一种基于FIFO机制的卡尔曼滤波新方法

在传感器采集数据的过程中,不可避免地受到来自传感器内部噪声(来自传感器件和电路元件的噪声)和外部噪声(温度变化、电磁辐射等带来的噪声)的影响。针对这种情况,在改善传感器结构、信号处理电路之外,设计了一种新算法,将基于同步先进先出队列机制的递推平均滤波算法和卡尔曼滤波算法结合起来,应用于传感器数据采集的预处理过程,并通过FPGA实现。试验结果表明,该算法有效降低了噪声,改善了传感器的非线性,提高测量的精确性和有效性。

A New Approach of FIFO-based Kalman Filting

In the process of sensors＇ data collection, signals are inevitably influenced by internal noise from the sensor （noise from the sensor com- ponents and circuitry） and external noise （noise caused by temperature electromagnetic radiation etc.）. In view of this situation, except for improving the sensor structure and the signal processing circuit, a new algorithm is designed, which is combined with a recursive filtering algo-rithm based on the average synchronous FIFO and a Kalman filter algorithm to be used in the pre-processing sensor data acquisition process, and applied with FPGA. The results show that the algorithm effectively reduces the noise and improves the sensor＇s nonlinearity and mea-surements＇ accuracy and effectiveness.