利用中国区域2023年夏季945个地基全球导航卫星系统(GNSS)测站的观测数据,分别采用双差网解法与精密单点定位法(Precise Point Positioning,PPP)对大气可降水量(Precipitable Water Vapor,PWV)进行了反演,以同址探空站和ERA5再分析资料...利用中国区域2023年夏季945个地基全球导航卫星系统(GNSS)测站的观测数据,分别采用双差网解法与精密单点定位法(Precise Point Positioning,PPP)对大气可降水量(Precipitable Water Vapor,PWV)进行了反演,以同址探空站和ERA5再分析资料的PWV为参考值,研究分析了两种方法在中国不同气候区域反演PWV的精度及稳定性特征。结果表明:与PPP解相比,双差解与探空和ERA5资料的PWV的相关性更强,偏差(Bias)频率分布更集中,峰值区概率更高,偏差范围更小。以探空资料获取的RS-PWV为参考值时,双差解与PPP解的平均Bias分别为-0.1 mm和1.1 mm,平均均方根误差(RMSE)分别为2.4 mm和3.1 mm,以ERA5-PWV为参考值时,双差解与PPP解的平均Bias分别为-0.2 mm和0.1 mm,平均RMSE分别为2.7 mm和3.2 mm,双差解的平均RMSE均小于3 mm,这表明双差网解法反演的PWV具有更高的精度和稳定性。GNSS探测水汽的精度总体表现为西部非季风区优于东部季风区,双差解在各气候区域的RMSE都更集中于中位数附近,而PPP解在不同测站多表现出不同的精度水平,在水汽充足且探测精度偏低的温带和亚热带季风气候区域精度离散程度较大,具有较强的不稳定性。展开更多
In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the mem...In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.展开更多
We are engaged in solving two difficult problems in adaptive control of the large-scale time-variant aerospace system. One is parameter identification of time-variant continuous-time state-space modei; the other is ho...We are engaged in solving two difficult problems in adaptive control of the large-scale time-variant aerospace system. One is parameter identification of time-variant continuous-time state-space modei; the other is how to solve algebraic Riccati equation (ARE) of large order efficiently. In our approach, two neural networks are employed to independently solve both the system identification problem and the ARE associated with the optimal control problem. Thus the identification and the control computation are combined in closed-loop, adaptive, real-time control system . The advantage of this approach is that the neural networks converge to their solutions very quickly and simultaneously.展开更多
基金supported in part by the National Natural Science Foundation of China(No.41876222)。
文摘In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.
文摘We are engaged in solving two difficult problems in adaptive control of the large-scale time-variant aerospace system. One is parameter identification of time-variant continuous-time state-space modei; the other is how to solve algebraic Riccati equation (ARE) of large order efficiently. In our approach, two neural networks are employed to independently solve both the system identification problem and the ARE associated with the optimal control problem. Thus the identification and the control computation are combined in closed-loop, adaptive, real-time control system . The advantage of this approach is that the neural networks converge to their solutions very quickly and simultaneously.
