基于注意力增强的三维速度约束手机GNSS/INS车载导航OA

In urban canyons,tunnels,and other GNSS signal-blocked environments,the positioning performance of smartphones integrated with consumer-grade GNSS chipsets and inertial measurement unit(IMU)degrades dramatically.Although inertial navigation systems(INS)can continuously provide navigation solutions in GNSS-denied environments,the low-cost IMU embedded in smartphones suffer from rapid error divergence due to low accuracy and high noise.Traditional non-holonomic constraint(NHC)methods experience severe failure when zero-value assumptions are violated during vehicle maneuvers such as turning and U-turns,thereby affecting positioning accuracy.To address these issues,this paper proposes an attention-enhanced three-dimensional velocity constraint method for smartphone-based vehicular positioning.The method constructs a hybrid architecture integrating convolutional neural network(CNN),long short-term memory(LSTM)network,and attention mechanism.Taking raw six-axis IMU observations as input,it directly outputs three-dimensional vehicle velocity,functioning as a three-dimensional virtual odometer during GNSS outages to constrain IMU error accumulation.Specifically,CNN captures local IMU features for noise filtering,LSTM models temporal correlations,and the sliding window-based attention mechanism adaptively weights historical information according to current motion states.Experimental results demonstrate that the proposed method achieves a forward velocity prediction root mean square(RMS)error of 0.48 m/s,representing a 40%-55%reduction compared to existing methods.In GNSS-denied scenarios,the average horizontal positioning RMS is 7.2 m and vertical positioning RMS is 2.3 m,representing reductions of 98.3%and 97.0%respectively compared to the unconstrained scheme,and 79.8%and 39.5%respectively compared to the traditional NHC scheme,significantly improving smartphone positioning performance in GNSS-denied environments.