Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. We also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, an interferometric radar altimeter (IRA) is used to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real-time on board. Finally, the performance of the proposed system is verified through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

In this paper, we present recent work on the g-sensitivity error of the MEMS vibratory gyroscope. Generally, the g-sensitivity error has been ignored in the use of commercial MEMS vibratory gyroscopes, but it deserves our attention if we are to achieve tactical grade performance for military applications. First, we mathematically show the reason the g-sensitivity error occurs as an additional scale-factor error during the use of MEMS vibratory gyroscopes. Then, we estimate the g-sensitivity error using FEM simulation and verify it by experiment using a centrifugal machine. Consequently, we propose a compensation model to accommodate the g-sensitivity error of a gyroscope and confirm the theoretical prediction with experimental results.

The authors propose an innovative solution to address challenges in terrain‐referenced navigation (TRN). The suggested solution is the interactive multiple‐model particle filter with a classification error minimisation strategy (IMM‐CPF) based on decision theory. TRN is a technique that estimates position by comparing measured terrain altitude to the digital elevation model and critically depends on obtaining accurate altitude measurements. However, these measurements can be easily contaminated to not only from sensor errors but also from vegetation effects. The TRN measurement noise model is characterised as a multi‐modal density, and it reveals an overlap between two density functions, with the mixture weight parameter varying based on surface environmental conditions. This variability can potentially degrade estimation accuracy. The proposed approach integrates truncated likelihoods into the mode estimation process to enhance mode estimation capability using a classification error minimisation strategy. The proposed strategy is based on decision theory and has been modified to be suited in the IMMPF form. The effectiveness of the proposed IMM‐CPF method is verified through simulations conducted under diverse surface conditions, demonstrating significant improvements in estimation accuracy compared to conventional algorithms. Furthermore, the significance of this method is presented in terms of computational cost and robustness.

Although interferometric radar altimeter (IRA) provides accurate three‐dimensional terrain information, it is difficult to eliminate some IRA uncertainties by signal processing. This calls for a filter that possesses strong robustness against outliers. An auxiliary particle filter (APF), which can be used to perform terrain referenced navigation using IRA technology as an alternative to an integrated navigation system using a global navigation satellite system, can mitigate the effect of outliers by introducing auxiliary variables. To improve APF robustness, the authors propose an immune APF, which introduces an artificial immune system to enhance outlier performance by replacing inferior particles with superior particles. The mutation process is applied to obtain particle diversity using a regularised kernel. Monte Carlo simulation results verify the robustness of the proposed filter.

In this paper, we proposed two methods for reducing the pyro-shock of the MEMS Inertial Measurement Unit (IMU). First, we designed the vibration isolator for reducing the pyro-shock inside the IMU. However, it turned out that there is a limit to reducing the pyro-shock with only the vibration isolator. Therefore, we improved the pyro-shock reduction performance by changing the method of mounting on the flight vehicle. Four mounting options were tested and one of them was adopted. The results showed the best reduction performance when we designed the vibration isolator with an aluminum integrated structure. When mounting, two methods were applied. One was to insert a bracket with a different material between the mounting surface and IMU and the other was to insert a set of three washers that was stacked in a PEEK-metal-PEEK order at each part of the screw connections.