Abstract
This paper addresses the problem of integration of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) for the purpose of developing a low-cost, robust and highly accurate navigation system for unmanned surface vehicles (USVs). A tightly-coupled integration approach is one of the most promising architectures to fuse the GNSS data with INS measurements. However, the resulting system and measurement models turn out to be nonlinear, and the sensor stochastic measurement errors are non-Gaussian and distributed in a practical system. Particle filter (PF), one of the most theoretical attractive non-linear/non-Gaussian estimation methods, is becoming more and more attractive in navigation applications. However, the large computation burden limits its practical usage. For the purpose of reducing the computational burden without degrading the system estimation accuracy, a quaternion-based adaptive unscented particle filter (AUPF), which combines the adaptive unscented Kalman filter (AUKF) with PF, has been proposed in this paper. The unscented Kalman filter (UKF) is used in the algorithm to improve the proposal distribution and generate a posterior estimates, which specify the PF importance density function for generating particles more intelligently. In addition, the computational complexity of the filter is reduced with the avoidance of the re-sampling step. Furthermore, a residual-based covariance matching technique is used to adapt the measurement error covariance. A trajectory simulator based on a dynamic model of USV is used to test the proposed algorithm. Results show that quaternion-based AUPF can significantly improve the overall navigation accuracy and reliability.
Highlights
Results show that quaternion-based adaptive unscented particle filter (AUPF) can significantly improve the overall navigation accuracy and reliability
The development of unmanned surface vehicles (USVs) for scientific, military and commercial purpose in applications such as oil and gas exploration, oceanographic data collection, hydrographic, oceanographic and environmental survey, mine counter measure, surveillance and reconnaissance, anti-submarine warfare and fast inshore attack craft for combat training require the corresponding development of navigation systems [1]
In order to solve the problem of dimension mismatch, we introduce the rotation vector in the rotation space, which is transformed from the corresponding quaternion vector error
Summary
The development of unmanned surface vehicles (USVs) for scientific, military and commercial purpose in applications such as oil and gas exploration, oceanographic data collection, hydrographic, oceanographic and environmental survey, mine counter measure, surveillance and reconnaissance, anti-submarine warfare and fast inshore attack craft for combat training require the corresponding development of navigation systems [1]. The EKF (extended Kalman filter) is known as state-of-the-art for fusion INS and GNSS data in tightly-coupled integration. This approximation will result in large errors when EKF calculates a posterior mean and covariance, which may lead to suboptimal performance or even divergence of the filter. With the development of the computer technology, particle filter (PF) turns out to be more attractive for nonlinear and non-Gaussian applications, and has been successfully used in [7] to recursively update the posterior distribution by sequential importance sampling and resampling. A quaternion-based GPS/INS integration algorithm using AUPF is proposed. Simulation results are analyzed and compared to illustrate the performance of the proposed AUPF algorithm
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