In-move aligned SINS/GNSS system using recurrent wavelet neural network (RWNN)-based integration scheme

Advances in micro-electro mechanical system (MEMS) technology bring about revolutionary changes in autonomous vehicle navigation. As a new development, strap-down inertial navigation system (SINS) is effectively combined with global navigation satellite system (GNSS) to construct an integrated SINS/GNSS system. However, time-growing navigation error is the main challenge of using MEMS-grade inertial measurement unit (IMU) in the SINS/GNSS system. Failure of un-accounted inertial sensor error causes a rapid degradation in the overall performance of low-cost SINSs. This paper aims to enhance the long-term performance of low-cost MEMS-grade SINS/GNSS navigation system. A new integration scheme is presented for in-move aligned SINS/GNSS system. Un-modeled nonlinearities in the SINS dynamics as well as error uncertainties in the measurements of MEMS-grade IMU motivate using a robust data fusion algorithm for the proposed integration scheme. Considering these facts, a new recurrent wavelet neural network (RWNN)-based algorithm is designed for data fusion in the proposed in-move aligned SINS/GNSS system. Several vehicular field tests have been carried out to assess the long-term performance and accuracy of the proposed navigation algorithm.