Abstract
How to limit the drifts of the navigation errors in an inertial navigation system (INS) with low-cost sensors is one of the main challenges for the land vehicle navigations. In this paper, we present a novel hybrid navigation strategy to integrate the Micro-Electric-Mechanic-systems (MEMS) INS, odometer (OD) and global navigation satellite systems (GNSS), with aim to enhance the positioning accuracy of the inertial system during GNSS outages. To accurately estimate the INS error states, the neural network (NN) is proposed to mimic the velocity of the navigation frame with the data from the MEMS INS, odometer, as well as the non-holonomic constraints (NHC). The long short-term memory (LSTM) NN is adopted in our approach due to its ability to adaptively use the data in the past. The road tests are conducted with two different MEMS IMUs to verify the proposed navigation strategy. Comparing to the traditional integrated MEMS INS/OD/GNSS system based on the extended Kalman filtering (EKF), our hybrid approach provides over 60% improvements in terms of the root mean square (RMS) and maximum horizontal position errors during GNSS outages.
Highlights
The strapdown inertial navigation system (INS)usually consists of a triad of accelerometers and gyroscopes, which measures the vehicle’s acceleration and rotation rate with respect to the inertial frame
The main drawback of INS is that the navigation errors, including position, velocity and attitude errors accumulate over time because of the intrinsic property of dead reckoning [1]
The global navigation satellite systems (GNSS) signals are to be blocked when the vehicle travels through urban areas or tunnels, the navigation errors still accumulate when the INS works in standalone mode during GNSS outages
Summary
The strapdown inertial navigation system (INS)usually consists of a triad of accelerometers and gyroscopes, which measures the vehicle’s acceleration and rotation rate with respect to the inertial frame. Due to the merits of low-cost, small-size and low powerconsumption [2], the Micro-Electric-Mechanic-systems (MEMS) inertial sensors have been widely employed for the land vehicle navigations. They feature significant sensor errors, such as high-frequency noise, bias instability and misalignment errors [3, 4]. To eliminate the error accumulations of an inertial system, the global navigation satellite systems (GNSS) was widely employed, and the Kalman filtering (KF) is the most popular technique to fuse the GNSS and INS data. The position errors accumulate to hundreds of meters in tens of seconds for the MEMS INS [8, 9]
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