The development and evaluation of micro-electromechanical systems inertial navigation system (MEMS-INS) for the navigation of underwater vehicles, both autonomous underwater vehicles (AUVs) and remotely-operated vehicles (ROVs), is extremely challenging. The navigation estimation of underwater vehicles is easily influenced by error noise arising in the micro inertial sensors. Therefore, this paper presents a hybrid calibration method to build the MEMS-INS and to overcome the challenges of micro inertial sensors for underwater navigation. The MEMS-INS is composed of a micro-accelerometer, a micro-gyroscope, and signal processing circuits. In general, the sources of error noise can be categorized into two groups, deterministic and stochastic: the former primarily include bias errors, misalignment and nonlinearity; the latter include temperature effects and signal drifting. Linearity calibration is used to modify deterministic errors and wavelet analysis can suppress stochastic noise. The hybrid calibration method therefore includes bias compensation, linearity calibration and wavelet signal processing to enhance the accuracy and performance of the MEMS-INS. The experimental results demonstrate that the output signal can be corrected suitably by means of the proposed method. Overall, the results confirm the strong potential of micro inertial sensors for use in underwater vehicle navigation applications.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Health(social science)
- Environmental Science(all)