Spacecraft inertia estimation via constrained least squares

This paper presents a new formulation for spacecraft inertia estimation from flight data. Specifically, the inertia estimation problem is formulated as a constrained least squares minimization problem with explicit bounds on the inertia matrix incorporated as LMIs (linear matrix inequalities). The resulting minimization problem is a semidefinite optimization problem that can be solved efficiently with guaranteed convergence to the global optimum by readily available algorithms. This method is applied to test data collected from a robotic testbed consisting of a free rotating body. The results show that the constrained least squares approach produces more accurate estimates of the inertia matrix than standard unconstrained least squares estimation methods.