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基于Transformer复杂运动辨识的机动星凸形扩展目标跟踪方法

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雷达学报（中英文） 2024年第3期13卷 629-645页

作者：陈辉边斌超连峰韩崇昭兰州理工大学电气工程与信息工程学院兰州730050 西安交通大学自动化科学与工程学院西安710049

针对复杂的机动扩展目标跟踪问题,利用Transformer网络设计了一种有效的星凸不规则形状机动扩展目标跟踪方法。首先,该文研究利用alpha-shape算法建立了星凸形状的变化模型,实现了静态场景下的星凸形扩展目标的形状估计。然后,通过对目... 详细信息

针对复杂的机动扩展目标跟踪问题,利用Transformer网络设计了一种有效的星凸不规则形状机动扩展目标跟踪方法。首先,该文研究利用alpha-shape算法建立了星凸形状的变化模型,实现了静态场景下的星凸形扩展目标的形状估计。然后,通过对目标状态转移矩阵进行重新设计,结合Transformer网络对机动扩展目标运动状态转移矩阵进行实时估计,实现了对复杂机动目标运动过程的精准跟踪。进一步地,将估计得到的形状轮廓与运动状态进行融合,最终实现了对星凸形机动扩展目标的实时跟踪。最后,通过构造复杂的机动扩展目标跟踪场景,利用多重性能指标测试算法对形状和运动状态的综合估计性能,验证了算法的有效性。

关键词：扩展目标跟踪机动目标 Transformer 星凸形弗雷歇距离-面积误差

An improved multiple model GM-PHD filter for maneuvering target tracking

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Chinese Journal of Aeronautics 2013年第1期26卷 179-185页

作者： Wang Xiao han chongzhao The Flight Automatic Control Research Institute of AVIC School of Electronic and Information Engineering Xi'an Jiaotong University

In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.

关键词： Estimation Gaussian mixture Maneuvering target racking Multiple model Probability hypothesis density

Quadrature Kalman particle fitler

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Journal of Systems Engineering and Electronics 2010年第2期21卷 175-179页

作者： Chunlincl Wu chongzhao han School of Electronic and Control Engineering Chang'an University Xi'an 710064 P. R. China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China

In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm.

关键词： particle filter statistical linear regression quadrature Kalman filter importance density function.

Dynamic weighted voting for multiple classifier fusion:a generalized rough set method

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Journal of Systems Engineering and Electronics 2006年第3期17卷 487-494页

作者： Sun Liang han chongzhao School of Electronic & Information Engineering Xi'an Jiaotong Univ. Xi'an 710049 P. R. China Inst. of Information Science & Technology Zhengzhou 450001 P. R. China

To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.

关键词： multiple classifier fusion dynamic weighted voting generalized rough set hyperspectral.

Direct data domain approach to space-time adaptive processing

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Journal of Systems Engineering and Electronics 2006年第1期17卷 59-64页

作者： Wen Xiaoqin han chongzhao School of Electronic and Information Engineering Xi'an Jiaotong Univ. Xi'an 710049 P. R. China

In non-homogeneous environment, traditional space-time adaptive processing doesn＇t effectively suppress interference and detect target, because the secondary data don＇ t exactly reflect the statistical characteristic of the range cell under test. A ravel methodology utilizing the direct data domain approach to space-time adaptive processing （ STAP ） in airbome radar non-homogeneous environments is presented. The deterministic least squares adaptive signal processing technique operates on a ＂snapshot-by-snapshot＂ basis to dethrone the adaptive adaptive weights for nulling interferences and estimating signal of interest （SOI）. Furthermore, this approach eliminates the requirement for estimating the covariance through the data of neighboring range cell, which eliminates calculating the inverse of covariance, and can be implemented to operate in real-time. Simulation results illustrate the efficiency of interference suppression in non-homogeneous environment.

关键词： space-time adaptive processing direct data domain interference suppression.

A novel approximation of basic probability assignment based on rank-level fusion

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Chinese Journal of Aeronautics 2013年第4期26卷 993-999页

作者： Yang Yi han Deqiang han chongzhao Cao Feng State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Xi'an Jiaotong University Institute of Integrated Automation MOE KLINNS Lab School of Electronic and Information Engineering Xi'an Jiaotong University School of Energy and Power Engineering Xi'an Jiaotong University

Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment （BPA） approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.

关键词： Belief approximation Distance of evidence Evidence combination Information fusion Rank-level fusion

Modified center-based feature line classification approach

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Frontiers of Electrical and Electronic Engineering in China 2010年第2期5卷 173-178页

作者： Deqiang han chongzhao han Yi YANG Institute of Integrated Automation Xi’an Jiaotong UniversityXi’an 710049China

A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of *** NFL,MCFL defines a different type of feature line and utilizes both the query point’s local information and corresponding class-global information in training *** experiments provided,the comparisons with the nearest neighbor(NN),NFL,and other NFL-refined approaches show that the computation time of MCFL can be shortened dramatically with less accuracy *** proposed is probably a better choice for the classification application tasks of large-scale dataset.

关键词： classification nearest feature line(NFL) nearest neighbor line(NNL) center-based nearest neighbor(CNN) modified center-based feature line(MCFL)

CLASSIFIER FUSION BASED ON EVIDENCE THEORY AND ITS APPLICATION IN FACE RECOGNITION

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Journal of Electronics(China) 2009年第6期26卷 771-776页

作者： Yang Yi han chongzhao han Deqiang Institute of Integrated Automation Xi＇an Jiaotong University Xi＇an 710049 China

A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL),which is called Center-based Nearest Neighbor (CNN). CNN retains the advantages of NFL while it has relatively low computational cost. Different member classifiers are trained based on different feature spaces respectively. Corresponding mass functions can be generated based on proposed mass function determination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.

关键词： Nearest Feature Line (NFL) Evidence combination Classifier fusion Face recognition

MULTITARGET STATE AND TRACK ESTIMATION FOR THE PROBABILITY HYPOTHESES DENSITY FILTER

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Journal of Electronics(China) 2009年第1期26卷 2-12页

作者： Liu Weifeng han chongzhao Lian Feng Xu Xiaobin Wen Chenglin School of Electronic Information Engineering Xi＇an Jiaotong University Xi＇an 710049 China Institute of Information and Control Hangzhou Dianzi University Hangzhou 310018 China

The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.

关键词： Probability Hypotheses Density (PHD) Particle-PHD filter State and track estimation Finite mixture models