Motion-Constrained GNSS/INS Integrated Navigation Method Based on BP Neural Network

Abstract

The global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation system have been widely used in Intelligent Transportation Systems (ITSs). However, the positioning error of integrated navigation systems is rapidly divergent when GNSS outages occur. Motion constraint and back propagation (BP) neural networks can provide additional knowledge to solve this issue. However, the predictions of a neural network have outliers and motion constraint is difficult to adapt according to the motion states of vehicles and boats. Therefore, this paper fused a BP neural network with motion constraints, and proposed a motion-constrained GNSS/INS integrated navigation method based on a BP neural network (MC-BP method). The pseudo-measurement of the GNSS was predicted using a fitting model trained by the BP neural network. At the same time, the prediction outliers were detected and corrected using motion constraint. To assess the performance of the proposed method, simulated and real data experiments were conducted with a vehicle on land and a boat offshore. A classical GNSS/INS integration algorithm, a motion-constrained GNSS/INS algorithm, and the proposed method were compared through data processing. Compared with the classical GNSS/INS integration algorithm and the motion-constrained GNSS/INS algorithm, the positioning accuracies of the proposed method were improved by 90% and 64%, respectively, in the vehicle land experiment. Similar performances were found in the offshore boat experiment. Using the proposed MC-BP method, improved meter-level-positioning results can be achieved with the GNSS/INS integration algorithm when GNSS outages occur.

A tightly coupled GNSS RTK/IMU integration with GA-BP neural network for challenging urban navigation

“…In harsh urban environments such as tunnels or overpasses, however, when GNSS signals can be continuously or intermittently blocked for long periods, IMU errors can increase rapidly, severely affecting the performance of the navigation system [3]. Although additional sensors, such as barometers, odometers, cameras, light detection and ranging with various fusion algorithms can be used to assist GNSS/IMU integrated navigation in such environments [4][5][6][7][8], the costly and complex fusion process can still be a issue to be solved for inhibiting their widespread use [9]. In recent years, machine learning algorithms have been demonstrated to improve the performance of GNSS/IMU integrated navigation by modelling the mathematical relationship between navigation parameters and vehicle dynamic data when GNSS fails [10].…”

A robust integrated navigation optimization method for USV in signal occlusion environment

“…In recent years, with the rapid development of artificial intelligence algorithms, its applicability in solving nonlinear problems has gradually improved, so it has begun to be used to assist navigation systems. Researchers have used methods such as support vector regression (SVR) [29], extreme learning machine (ELM) [30], Back propagation (BP) network [31], Long Short-Term Memory (LSTM) network [32], gated recurrent unit (GRU) network [33] and LightGBM regression [34] to conduct research on navigation assistance for vehicles, ships, and unmanned underwater vehicles. Meanwhile, when using artificial intelligence algorithms to predict GNSS navigation results using INS navigation results, historical data based on a certain step size can effectively improve the prediction accuracy [35].…”

A Novel Optimal Robust Adaptive Scheme for Accurate GNSS RTK/INS Tightly Coupled Integration in Urban Environments

“…As one of the three common integration methods of GNSS/INS, the biggest advantage of tightly coupled GNSS/INS is that the INS provides prior position information for GNSS and can filter and solve normally when the number of available GNSS satellites is insufficient [20]. In the open sky, tightly coupled GNSS real-time kinematic (RTK)/INS can make full use of INS information to assist ambiguity resolution and cycle slip detection, and high-precision carrier phase differential observations can better correct INS drift errors and improve system accuracy [21].…”