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For more accurate and reliable aviation navigation systems which can be used for civil and military aircraft or missiles, researchers have employed various filtering methods to reduce the measurement noise level, or to integrate sensors such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Most GNSS applications including Differential GNSS assume that the GNSS measurement error follows a Gaussian distribution, but this is not true. Therefore, we propose an integrity monitoring method using particle filters assuming non-Gaussian measurement error. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian error. Since the Kalman filters presume that measurement error follows a Gaussian distribution, they use an overbounded standard deviation to represent the measurement error distribution, and since the overbound standard deviations are too conservative compared to actual deviations, this degrades the integrity monitoring performance of the filters. A simulation was performed to show the improvement in performance provided by our proposed particle filter method, which does not use sigma overbounding. The results show that our method can detect about 20% smaller measurement biases and reduce the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual error model instead of overbounding, or to improve the overbounding methods.

A Particle Filter Approach to DGNSS Integrity Monitoring —Consideration of Non-Gaussian Error Distribution—