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The key to promoting urbanization in China is to achieve urban integration of migrant peasant workers, and basic public health services may have a potential social impact on the subjective welfare of migrant peasant workers. In this paper, we use data from the China Migrants Dynamic Survey (CMDS) to study the impact of basic public health services on the urban integration of migrant peasant workers. The results show that basic public health services can significantly improve the probability of migrant peasant workers’ urban integration. The conclusions remain robust after mitigating potential endogeneity issues using the propensity score matching method (PSM), the CMP method, and robustness testing by replacing the explanatory variables with the reduced tail treatment. Further research found that the impact of basic public health services on migrant peasant workers’ urban integration is heterogeneous. The urban integration effect of basic public health services is more significant in male, high school and above, migrant peasant workers’ groups. The urban integration effect of basic public health services gradually increases with upgrading of the urban grade at the second-tier city and higher urban levels, and it also plays a vital role in the urban integration of the migrant peasant workers at the third-tier city and lower urban levels. Our findings also provide an evidence-based policy for China to promote equalizing basic public health services and a path to the urbanization of migrant peasant workers.

In order to solve the problem that the spherical harmonic model has a large amount of calculation, especially in the background of aviation navigation, it is difficult to realize real-time gravity compensation, a gravity compensation method based on back-propagation neural network (BPNN) is proposed. Firstly, the highest order/degree spherical harmonic model EIGEN-6C4 is used to calculate the gravity vector information in the planning navigation area, which is regarded as the true value. The gravity disturbance information and the corresponding position information are used as the training data set of BPNN. Then, the gravity model based on BPNN is obtained by training, and the model is applied to the gravity compensation of INS. Finally, the effectiveness of the method is verified by experiments. The numerical simulation results show that the standard deviations of east-west, north-south gravity disturbances errors and gravity anomaly errors are 0.29 mGal, 0.35 mGal and 0.81 mGal, respectively. The vehicle experiment results show that the horizontal radial position performance of INS after gravity compensation is improved by more than 26.68%, which is basically consistent with the gravity compensation using the highest order/degree spherical harmonic model, and ensures the real-time performance of the algorithm.

In order to further understand and master the positioning performance of UWB system and enrich the application scenarios of UWB system, the positioning performance for it under the metal NLOS environment is studied. First, we designed 3 different experimental scenarios (LOS, NLOS-lcm and NLOS-2cm), and compared the influence of different scenarios on the positioning performance of UWB system. Then, the positioning is divided into 3 base stations and 4 base stations two modes. Under the metal NLOS environment, the influence of the number of base stations on the positioning performance of the UWB system is analyzed. The experimental results show that: Compared with the LOS environment, the ranging error in metal NLOS environment is increased by nearly 2 times, and the shielding effect of aluminum plate leads to obvious NLOS error. Comparing and analyzing the NLOS-lcm and NLOS-2cm scenes, the effect of the aluminum plate with thickness of lcm and 2cm on the positioning performance of UWB system is basically the same, whether from distance measurement or location calculation. Comparing the two location modes of 3 base station and 4 base station. When one of the base stations is disturbed by NLOS, the location result of the 3 base station is seriously deteriorated, and the 4 base station positioning has a good ability to resist NLOS error. Therefore, increasing the number of base stations can improve the anti NLOS performance of the UWB system.

In view of the complexity and variability of bathymetric data, the paper introduces a new algorithm named DAE-WGAN to construct sea bottom trend surface. This new model is an alternative to traditional GAN training method, combined with the advantages of Denoising Autoencoder (DAE) and Wasserstein Generative Adversarial Network (WGAN). Firstly, the network structure is introduced in detail, in which the critic (or ‘discriminator’) estimates the Wasserstein-1 distance between the generated-sample distributions and the real-sample distributions, and optimizes the generator to approximate the minimum Wasserstein-1 distance, which effectively improves the stability of the adversarial training. Moreover, the generalized Denoising Autoencoder algorithm is added to train the back-propagation process, having a better ability of dimensionality reduction, which improves the robustness of the whole algorithm. Then, using two different types of bathymetric data (seabed tiny-terrain data and Electronic Nautical Chart data), we had long-time experiments to train the DAE-WGAN till optimality, and got the better sea bottom trend surface. Finally, by comparison with other GAN models (such as InFoGAN, LSGAN), the results show that the proposed method has an obvious improvement in accuracy, stability and robustness, and further illustrate the feasibility of this method in bathymetric precise data processing area.

According to the characteristics of the random errors of MIMU, an Allan variance analysis method fused with genetic algorithm is proposed, which can effectively evaluate the different random errors. Firstly, how to analyze and identify the errors of inertial devices by Allan variance analysis method is introduced in detail. Then, according to the characteristics of genetic algorithm that can achieve global optimum, an Allan variance analysis method fused with genetic algorithm is proposed. Finally, by long-time experiment to test the MEMS inertial devices of three different manufacturers, the measured data of gyro and accelerometer are processed and compared respectively, and the numerical results of each random error have been calculated, which proved the validity of the method. This method combines genetic algorithm with Allan variance analysis method, providing a new idea for the theoretical study of random error field in MEMS inertial devices.