ADAPTIVE AND ROBUST SINGULAR VALUE DECOMPOSITION AIDED CUBATURE KALMAN FILTER WITH CHI-SQUARE TEST

Wei Zhao, Huiguang Li, Liying Zou, and Renhui Yuan

Keywords

Robustness, adaptability, Cubature Kalman filter, chisquare test, singular value decomposition

Abstract

The state variation and gross error will cause decline or divergence of performance of Kalman filter, during the state estimation in a stochastic dynamic system. To solve this problem, the paper gives an adaptive and robust Cubature Kalman filter aided by singular value decomposition. Since when state variation and gross error occur, the covariance of innovation cannot keep orthogonal, the adaptive and robust method uses chi-square test as the judgment of the state variation and gross error. Then the strong-trackingfilter-based adaptive algorithm is used for adjusting process noise covariance matrix to eliminate the impact of state variation, and the measurement-noise-inflating robust algorithm is used for adjusting measurement noise to eliminate the impact of gross error. Results of numerical simulation show that, the proposed method is adaptive to the state variation and robust to the gross error, especially keeps adaptability and robustness when the two cases occur at the same time.

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