have been obtained as solutions of a least squares problem. Note that this is not always the case for orbit catalogs containing asteroids observed only over a short arc. There are therefore two uniquely defined vectors of elements, and , and the normal and covariance matrices computed after convergence of the iterative differential correction procedure, that is at . The two target functions of the two separate orbit determination processes are:

where are the two vectors of dimensions of residuals of the separate orbit determination processes.

For the two orbits to represent the same object, observed at different times, we need to find a low enough minimum for the joint target function, formed with the sum of squares of the residuals:

where is the value corresponding to the sum (with suitable weighting) of the two separate minima, and the penalty measures the increase in the target function which results from the need to use the same orbit for both sets of observations.

The linear algorithm to solve the problem is obtained when the quasi-linear approximation can be used, not only locally, in the neighborhood of the two separate solutions and , but even globally for the joint solution. This is a very strong assumption, because in general we cannot assume that the two separate solutions are near to each other, but if the assumption is true, we can use the quadratic approximation for both penalties , and obtain an explicit formula for the solution of the identification problem:

Neglecting higher order terms, the minimum of the penalty can be found by minimizing the nonhomogeneous quadratic form of the formula above. If the new joint minimum is , then by expanding around we have

and by comparing the last two formulas we find:

If the matrix , which is the sum of the two separate normal matrices and , is positive-definite, then it is invertible and we can solve for the new minimum point:

This equation has a very simple interpretation in terms of the differential correction process: at convergence in each one of the two separate pseudo-Newton iterations, with and ; therefore

The assumption that the quasi-linear approach is applicable to the identification means that can be kept constant, thus they have the same value at and at ; under these conditions can be interpreted as the result of the first differential correction iteration for the joint problem.

The computation of the minimum identification penalty can be simplified by taking into account that is translation invariant:

Then we can compute after a translation by , that is assuming , , and :

and by defining

we have a simple expression of as a quadratic form:

Alternatively, translating by , that is with , and :

and the same matrix can be defined by the alternative expression:

Note that both these formulas only assume that exists. Under this hypothesis

 (2)

This is true in exact arithmetic, but might be difficult to realize in a numerical computation if the matrix is badly conditioned.

We can summarize the conclusions by the formula

which gives the minimum identification penalty and also allows one to assess the uncertainty of the identified solution, by defining a confidence ellipsoid with matrix .

Next: 2.3 Restricted orbit identification Up: 2. Identification penalties Previous: 2.1 Differential corrections as

Maria Eugenia Sansaturio
1999-05-20