An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas.

Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network's output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.

Research on the True Triaxial Mechanical Properties of Concrete under the Coupling Action of High Temperature and Biaxial Unequal Lateral Pressure.

Based on engineering practice and practical needs, this paper takes ordinary concrete specimens as the research object, and adopts a high-temperature true triaxial loading test system to carry out high-temperature uniaxial and true triaxial static compression tests of concrete under high-temperature conditions. By comparing with normal temperature conditions, this paper analyzes the influence of the coupling effect of high-temperature and biaxial unequal lateral pressure on the static mechanical properties of concrete. By analyzing the experimental data, we reached a series of conclusions and carried out theoretical research on this basis. High temperatures can significantly affect the uniaxial static pressure strength characteristics, deformation characteristics, and failure mode of concrete. When the temperature exceeds 400 °C, the compressive strength decreases significantly, the peak strain increases sharply, and the plasticity of concrete is further enhanced. The coupling effect of high-temperature deterioration and lateral pressure strengthening makes the true triaxial mechanical properties of concrete change significantly; 0.6:0.2 and 400 °C are the turning points of side pressure ratio and temperature that affect the change law of the true triaxial mechanical properties of concrete, respectively. Based on the study of the high-temperature deterioration factor and lateral pressure strengthening factor, this paper further puts forward a concrete strength formula under the coupling action of high temperature and biaxial unequal lateral pressure. It was verified that the formula has a high accuracy.