IEEE Signal Processing Letters · 2013 · 99 citations · 15 references
ComputingComputer ScienceComputational GeometryGeometric ModelingSensor FusionBayesian FilteringDigital Signal ProcessingKalman FilteringState EstimationTracking SystemsAccuracyEfficiencyMomentsNonlinear EstimationNonlinear State Estimation
In this letter, a new class of cubature Kalman filters (CKF) based on a spherical simplex-radial rule is proposed to further improve accuracy and efficiency of the traditional CKF. The transformation group of the regular simplex and the moment matching method are utilized to compute the spherical and radial integrals, respectively. In addition, with the increase of the state dimension, the new CKF of the fifth degree uses fewer quadrature points than the traditional CKF with the same degree. Simulations on moments calculation and nonlinear state estimation show that the proposed CKFs can achieve higher accuracy and better efficiency than the traditional CKFs.
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