排序方式: 共有67条查询结果,搜索用时 828 毫秒
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针对惯性/卫星组合导航系统易受干扰或自主性不足等问题,引入偏振光传感器和光流传感器,分别建立航向角和速度量测方程,以辅助惯性导航系统,提出了一种基于惯性/偏振光/光流的自主导航方法。同时,为实现惯性传感器、偏振光传感器和光流传感器等多传感器的融合,设计了无迹Kalman滤波器。为验证该方法的有效性,以六足步行机器人为对象开展仿真和实验验证。结果表明,在没有卫星信号源的情况下,仅依靠机器人自身感知,可实现较高精度的机器人位姿估计,实现了不依赖于卫星导航信号的自主导航,提升了导航系统的自主性。 相似文献
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针对传统目标跟踪算法鲁棒性较差等问题,提出了一种基于当前统计模型的无迹卡尔曼滤波交互式多模型(IMM-CS-UKF)融合算法。在交互式多模型算法框架内,计算当前统计模型的概率,提高了统计模型的目标加速度和自适应性。该算法结合了交互式多模型和无迹卡尔曼滤波算法,具有对不同机动模式目标的自适应跟踪能力和精度高等优点。仿真结果表明,该算法对以多种机动策略实时机动的目标具有较好的跟踪性能。 相似文献
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研究了低载噪比与高动态环境下的深空测控系统频率估计算法,在分析已有方法不足的基础上,提出了一种基于无迹卡尔曼滤波(UKF)的闭环载波跟踪方法。此方法结合了锁频环鉴别器和UKF的优点,获得了宽的估计范围,高的估计精度和低的载噪比门限。在分析UKF模型的基础上,此方法还减少了原有UKF算法的运算量。仿真过程模拟了接收机的高动态运动轨迹,结果表明此法具有较好的动态适应能力、收敛性能和跟踪精度,能够有效地完成低载噪比与高动态环境下的频率估计。此法与基于扩展卡尔曼滤波(EKF)的频率估计算法相比,具有更低的频率估计误差,因此有着良好的应用前景。 相似文献
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The adaptive Gaussian mixtures unscented Kalman filter for attitude determination using light curves
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(6):2609-2628
The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaussian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kalman Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non-linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can converge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state. 相似文献