Content area
Full text
Received Jan 28, 2018; Accepted Apr 8, 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
The unmanned aerial vehicles (UAV) have been widely used in civil and military applications, including search and rescue operations, area mapping, weather monitoring, and agricultural operations [1–4]. Whenever the inertial navigation system (INS) of UAV is concerned, cost or weight is always an issue; therefore, the accurate inertial sensors have been constantly excluded. Instead, the microelectromechanical systems (MEMS) have been universally used [5–7], which have the characteristics of lightweight, small mass, less expensive, and lower power requirements [8, 9]. Typically, MEMS sensors have large bias drifts and stochastic errors, which make it difficult to use the MEMS sensors as INS only. Generally, the combination of INS/GPS is used to provide an ideal navigation system with full capability of continuous position, velocity, and attitude output [10–12]. However, the accuracy of the integrated navigation system degrades with time when GPS signals are blocked in environments such as high buildings and indoors. In order to control the simple INS error within a certain range, it is necessary to estimate and identify the various noise terms existing in MEMS sensors.
Allan variance method is a time analysis technique developed by Dr. David Allan to study the characteristic of random noise terms and stability in precision oscillators used in clock application [13]. Allan variance method can be used to determine the characteristics of the underlying random processes which lead to data noises [14, 15], and it is also generally used to identify the errors of inertial sensors (i.e., gyroscopes and accelerometers) [16–19]. The dynamical Allan variance (DAVAR) is a sliding version of Allan variance, which could represent the nonstationary behavior of the signal [20, 21]. For MEMS sensor analyses, DAVAR could track and describe the dynamic characteristics of time series, and it is advantageous to analyze the process of gyroscope errors. The DAVAR is a cluster of Allan variance; therefore, the computational burden is very high because the DAVAR requires the computation of an Allan variance at every time instant [22, 23]. A recursive algorithm for DAVAR is proposed in...