scholarly journals A New State Estimation Method with Radar Measurement Missing

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hongjian Wang ◽  
Cun Li ◽  
Ying Wang ◽  
Qing Li ◽  
Xicheng Ban
Keyword(s):  
State Estimation ◽  
State Transition ◽  
Estimation Method ◽  
The State ◽  
Kolmogorov Equation ◽  
Transition Model ◽  
Observation Data ◽  
Adjustment Strategy ◽  
Adaptive Adjustment ◽  
State Transition Model

This paper describes a method that addresses the transient loss of observations in sea surface target state estimations. A six degrees of freedom swing platform fixed with a MiniRadaScan is used to simulate the loss of observations. The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. An observation data sliding window width adaptive adjustment strategy is proposed that can improve the fitting accuracy of the state transition model. To solve the problem where the weight value of the Gaussian components of the Gaussian mixture filter is not changed in the time update stage while the observation is missing, an adaptive adjustment strategy for the weight is proposed based on the Chapman-Kolmogorov equation, which can improve the estimation precision under the conditions of the missing observation. The simulation test demonstrates the proposed accuracy and real-time performance of the proposed algorithm.

scholarly journals Vehicle Tracking Algorithm Based on Observation Feedback and Block Symmetry Particle Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yanshuang Hao ◽  
Yixin Yin ◽  
Jinhui Lan
Keyword(s):  
Particle Filter ◽  
State Transition ◽  
Vehicle Tracking ◽  
Transition Model ◽  
Observation Data ◽  
Observation Model ◽  
Partial Occlusion ◽  
Proposal Distribution ◽  
State Transition Model ◽  
Particle Filter Algorithm

This paper proposes a novel particle filter algorithm for vehicle tracking, which feeds observation information back to state model and integrates block symmetry into observation model. In view of the proposal distribution in traditional particle filter without considering the observation data, a new state transition model which takes the observation into account is presented, so that the allocation of particles is more familiar with the posterior distribution. To track the vehicles in background with similar colors or under partial occlusion, block symmetry is proposed and introduced into the observation model. Experimental results show that the proposed algorithm can improve the accuracy and robustness of vehicle tracking compared with traditional particle filter and Kernel Particle Filter.

State Transition Model of Green Logistical Network in Manufacturing Engineering

2013 ◽  
Vol 340 ◽  
pp. 255-258
Author(s):  
Yong Sheng Huang ◽  
Hua Mei Du
Keyword(s):  
Internet Of Things ◽  
State Transition ◽  
The State ◽  
The Internet ◽  
Transition Model ◽  
State Transition Model ◽  
Manufacturing Engineering ◽  
The Internet Of Things

With the developing of the technologies in the field of the Internet of things, it is much possible to achieve more information about things scattered in a certain zone. Based on the Internet of things, the information zing and modelling of the logistical network are efficient methods for logistical services. In this paper, an approach for the state transition model of logistical network is put forward, in which the sets of key elements and their states, and as well as the correlations between and among the key elements with certain states are used as components to express the states and states transition of the logistical network.

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