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集中交互式多传感器联合概率数据互联算法 总被引:2,自引:1,他引:2
为了解决杂波环境下多传感器多机动目标跟踪问题,本文提出了一种集中交互式多传感器联合概率数据互联算法。本文提出的算法首先应用广义S-D分配的规则对每个传感器送来的观测数据进行排列组合,并对所有的测量组合进行有效性判断,然后应用数据压缩的方法将每个有效量测组合压缩成一个等效量测点并根据每个等效量测点的联合似然函数计算其联合互联概率,最后在此基础上应用交互式多模型算法的思想以处理目标出现机动的问题。本文最后给出了该算法的分析,仿真结果表明,本文算法能够很好地解决杂波环境下多传感器多机动目标的跟踪问题。 相似文献
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Abstract The multiple‐target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions. 相似文献
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多红外传感器观测系统跟踪精度分析 总被引:1,自引:0,他引:1
针对多红外传感器观测系统的配置问题,本文首先采用扩展卡尔曼滤波与集中式融合相结合的算法进行被动目标跟踪,给出了算法的克拉美-劳下限,在此基础上分析比较了防区内对不同目标航迹的跟踪性能,给出了跟踪精度的几何分布,分析了系统参数对跟踪精度的影响,给出了提高系统性能的措施. 相似文献
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This paper aims to find a reliable, collision-free path in a dynamic environment for highly maneuverable unmanned combat air vehicles (UCAVs). Given the real-time nature of the operational scenario, quick and adaptable reactions of UCAVs are necessary for updates in situational awareness. Therefore, we propose a three dimensional (3D) path planning approach based on the situational space to provide the tactical requirements of UCAVs for tracking targets and avoiding collisions. First, to ensure reliable nonlinear measurements, the interacting multiple model (IMM) algorithm based on a cubature Kalman filter (CKF) is chosen for the tracking and prediction algorithm. A constraint reference frame combining the kinematic model of constant acceleration (CA) is developed to solve the problem of arrival point generation. Second, by analyzing the relative motion between the UCAV and the moving objects, we define the situation space and give the corresponding calculation method. In tracking the moving target, the guidance vector contains the fusion information of displacement and velocity. At the same time, taking advantage of the one-step situation space as the judgment of the threat, we further plan the collision avoidance strategy. Third, as the safety in a practically reachable trajectory of the UCAV possesses the absolute priority, the collision avoidance acceleration accounts for this dominant factor in path planning. Simulations and experimental results prove that the proposed approach can plan a smooth and flyable path in 0.008 s under the premise of soft-landing target tracking. 相似文献
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G. Collazuol G. Lamanna J. PinzinoM.S. Sozzi 《Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment》2012,662(1):49-54
We discuss an approach for using commercial graphic processors (GPUs) at the earliest trigger stages in high-energy physics experiments, and study its implementation on a real trigger system in preparation. Latency and processing rate measurements on several state-of-the-art devices are presented, and potential issues related to processing time jitter and data transfer throughput are discussed. GPUs might act as the missing link to allow present implementations of large DAQ systems to be entirely based on commodity devices. 相似文献
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Distributed data fusion algorithms for inertial network systems 总被引:1,自引:0,他引:1
New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and new distributed data fusion algorithms are developed for inertial network systems. The distributed data fusion algorithm comprises two steps: inertial measurement fusion and state fusion. The inertial measurement fusion allows each node to assimilate all the inertial measurements from an inertial network system, which can improve the performance of inertial sensor failure detection and isolation algorithms by providing more information. The state fusion further increases the accuracy and enhances the integrity of the local inertial states and navigation state estimates. The simulation results show that the two-step fusion procedure overcomes the disadvantages of traditional inertial sensor alignment procedures. The slave inertial nodes can be accurately aligned to the master node. 相似文献
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Bayesian and Dempster-Shafer fusion 总被引:3,自引:0,他引:3
The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can
be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory,
using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion
equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction
integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory,
which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although
these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer
theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative
merits and demerits using an illustrative example. 相似文献
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This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time. 相似文献
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Varvara G. Asouti 《工程优选》2013,45(3):241-257
A parallel asynchronous evolutionary algorithm controlled by strongly interacting demes for single- and multi-objective optimization problems is proposed. It is suitable even for non-homogeneous, multiprocessor systems, ensuring maximum exploitation of the available processors. The search algorithm utilizes a structured topology of evaluation agents organized in a number of inter-communicating demes arranged on a 2D supporting mesh. Once an evaluation terminates and a processor becomes idle, a series of intra- and inter-deme processes determines the next agent to undergo evaluation on this specific processor. Real coding and differential evolution operators are used. Mathematical and aerodynamic-turbomachinery optimization problems are presented to assess the proposed method in terms of CPU cost, parallel efficiency and quality of solutions obtained within a predefined number of evaluations. Comparisons with conventional evolutionary algorithms, parallelized based on the master–slave model on the same computational platform, are presented. 相似文献
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A parallel implementation for the finite volume method (FVM) for three-dimensional (3D) viscoelastic flows is developed on
a distributed computing environment through Parallel Virtual Machine (PVM). The numerical procedure is based on the SIMPLEST
algorithm using a staggered FVM discretization in Cartesian coordinates. The final discretized algebraic equations are solved
with the TDMA method. The parallelisation of the program is implemented by a domain decomposition strategy, with a master/slave
style programming paradigm, and a message passing through PVM. A load balancing strategy is proposed to reduce the communications
between processors. The three-dimensional viscoelastic flow in a rectangular duct is computed with this program. The modified
Phan-Thien–Tanner (MPTT) constitutive model is employed for the equation system closure. Computing results are validated on
the secondary flow problem due to non-zero second normal stress difference N
2. Three sets of meshes are used, and the effect of domain decomposition strategies on the performance is discussed. It is
found that parallel efficiency is strongly dependent on the grid size and the number of processors for a given block number.
The convergence rate as well as the total efficiency of domain decomposition depends upon the flow problem and the boundary
conditions. The parallel efficiency increases with increasing problem size for given block number. Comparing to two-dimensional
flow problems, 3D parallelized algorithm has a lower efficiency owing to largely overlapped block interfaces, but the parallel
algorithm is indeed a powerful means for large scale flow simulations.
Received: 2 July 2002 / Accepted: 15 November 2002
This research is supported by an A⋆STAR Grant EMT/00/011. 相似文献
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In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship. 相似文献
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Theory and implementation of NDT data fusion 总被引:3,自引:0,他引:3
Scientific measurements from single or multiple sensors are usually incomplete and uncertain. A process making use of the concept of data fusion has been developed to try to encompass this problem by combining information from multiple sensors. The objective to synergistic use of information from multiple sources is to reduce uncertainty and increase the confidence level of a measurand. The implementation of data fusion to the field of NDT is relatively new. This paper summarizes the achievements of current research on data fusion applied to NDT. A theoretical data fusion strategy is described and experimental results generated from weld inspection are presented. 相似文献
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This article discusses the benefits of different infill sampling criteria used in surrogate-based constrained global optimization. A new method which selects multiple updates based on Pareto optimal solutions is introduced showing improvements over a number of existing methods. The construction of surrogates (also known as meta-models or response surface models) involves the selection of a limited number of designs which are analysed using the original expensive functions. A typical approach involves two stages. First the surrogate is built using an initial sampling plan; the second stage updates the model using an infill sampling criterion to select further designs that offer improvement. Selecting multiple update points at each iteration, allowing distribution of the expensive function evaluations on several processors offers large potential for accelerating the overall optimization process. This article provides a comparison between different infill sampling criteria suitable for selecting multiple update points in the presence of constraints. 相似文献
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W. J. T. Daniel 《International journal for numerical methods in engineering》1997,40(15):2841-2855
An algorithm for explicit integration of structural dynamics problems with multiple time steps is proposed that averages accelerations to obtain subcycle states at a nodal interface between regions integrated with different time steps. With integer time step ratios, the resulting subcycle updates at the interface sum to give the same effect as a central difference update over a major cycle. The algorithm is shown to have good accuracy, and stability properties in linear elastic analysis similar to those of constant velocity subcycling algorithms. The implementation of a generalised form of the algorithm with non-integer time step ratios is presented. © 1997 John Wiley & Sons, Ltd. 相似文献
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A heteroassociative joint transform correlation (JTC) technique is proposed for recognizing and tracking multiple heteroassociative or dissimilar targets from gray-level image sequences by use of the concept of fringe-adjusted JTC and a multiple-target-detection algorithm. A fringe-adjusted JTC technique is used to ensure quantification of the similarities among several input images while it satisfies the equal-correlation-peak criterion. Tracking is accomplished by retrieval of the target motion information estimated from multiple consecutive image frames. An enhanced version of the fringe-adjusted filter is incorporated into the heteroassociative multiple-target-detection process to optimize the correlation performance. The feasibility of the proposed technique is tested by computer simulation with real infrared image data. 相似文献