Zobrazeno 1 - 10
of 134 333
pro vyhledávání: '"P, KALMAN"'
Autor:
Sanz-Alonso, Daniel, Waniorek, Nathan
Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state is high dimensional, ensemble Kalman filters are often the method of choice. This paper establishes
Externí odkaz:
http://arxiv.org/abs/2412.14318
This paper presents an invariant Kalman filter for estimating the relative trajectories between two dynamic systems. Invariant Kalman filters formulate the estimation error in terms of the group operation, ensuring that the error state does not depen
Externí odkaz:
http://arxiv.org/abs/2412.10519
We investigate the relations between the Kalman Theorem and the Chow-Rashevskji Theorem or, more precisely, the general theory of flows tangent to non-integrable distributions. The main results consist of two proofs of the Kalman Theorem, which are a
Externí odkaz:
http://arxiv.org/abs/2412.07438
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and relative pose m
Externí odkaz:
http://arxiv.org/abs/2411.19033
The problem of incorporating information from observations received serially in time is widespread in the field of uncertainty quantification. Within a probabilistic framework, such problems can be addressed using standard filtering techniques. Howev
Externí odkaz:
http://arxiv.org/abs/2411.18864
Neural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models heavily depends on the training data obtained by costly electronic struct
Externí odkaz:
http://arxiv.org/abs/2411.13850
We investigate a monostatic orthogonal frequency-division multiplexing (OFDM)-based joint communication and sensing (JCAS) system for object tracking. Our setup consists of a transmitter and receiver equipped with an antenna array for fully digital b
Externí odkaz:
http://arxiv.org/abs/2411.12464
Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehens
Externí odkaz:
http://arxiv.org/abs/2411.11483
In this paper, we derive a new Kalman filter with probabilistic data association between measurements and states. We formulate a variational inference problem to approximate the posterior density of the state conditioned on the measurement data. We v
Externí odkaz:
http://arxiv.org/abs/2411.06378
This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent
Externí odkaz:
http://arxiv.org/abs/2411.05847