Tent-ASO-BP aided GNSS/INS integrated navigation algorithm during GNSS outages
GNSS/INS integrated navigation is one of the most widely used vehicle navigation systems.However,the positioning accuracy in shielded regions such as long tunnels and basements is significantly degraded due to the long-term locking of satellite signals.To address this problem,we proposed a Tent-ASO-BP aided GNSS/INS integrated navigation algorithm.Firstly,the weight and threshold of(back propagation,BP)neural network model were optimized by combining chaotic tent map and atom search algorithm(ASO)to construct Tent-ASO-BP intelligent prediction model.Then,the intelligent prediction model was trained by using GNSS/INS integrated algorithm data collected on outdoor open areas.The well-trained Tent-ASO-BP model was used to predict the position parameters in the GNSS outage regions.Finally,vehicle field tests were performed to verify the availability of the Tent-ASO-BP model.Experimental results show that the overall accuracy of the Tent-ASO-BP prediction model is significantly higher than that of the GNSS/INS model.The root mean square error of the Tent-ASO-BP prediction model in the horizontal direction is 15.439 4 m while the GNSS/INS model is 20.429 2 m,and the horizontal accuracy is increased by 24.42%.The proposed model can effectively address the problem of continuous high-precision positioning of GNSS/INS integrated navigation during GNSS outages.
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