Research On Improving The Performance Of The Silicon Gyroscope Based On Neural Network

MEMS-based Micro-machined inertial sensors have attracted a lot of attention duringthe past few years, among which most people focus on the silicon Micro-machined inertialmeter. Although developed for many years, the performance of micro-silicon gyroscope isnot satisfied. Many researchers are devoted to enhance its performance, and get a lot ofachievements. It is well known that neural network has the ability of approximating nonlinearfunction to arbitrary accuracy, so that a neural network based micro-silicon gyroscopeperformance enhancement scheme is proposed. The employed neural networks are'delayless'nonlinear filters, which reduced the noise of micro-silicon gyroscope. The operating principle, structure, and algorithm of the scheme are described indetails. The effectiveness of this method is demonstrated by using real signals. Theoreticalanalysis and simulation experiment show that the nonlinear filter based on neural networkcan enhance the performance of micro-silicon gyroscope. A signal-processing card basedon TMS320VC5402 is also designed, including the minimum system of DSP, A/Dconverter circuit, D/A converter circuit, and RS-232 interface circuit.