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Hybrid systems are subject to multiple dynamic models, or so-called modes. To estimate
the state, the sequence of modes has to be estimated, which results in an exponential growth of possible sequences. The most prominent solution to handle this is the interacting multiple model filter, which can be extended to smoothing. In this paper, we derive a...
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... mixture of Gaussian approximations are exchanged with the -WeightedSum and -WeightedCov algorithms. The mode-matched smoothing is exchanged with the -EKS from Section 4. This results in the -RTSIMM smoother (-RTSIMMS), as shown in Table 2. ...Similar publications
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In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estim...
Citations
... Another popular application is graph optimization [25], which is particularly relevant in the context of graph-based simultaneous localization and mapping [26]. Most recently, [27] utilized the ⊞-method for applying the interacting multiple model filter and smoother to manifold spaces. ...
... Calculating the Kalman gain K t requires an expensive matrix inversion in the UKF. In the SRUKF, it is instead computed in (27) using two nested inverse solutions of K t (S z t S z t ⊤ ) = Σ x,z t , which can be implemented using efficient back-substitutions. As in [5], we use x = b/A to denote solving Ax = b for x. ...
... For example, the generation of sigma points requires a Cholesky decomposition of complexity O(n 3 /6) in the UKF, while in the SRUKF in (16), (21), (32), and (37) no extra computations are required since the Cholesky factor is already given. Furthermore, the back-substitutions for calculating the Kalman gain in (27) and (43) of the SRUKFs are more efficient than the matrix inverse needed in the UKFs. ...
Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands.
... A single dynamic model in estimating motion parameters cannot accurately describe all of the unpredictable maneuvering behaviors of an underwater passive vehicle. The performance of motion estimation techniques within a single model may be significantly affected if the state equation and the actual movements of the target do not match [7]. The most comprehensive and effective motion prediction models are those which calculate the statistical equations of object kinematics at each moment of the turning path [8]. ...
This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF).
... Multimodal systems based on tactile and kinesthetic fusion feedback have emerged to display more realistic haptic feedback for operators in virtual environments [24]. Multimodal fusion technology refers to the integration and fusion of information from different sensors and modalities to improve the performance and effectiveness of the system, including sensor fusion, data fusion, and information fusion [25][26][27]. Luo Shan et al. [28] presented a method called Iterative Closest Labeled Point (iCLAP) to link kinesthetic and tactile modalities to achieve integrated perception of the touched object. Fan Liqiang et al. [29] presented a multimodal haptic fusion method of cable-drive and ultrasonic haptics that can generate multimodal haptic stimuli. ...
This paper proposes a kinesthetic–tactile fusion feedback system based on virtual interaction. Combining the results of human fingertip deformation characteristics analysis and an upper limb motion mechanism, a fingertip tactile feedback device and an arm kinesthetic feedback device are designed and analyzed for blind instructors. In order to verify the effectiveness of the method, virtual touch experiments are established through the mapping relationship between the master–slave and virtual end. The results showed that the average recognition rate of virtual objects is 79.58%, and the recognition speed is improved by 41.9% compared with the one without force feedback, indicating that the kinesthetic–tactile feedback device can provide more haptic perception information in virtual feedback and improve the recognition rate of haptic perception.
This paper presents an extension to the original Frenet-Serret and Bishop frame target models used in the invariant extended Kalman filter (IEKF) to account for tangential accelerations for highly-manoeuvrable targets. State error propagation matrices are derived for both IEKFs and used to build the accelerating Frenet-Serret (FSa-LIEKF) and Bishop (Ba-LIEKF) algorithms. The filters are compared to the original Frenet-Serret and Bishop algorithms in a tracking scenario featuring a target performing a series of complex manoeuvres. The accelerating forms of the LIEKF are shown to improve velocity estimation during non-constant velocity trajectory segments at the expense of increased noise during simpler manoeuvres.
