scholarly journals Tracking Air-to-Air Missile Using Proportional Navigation Model with Genetic Algorithm Particle Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hongqiang Liu ◽  
Lei Yu ◽  
Chenwei Ruan ◽  
Zhongliang Zhou
Keyword(s):  
Genetic Algorithm ◽  
Particle Filter ◽  
State Space Model ◽  
Filter Method ◽  
Motion Model ◽  
Proportional Navigation ◽  
Real Motion ◽  
Space Model ◽  
Motion Models ◽  
Better Than

The purpose of this paper is to track the air-to-air missile. Here we put forward the PN-GAPF (Proportional Navigation motion model and Genetic Algorithm Particle Filter) method to solve the problem. The main jobs we have done can be listed as follows: firstly, we establish the missile state space model named as the Proportional Navigation (PN) motion model to simulate the real motion of the air-to-air missile; secondly, the PN-EKF and PN-PF methods are proposed to track the missile, through combining PN motion model with EKF and PF; thirdly, in order to solve the particle degeneracy and diversity loss, we introduce the intercross and variation in GA to the particles resampling step and then the PN-GAPF method is put forward. The simulation results show that the PN motion model is better than the CV and CA motion models for tracking the air-to-air missile and that the PN-GAPF method is more efficient than the PN-EKF and PN-PF.

Accuracy Improvement of GPS Positioning Based on GA-Aided Particle Filter

2015 ◽  
Vol 719-720 ◽  
pp. 737-743
Author(s):  
Er Shen Wang ◽  
Tao Pang ◽  
Zhi Xian Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Filter ◽  
State Space Model ◽  
Experimental Results ◽  
Dynamic State ◽  
Estimation Accuracy ◽  
Particle Filter Algorithm ◽  
Gps System ◽  
Evolutionary Idea ◽  
Better Than

Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on GA-aided particle filter was presented. Taking the advantage of genetic algorithm ( GA ) in selection ,crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This GA-aided particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the GA-aided particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of GA-aided particle filter is better than that of particle filter (PF).

Short-Term Oil Price Forecasting Based on State Space Model

2011 ◽  
Vol 403-408 ◽  
pp. 2530-2534
Author(s):  
Wei Qi Li ◽  
Lin Wei Ma ◽  
Ya Ping Dai ◽  
Dong Hai Li
Keyword(s):  
State Space ◽  
Likelihood Function ◽  
State Space Model ◽  
The State ◽  
Oil Price ◽  
Filter Method ◽  
Parameter Estimates ◽  
Price Forecasting ◽  
Short Term ◽  
Space Model

In competitive petroleum markets, oil price forecasting has always been an important strategic tool for oil producers and consumers to predict market behavior. In this study, we researched the monthly crude oil price in the period between 1988 and 2009. Firstly, we present a state space model to represent oil price system. Secondly, we determine the parameter estimates of the state space model for oil price through a faster algorithm to compute the likelihood function. Lastly, we use the Kalman filter method to estimate the next three months’ oil price and compare it with the econometric structure model as a benchmark. Empirical results indicate that the state space model performs well in terms of some standard statistics indices, and it may be a promising method for short-term oil price forecasting.

Estimation of SOC Based on Subspace Model of Power Battery and Adaptive Kalman Filter Method

2013 ◽  
Vol 448-453 ◽  
pp. 1423-1427
Author(s):  
Min Xin Zheng ◽  
Qing Sen Yang
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
State Space Model ◽  
Equivalent Circuit Model ◽  
Filter Method ◽  
Adaptive Kalman Filter ◽  
Space Model ◽  
Kalman Filter Method ◽  
Set Up ◽  
The Relationship

Subspace identification method was adopted to build a state-space model of the battery pack by directly using the acquisition data of current and voltage. The terminal voltage was split into four parts according to the relationship between the current and each element of the models output voltage. Then an equivalent circuit model composed of resistances and capacities was set up to simulate the relationship. Based on the battery model, a state space model with SOC as the state variable and voltage UCb as the output was set up. By applying a designed adaptive Kalman filter method to the model and adopting the voltage UT1 from the subspace method as the measured output, the optimum estimation of SOC can be acquired with only calculations of one dimension.

scholarly journals A Kalman particle filter for online parameter estimation with applications to affine models

2021 ◽  
Author(s):  
Jian He ◽  
Asma Khedher ◽  
Peter Spreij
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Space ◽  
Outer Layer ◽  
Posterior Distribution ◽  
State Space Model ◽  
The State ◽  
State Variables ◽  
Unknown Parameters ◽  
Space Model

AbstractIn this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous-time state space model with discrete-time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two layer structure, in which the outer layer provides the estimation of the posterior distribution of the unknown parameters and the inner layer provides the estimation of the posterior distribution of the state variables. This algorithm has a similar structure as the so-called recursive nested particle filter, but unlike the latter filter, in which both layers use a particle filter, our algorithm introduces a dynamic kernel to sample the parameter particles in the outer layer to obtain a higher convergence speed. Moreover, this algorithm also implements the Kalman filter in the inner layer to reduce the computational time. This algorithm can also be used to estimate the parameters that suddenly change value. We prove that, for a state space model with a certain structure, the estimated posterior distribution of the unknown parameters and the state variables converge to the actual distribution in $$L^p$$ L p with rate of order $${\mathcal {O}}(N^{-\frac{1}{2}}+\varDelta ^{\frac{1}{2}})$$ O ( N - 1 2 + Δ 1 2 ) , where N is the number of particles for the parameters in the outer layer and $$\varDelta $$ Δ is the maximum time step between two consecutive observations. We present numerical results of the implementation of this algorithm, in particularly we implement this algorithm for affine interest models, possibly with stochastic volatility, although the algorithm can be applied to a much broader class of models.

Online remaining useful life prognostics using an integrated particle filter

2018 ◽  
Vol 232 (6) ◽  
pp. 587-597 ◽  
Author(s):  
Yawei Hu ◽  
Shujie Liu ◽  
Huitian Lu ◽  
Hongchao Zhang
Keyword(s):  
Particle Filter ◽  
State Space ◽  
State Space Model ◽  
Remaining Useful Life ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Mechanical Equipment ◽  
Milling Cutter ◽  
Space Model ◽  
Useful Life

The lifetime evolution of mechanical equipment with complicated structure and the harsh operating environment cannot be accurately expressed due to the dynamics of the failure mechanism. However, the performance monitoring of equipment, with the information characterizing the failure process from the sensed data, can be used to assess the failure time and then the online remaining useful life. Because of the existence of nonlinearity and non-Gaussian for most real systems, for online assessment, unscented Kalman filter combined with particle filter is studied, instead of the standard particle filter with importance sampling, which is modified to update the states iteratively. Meanwhile, Markov chain Monte Carlo is performed after resampling to improve the prediction accuracy. In the modeling, state–space model is developed to quantify the relationship between the information from online observation and underlying degradation, and the unscented particle filter is investigated to realize the assessment of remaining useful life. In particular, the sufficient statistic method is presented to obtain a joint recursive estimation on both the system state and model parameters for those state–space model with unknown time-invariant ones. At the end of this article, the acoustic emission signals of a milling cutter are illustrated as a case study for cutter online remaining useful life estimate. The milling cutter example demonstrates the effectiveness of the proposed method for online estimate and provides useful insights regarding the necessity of online updating and the assessment.

Comparison on Three Filter Methods in Self-Alignment for SINS of the Carrier Craft

2012 ◽  
Vol 591-593 ◽  
pp. 1793-1799
Author(s):  
Yong Jun Wang ◽  
Jing Shuo Xu ◽  
Rui Hua Song ◽  
Yang Gao ◽  
Ya Zhou Di
Keyword(s):  
Kalman Filter ◽  
Adaptive Filter ◽  
State Space Model ◽  
Basic Principles ◽  
Space Model ◽  
Filter Performance ◽  
Filter Methods ◽  
Simulation Results ◽  
Better Than ◽  
Fuzzy Adaptive

Fuzzy adaptive filter and H∞ filter are introduced to solve the problem of low filter performance, which comes from uncertain noise caused by seawave and high frequence vibrancy. First, basic principles of the fuzzy adaptive filter and H∞ filter are formulated. Second, state space model of self-alignment for SINS of the carrier craft is built. Finally, according to each character, a comparison on results that Kalman filter, fuzzy adaptive filter and H∞ filter are applied to alignment for SINS of the carrier craft is made. Simulation results show that although Kalman filter has definite robustness to external uncertainty noise, weak anti-jamming ability and bad filter performance make self-alignment failed. Fuzzy adaptive filter and H∞ filter have strong ability to suppress external uncertain noise and can obtain good filtering accuracy. They both can complete self-alignment. Filtering accuracy and rapidity of fuzzy adaptive filter are better than those of H∞ filter, while robustness and curve smoothness of H∞ filter are stronger than those of other filters.

scholarly journals A Novel Identification Method for Generalized T-S Fuzzy Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ling Huang ◽  
Kai Wang ◽  
Peng Shi ◽  
Hamid Reza Karimi
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Systems ◽  
Ant Colony Algorithm ◽  
State Space Model ◽  
The State ◽  
Model Parameters ◽  
State Variables ◽  
Space Model ◽  
Affine Nonlinear Systems ◽  
Simulation Results

In order to approximate any nonlinear system, not just affine nonlinear systems, generalized T-S fuzzy systems, where the control variables and the state variables, are all premise variables are introduced in the paper. Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm. The simulation results show the effectiveness of the proposed algorithm.

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