# Relative Motion Estimation Algorithm for Noncooperative Targets Considering Multiple Solutions of Rotational Parameters

## TL;DR

This paper introduces a new method for estimating the motion of noncooperative space objects using stereovision and advanced filtering techniques.

## Contribution

A novel relative motion estimation algorithm is proposed that resolves multiple rotational parameter solutions using a unique principal axis coordinate frame and an EKF-based filter.

## Key findings

- The method identifies target mass distribution using least-square and angular momentum conservation.
- A unique principal axis coordinate frame resolves the multiple-solution problem in rotational estimation.
- The EKF-based filter effectively estimates motion states and inertia parameters even under occlusion.

## Abstract

On-orbit servicing using a space robot is gaining popularity among the space community for both economic and safety aspects. In particular, the estimation of the relative motion of a noncooperative target is a challenging problem. This study presents a relative motion estimation scheme based on stereovision for noncooperative targets considering multiple solutions of rotational parameters. Specifically, the mass distribution of the target is identified based on the least-square method and the principle of conservation of angular momentum. Then, the determination of a unique principal axis coordinate frame of the target is employed to resolve the multiple-solution problem. In addition, an EKF (extended Kalman filter)-based filter with global observability is designed to estimate the full motion states and inertia parameters of the target. The convergence performance of the proposed method is verified by numerical simulation. The results also demonstrate that the method is robust to occlusion.