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Ship navigation via GPS/IMU/LOG integration using adaptive fission particle filter

机译:使用自适应裂变粒子滤波器通过GPS / IMU / LOG集成进行船舶导航

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摘要

To achieve a continuous and accurate navigation solution that avoids interruption, GPS is integrated with dead reckoning techniques such as inertial navigation and speed log. A common approach to obtain a low-cost navigation solution is to utilize a multisensor system consisting of MEMS-based inertial measurement unit (IMU), and the whole system is integrated with GPS. However, these sensors provide significant inherent errors because of their complex error characteristics. Particle filter (PF) is considered as a nonlinear filtering technique to accommodate for arbitrary inertial sensor characteristics and motion dynamics. An adaptive fission PF (AFPF) method is proposed to overcome the shortcoming of sample impoverishment problem and improve particle quality. Moreover, further improvement of the MEMS-based IMU/GPS integration navigation system performance during GPS unavailability is achieved by integrating measurement updates from speed log data. The performance of the proposed GPS/IMU/LOG integration using AFPF is evaluated by sea trial, and results indicate that the AFPF solution outperform three different solutions.
机译:为了实现连续,准确的导航解决方案,避免干扰,GPS与惯性导航和速度记录等航位推算技术集成在一起。获得低成本导航解决方案的常用方法是利用由基于MEMS的惯性测量单元(IMU)组成的多传感器系统,并将整个系统与GPS集成在一起。然而,这些传感器由于其复杂的误差特性而提供了明显的固有误差。粒子滤波器(PF)被认为是一种非线性滤波技术,可适应任意惯性传感器特性和运动动力学。提出了一种自适应裂变PF(AFPF)方法,克服了样品贫乏问题的缺点,提高了颗粒质量。此外,通过集成速度记录数据中的测量更新,可以进一步提高GPS不可用期间基于MEMS的IMU / GPS集成导航系统的性能。通过海上试验评估了使用AFPF提出的GPS / IMU / LOG集成的性能,结果表明AFPF解决方案优于三种不同的解决方案。

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