Inertial dead reckoning error suppression is key to improving the performance of inte-grated navigation and positioning in complex scenarios.Most existing motion constraint or system error high-order modeling methods rely on kinematic models and sensor error models,with optimal model parameter solutions determined empirically.Deep learning implicit models can un-cover implicit relationships within data,autonomously optimizing parameters,and offering advan-tages in enhancing the accuracy of inertial navigation error modeling.The article summarizes the advantages and disadvantages of the existing mainstream network model design,and by comparing different input and output schemes for preference,a set of lightweight neural network self-learning model for inertial dead reckoning error suppression is finally constructed using convolutional neural network.The model's validity is verified using measured vehicle data.Experimental results dem-onstrate that the network model speed constraint algorithm has certain improvements compared with inertial dead reckoning and traditional non-holonomic constraint(NHC)algorithm.Specific-ally,when the GNSS signal is lost for 300 seconds in Section Ⅰ and 285 seconds in Section Ⅱ,in-tegrating NHC with the network model's speed constraints enhances horizontal positioning accuracy by 41.7％to 47.4％and 26.7％to 36.6％,respectively,effectively suppressing inertial dead reckoning errors.
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