Satellite mission datasets increase in size as their life span grows and the resolutions of the instruments increase. Accurately projecting antenna-based satellite measurements on a geographical grid while maintaining...
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ISBN:
(纸本)9798350360332;9798350360325
Satellite mission datasets increase in size as their life span grows and the resolutions of the instruments increase. Accurately projecting antenna-based satellite measurements on a geographical grid while maintaining reasonable computational time and costs can be challenging. It is thus necessary to include big data algorithms and dedicated management techniques in the processing of such datasets. In this regard, the optimization of the number of interpolations and projections is one of the key aspects, as the native errors of the measurements propagate at each processing step. Besides each interpolation and projection implies a non-negligible increase in total computational time. This work is based on the Sea Surface Salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission, but it could be easily extended to any other satellite mission where individual values for each measurement are retrieved. We propose a redefinition of the complete processor chain so it can work with the measurements within the instrument coordinate system. This allows us to avoid projectionrelated errors during the generation of the final product. Additionally, we introduce a novel algorithm to project those measurements taking into account the actual spatial extent of the acquisitions instead of taking them as points, so measures are averaged weighted by the area they cover on the Earth-based grid. This method is optimized to transform 2D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. Our algorithm has demonstrated its potential when incorporated into the SMOS SSS processor at the Barcelona Expert Center (BEC), allowing us to keep a final resolution very close to the one attained at the antenna coordinate system.
Diffusion affine projection algorithms have the ability to de-correlate the input signal and have faster convergence but with the expense of increased computational complexity. Moreover, traditional diffusion affine p...
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Diffusion affine projection algorithms have the ability to de-correlate the input signal and have faster convergence but with the expense of increased computational complexity. Moreover, traditional diffusion affine projection algorithms consider the noise to be of Gaussian nature. However, practically this noise can be non-Gaussian which can significantly deteriorate the convergence of the algorithms. To mitigate this issue in this brief, we propose two robust affine projection algorithms based on the generalized maximum correntropy criterion (d-A-GMCC) and the logarithmic hyperbolic cosine cost function (d-A-lncosh). To reduce the computational expense of the proposed algorithms, we propose dichotomous coordinate descent based d-A-GMCC and d-A-lncosh algorithms. Extensive simulation study for different Gaussian and non-Gaussian noise environments shows the improved estimation ability of proposed algorithms.
To optimize the path planning of a six-degree-of-freedom robotic arm in complex environments, this paper proposes a multi-objective optimization method that integrates the gradient projection method and the RRT* algor...
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ISBN:
(数字)9798350377255
ISBN:
(纸本)9798350377262
To optimize the path planning of a six-degree-of-freedom robotic arm in complex environments, this paper proposes a multi-objective optimization method that integrates the gradient projection method and the RRT* algorithm. Based on the D-H parameter method, the kinematic model of the robotic arm is constructed. The gradient projection method is employed to optimize joint trajectories for energy efficiency, while the RRT* algorithm is used to solve the obstacle avoidance problem. On this basis, a multi-objective path planning model is established, aiming to minimize end-effector error and energy consumption. MATLAB simulations verify the effectiveness of the proposed method. Experimental results demonstrate that the method excels in path smoothness, energy optimization, and obstacle avoidance, providing theoretical and technical support for the application of robotic arms in industrial and service fields.
In this paper, we propose an alternated inertial subgradient extragradient algorithm for variational inequalities with self-adaptive step-sizes and obtain weak and linear convergence results. We also obtain linear con...
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In this paper, we propose an alternated inertial subgradient extragradient algorithm for variational inequalities with self-adaptive step-sizes and obtain weak and linear convergence results. We also obtain linear convergence results using an alternated inertial projected gradient algorithm for which knowledge of the modulus of strong pseudomonotonicity and Lipschitz constant of the cost function are not needed. We compare numerically our algorithms with other projection-type algorithms in the literature. (C) 2022 Elsevier B.V. All rights reserved.
This paper deals with the convex feasibility problem where the feasible set is given as the intersection of a (possibly infinite) number of closed convex sets. We assume that each set is specified algebraically as a c...
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ISBN:
(数字)9781728113982
ISBN:
(纸本)9781728113999
This paper deals with the convex feasibility problem where the feasible set is given as the intersection of a (possibly infinite) number of closed convex sets. We assume that each set is specified algebraically as a convex inequality, where the associated convex function may be even non-differentiable. We present and analyze a random minibatch projection algorithm using special subgradient iterations for solving the convex feasibility problem described by the functional constraints. The updates are performed based on parallel random observations of several constraint components. For this minibatch method we derive asymptotic convergence results and, under some linear regularity condition for the functional constraints, we prove linear convergence rate. We also derive conditions under which the rate depends explicitly on the minibatch size. To the best of our knowledge, this work is the first proving that random minibatch subgradient based projection updates have a better complexity than their single-sample variants.
Sparsity is an inherent feature of certain practical systems and appears in problems such as channel equalization and echo cancellation. Designed for exploiting the intrinsic structure of sparse environments, while al...
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Sparsity is an inherent feature of certain practical systems and appears in problems such as channel equalization and echo cancellation. Designed for exploiting the intrinsic structure of sparse environments, while also taking advantage of the data reuse and selection strategies, the set-membership proportionate affine projection algorithm (SM-PAPA) relies on the choice of a constraint vector (CV) that affects the behavior of the adaptive system. Although the selection of this CV has been based on some heuristics, a recent work proposes an optimal CV for the set-membership affine projection algorithm, a particular instance of the SM-PAPA. This paper adopts a convex optimization framework and generalizes the optimal CV concept for the SM-PAPA, allowing its use in sparse systems. Moreover, by using the gradient projection method for solving the related constrained convex problem, this paper demonstrates that the optimal CV can indeed be applied in real-time applications.
In this paper, two new kernel adaptive algorithms are proposed. An approximation is used in order to derive the pseudo kernel affine projection algorithm and the pseudo kernel proportionate affine projection algorithm...
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ISBN:
(纸本)9781509067497
In this paper, two new kernel adaptive algorithms are proposed. An approximation is used in order to derive the pseudo kernel affine projection algorithm and the pseudo kernel proportionate affine projection algorithm, respectively. The computational efficiency and performance of the proposed algorithms is verified for a nonlinear system identification application.
In this paper two block-sparse approximated memory improved proportionate affine projection algorithm are proposed for block sparse system identification. An approximation is used for a recently proposed family of blo...
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In this paper two block-sparse approximated memory improved proportionate affine projection algorithm are proposed for block sparse system identification. An approximation is used for a recently proposed family of block-sparse proportionate affine projection algorithms. It is shown that the proposed algorithms have close convergence performance to the original ones and they are less numerically complex. An investigation of the influence of their parameters is also presented.
For intelligent unmanned aerial vehicles working in complex environments, it is necessary to have a certain autonomous flight control decision-making ability to adapt to complex and changeable environments. In order t...
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For intelligent unmanned aerial vehicles working in complex environments, it is necessary to have a certain autonomous flight control decision-making ability to adapt to complex and changeable environments. In order to realize the rapid path planning of intelligent unmanned aerial vehicle in complex flight environment and ensure its accurate positioning, we consider the constraints of error correction and turning radius and so on, and establish a multiobjective optimization model with the shortest path and the least correction times. In addition, a novel projection algorithm is proposed to solve this model. The evaluation of our proposed method is done from a dataset. We clearly show its effectiveness and its superiority compared to several state-of-the art approaches.
The LMS algorithm is widely employed in adaptive systems due to its robustness, simplicity, and reasonable performance. However, it is well known that this algorithm suffers from a slow convergence speed when dealing ...
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The LMS algorithm is widely employed in adaptive systems due to its robustness, simplicity, and reasonable performance. However, it is well known that this algorithm suffers from a slow convergence speed when dealing with colored reference signals. Numerous variants and alternative algorithms have been proposed to address this issue, though all of them entail an increase in computational cost. Among the proposed alternatives, the affine projection algorithm stands out. This algorithm has the peculiarity of starting from $N$ data vectors of the reference signal. It transforms these vectors into as many data vectors suitably normalized in energy and mutually orthogonal. In this work, we propose a version of the LMS algorithm that, similar to the affine projection algorithm, starts from $N$ data vectors of the reference signal but corrects them by using only a scalar factor that functions as a convergence step. Our goal is to align the behavior of this algorithm with the behavior of the affine projection algorithm without significantly increasing the computational cost of the LMS.
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