ML and EM Estimation of Sampling Intervals of Sensor Devices

With rising demand for sensor networks in recent years, microelectromechanical systems (MEMS) sensors are growing more attractive because they are tiny and available at increasingly lower cost. A data sample detected using a MEMS sensor is, in general, stored temporarily in a register of the sensor module. It is regarded as a buffer of only one sample. Therefore, buffer overrun/underrun is likely to occur. A precise method of estimating update interval of the register is discussed herein in conjunction with modeling of the problem. According to the comparison between every pair of adjacent data samples at various intervals, binary random variables are constructed. Based on the proposed model, the update interval is estimated by a slightly modified version of basic estimation algorithms such as ML and EM. It is shown that the proposed method successfully estimates the update intervals for an actual MEMS pressure sensor.