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西北工业大学 国内
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研究单位
科研成果
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查看 Scopus 资料
梁 彦
Professor
自动化学院
h-index
2016
引用
23
H-指数
根据储存在 Pure 的刊物以及来自 Scopus 的引用文献数量计算
2006
2025
每年的科研成果
综述
指纹
网络
科研成果
(205)
相似简介
(6)
指纹
深入其中 Yan Liang 为活跃的研究主题。这些主题标签来自此人的成果。它们共同形成唯一的指纹。
分类
加权
按字母排序
Engineering
Tracking (Position)
100%
Target Tracking
92%
State Estimation
72%
Joints (Structural Components)
63%
Simulation Result
53%
Maximization
44%
Gaussians
41%
Measurement Noise
31%
Multi--Target Tracking
31%
Dynamic Model
30%
Over-the-Horizon Radar
28%
Recursive
26%
Nonlinear System
26%
Joint Estimation
26%
Estimation Error
23%
Target State
22%
Reinforcement Learning
21%
Equality Constraint
21%
Mean Square Error
20%
Numerical Example
20%
Gaussian Mixture
19%
Sensor Network
19%
Sensor Fusion
19%
Least Square
16%
Particle Filter
16%
Nodes
14%
Process Noise
13%
Discrete Time
13%
Tracking Algorithm
13%
Stochastic System
12%
System State
12%
Motion Model
11%
Error Covariance
10%
Orthogonality
10%
Control System
10%
Likelihood Function
10%
Non-Linear Dynamics
9%
Observability
9%
Target Recognition
9%
Propagation Mode
9%
Cyber-Physical Systems
8%
Model System
8%
Random Measurement
8%
Lstm
8%
Expectation Maximization Algorithm
8%
Flight Control
8%
Illustrates
8%
Extended Kalman Filter
8%
Multiplicative Noise
8%
Load Frequency Control
8%
Computer Science
Tracking (Position)
76%
State Estimation
43%
multi sensor
20%
Nonlinear System
16%
Equality Constraint
15%
Association Rule
15%
Particle Filter
14%
And-States
14%
maneuvering target
14%
Least Squares Method
13%
Belief Function
13%
Estimation Accuracy
13%
Bernoulli Filter
12%
Sensor Network
12%
Gaussian Mixture
11%
Reinforcement Learning
11%
Data Association
10%
System Dynamic
10%
Tracking Algorithm
8%
Random Measurement
8%
Likelihood Function
8%
Experimental Result
8%
Probability Density Function
8%
load-frequency control
8%
Motion Model
7%
Transition Matrix
7%
Tracking Object
7%
Cardinality
7%
Target Recognition
7%
probability hypothesis density filter
7%
probability hypothesis density
7%
Traditional Method
7%
Estimation Error
7%
Computational Complexity
7%
State Estimate
6%
Covariance Matrix
6%
Interpretability
6%
Expert Knowledge
6%
Density Function
6%
Numerical Example
6%
Deep Reinforcement Learning
6%
Optimization Problem
6%
Bayesian Framework
5%
Computation Complexity
5%
Traffic Control
5%
Boundary Detection
5%
Unknown Parameter
5%
Deep Learning Method
5%
Posterior Probability
5%
Constraint Parameter
5%
