Jet Transport Application to Particle Filter for Attitude Estimation of Tumbling Space Objects

The particle filter is one of the most powerful methods for nonlinear state estimation of spacecraft on account of its accuracy and stability. However, its heavy computational burden limits its application in real-time estimations. In this chapter, we propose an improvement for the particle filter based on the Jet Transport (JT) method, and apply it to the real-time attitude estimation of tumbling space objects. The main innovation of the Jet Transport particle filter (JTPF) is to use the Jet Transport technique in the particle evolution process, rather than using the numerical integration as the classical particle filter does, so as to reduce the computational burden of the algorithm. Furthermore, the proposed JTPF uses the multiplicative error quaternion to avoid further errors in the normalizing process and the regularization technique to avoid the particle degeneracy. The JTPF is tested in three scenarios with different state and observation dimensions. Monte Carlo simulations demonstrate that the JTPF has a similar accuracy as the classical particle filter, and costs only a \(7 \,\mathrm {\%} \sim 13 \,\mathrm {\%}\) of CPU time of the latter. Moreover, some empirical rules are summarized about the optimal Jet Transport expansion order and particle number of the JTPF.