A Novel Demodulation Scheme in the Digital Readout System for a Micro-Machined Gyroscope

Abstract

A novel demodulation scheme with Sage-Husa adaptive Kalman filtering algorithm is presented for the digital readout system of a micro-machined gyroscope. Based on the analysis of the detection signal from gyroscope, a mathematical model of the detection signal is established in the condition of the additive white Gaussian noise, and the state and observation model for the demodulation scheme are also given. Moreover, the simulation system is built by the Matlab/Simulink. Assuming the input signal of angular velocity is the step or sine function, and the statistical characteristics of the noise in the detection signal are unknown, simulations are given to demodulate the detection signal with different SNRs (signal-to-noise ratio). In addition, the phase sensitive demodulation scheme in the analog readout system is simulated for comparison. Results show that, the demodulation gains reach +24.23dB (input SNR is above −12.04dB) and +23.92dB (input SNR is above −16.99dB) respectively when the angular velocity input is Ω = 0.005[deg/s] for the step function, while Ω = 0.005 sin(2π × 50t)[deg/ s] for the sine function. Meanwhile, the demodulation gain increases along with the decreasing SNR of the detection signal. Compared with the analog phase sensitive demodulation scheme, the demodulation output with Sage-Husa adaptive Kalman algorithm has the shorter convergence time, but about 5dB higher of MSE (mean square error). In conclusion, the novel demodulation scheme with Sage-Husa adaptive Kalman filtering algorithm can real-timely and precisely estimate the covariance of dynamic noise in the detection signal from gyroscope, and increase the SNR of the demodulation signals dramatically. It can demodulate the angular velocity accurately and quickly, and shows a good practicability.