An algorithm namely the TDOA-Camberra is proposed for the multi-target passive location in this paper. The algorithm uses the Camberra distance to associate the target data and do the optimal search, then uses the tim...
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ISBN:
(纸本)9781467355339
An algorithm namely the TDOA-Camberra is proposed for the multi-target passive location in this paper. The algorithm uses the Camberra distance to associate the target data and do the optimal search, then uses the time difference of arrival (TDOA) to locate multiple targets passively once data from the same target is determined. At the same time, as the computing time of associating data using Camberra distance is long, the improved sequential similarity detection algorithm (SSDA) is added to the calculation in order to shorten the time of the data association, thus to increase the speed of the multi-target passive location. The theoretical analysis and simulation experiment proves that the multi-target passive location can be implemented by the proposed algorithm accurately and quickly.
With the increase of the capacity of PV generated systems, how to eliminate the problem caused by the randomness of power output for photovoltaic system becomes more significant. Most of the existing photovoltaic pred...
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Vehicle Detection is an important part in intelligent transportation system (ITS) and driver assistance system. Considering vehicles have strong edges and lines in different orientation and scales, in this paper, we p...
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作者：
Liang, HailiSu, HoushengWang, XiaofanChen, Michael Z.Q.[a] Department of Automation
Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China[b] School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China[c] Department of Mechanical Engineering The University of Hong Kong Hong Kong
This paper investigates the problem of swarm aggregations of heterogeneous multi-agent systems. Comparing with the existing studies on swarm aggregations of homogeneous multi-agent systems, this paper is much more res...
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This paper investigates the problem of swarm aggregations of heterogeneous multi-agent systems. Comparing with the existing studies on swarm aggregations of homogeneous multi-agent systems, this paper is much more resembling the practical situations, where the agents have different dynamics. We show that the heterogeneous agents will gather with a certain error under some assumptions and conditions. The stability properties have been proven by theoretical analysis and verified via numerical simulation. The stability of the heterogeneous multi-agent systems has been achieved based on matrix theory and the Lyapunov stability theorem. Numerical simulation is given to demonstrate the effectiveness of the theoretical result. [PUBLICATION ABSTRACT]
To make humanoid robots walking fast, it's important to improve driving force of their leg joints. Usually, each joint of humanoid robots is driven by a single motor. Dual-motor joint, on the other hand, is one of...
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ISBN:
(纸本)9781467355339
To make humanoid robots walking fast, it's important to improve driving force of their leg joints. Usually, each joint of humanoid robots is driven by a single motor. Dual-motor joint, on the other hand, is one of the candidate solutions to meet the power requirement needed for fast walking. This paper proposed a new dual-motor control model. In the model, two motors are treated as a single control plant instead of two parallel control plants. With the usage of current distributor, the control model can pump different current to each motor freely so as to eliminate the unbalance of the load imposed on each motor. Simulation and experiment show that the proposed model works well under high joint load and it can be used on a fast walking humanoid robot.
For UGV, driving in the unstructured environment safely and quickly without human intervention becomes increasingly important. While many scholars have conducted researches in driving in this case, the results seem qu...
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One key technology for a humanoid robot is to sense the environment accurately and control the movement of the robot in realtime. This paper addresses this problem and focuses on the visual servoing control of an anth...
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The multipath estimation of global navigation satellite system (GNSS) signal is actually the state estimation of nonlinear/non-Gaussian systems. The extension of sliced Gaussian mixture filter (ESGMF) based on Gaussia...
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The multipath estimation of global navigation satellite system (GNSS) signal is actually the state estimation of nonlinear/non-Gaussian systems. The extension of sliced Gaussian mixture filter (ESGMF) based on Gaussian sum approximation is proposed for the state estimation of nonlinear/non-Gaussian state space, and the probability density function (PDF) expression of states is derived recursively for a time varying system. Resampling is applied to the prediction PDF to reduce the complexity of Bayesian inference. The simulation result of multipath estimation with ESGMF shows that the ESGMF algorithm performs better in accuracy than the algorithms based on particle filter (PF) and extended Kalman filter (EKF).
To obtain and update the kernel-bandwidth,we present an adaptive bandwidth obtainment algorithm based on object contour extraction from optical-flow *** combination of modified mountain cluster approach and fast scann...
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ISBN:
(纸本)9781479900305
To obtain and update the kernel-bandwidth,we present an adaptive bandwidth obtainment algorithm based on object contour extraction from optical-flow *** combination of modified mountain cluster approach and fast scanning window contour extractor guarantees the speed of this algorithm.A novel ellipse detection method based on a modified RANSAC is adopted to reduce the *** results demonstrate that the algorithm select the proper size of tracking kernel-bandwidth with minor extra computational overhead and keep up with the object robustly when the scale changed rapidly.
This article proposes a multi-objective decomposition stochastic particle swarm optimization (MDSPSO) algorithm. In MDSPSO, every particle has a weighted vector constantly. Then, an improved Tchebycheff decomposition ...
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This article proposes a multi-objective decomposition stochastic particle swarm optimization (MDSPSO) algorithm. In MDSPSO, every particle has a weighted vector constantly. Then, an improved Tchebycheff decomposition method is applied to decompose the multi-objective problem into some single-objective problems. The reference position of every particle is uniformly generated in the zone with the center which is the geometrical center of its current position, the best previous reference position as well as the swarm best reference position. The radius of this zone is the distance from the center to its current position. Then the particle is updated to the new position according to the reference position and its current velocity. The comparisons with the decomposition-based multi-objective particle swarm optimizer (dMOPSO), a multiobjective evolutionary algorithm based on decomposition (MOEA/D), and nondominated sorting genetic algorithm II (NSGA-II) show that the solutions of MDSPSO can be dominated at least with the best diversity. To reduce the computational time by finite element analysis for optimizing the structure parameters of linear motor, artificial neural network is used as the model to evaluate the performance. Finally, MDSPSO is applied to optimize four objectives simultaneously. The practical result is shown that the optimized linear motor has an increased thrust, improved efficiency, reduced fluctuation and manufacturing cost.
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