A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases

The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.

作 者： GAO Yu ZHANG Jian-qiu HU Bo   作者單位： Department of Electronics, Fudan University, Shanghai 200433, China  刊 名： 中國(guó)航空學(xué)報(bào)（英文版）  ISTIC 英文刊名： CHINESE JOURNAL OF AERONAUTICS  年，卷(期)： 2007 20(3)  分類(lèi)號(hào)： V2  關(guān)鍵詞： signal processing   dynamic bias estimation   simulation   multisensor   Kalman filter  

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