The Marginalized Square-Root Quadrature Kalman Filter

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Bayesian filtering appears in many signal processing problems, reason which has attracted the attention of many researchers to develop efficient algorithms, yet computationally affordable. Ranging from Kalman Filter (KF) to particle filters, there is a plethora of alternatives depending on model assumptions. The authors focus their interest into a recently developed algorithm known as the Square-root Quadrature Kalman Filter (SQKF). Under the Gaussian assumption, the SQKF is seen to optimally tackle arbitrary nonlinearities by resorting to the Gauss - Hermite quadrature rules.