Adaptive Kalman filtering based on higher-order statistical analysis for digitalized silicon microgyroscope

In order to reduce the noise level of a silicon microgyroscope (SMG) digital system, a filter based on the theory of Kalman filter algorithm is designed. The performance of a Kalman filter depends on the proper model identification and parameter estimation. The method of higher-order statistical theoretical analysis is used to obtain the statistical properties of the gyroscope random signal. By modifying the conventional Kalman filter as adaptive Kalman filter, the filtering performance is enhanced. The filter is programmed in the Field Programmable Gate Array (FPGA) soft-core of the silicon microgyroscope digital system, based on SOPC (System-on-a-Programmable-Chip) technology. The effect of the filter is evaluated by Allan variance analysis. It is shown that noise characteristics such as quantization noise and angular random walk are obviously lowered compared with the original signal. In this way, by making use of ARMA (Auto Regression Moving Average) modeling results, a method of designing adaptive Kalman filter on digitalized SMG system is implemented.