원문정보
초록
영어
Particle filter based on particle swarm optimization (PSO-PF) is not precise enough
and trapping in local optimum easily, it is not able to meet the requirement of modern
navigation system. To solve the problems, a new particle filter based on dynamic clone
particle swarm optimization (DPSO-PF) is presented in this paper. This improved filter
enables the particles to fit the condition better and then reach the goal of global
optimization through orthogonal initialization, clonal selection and local searching of
self-learning, accordingly a best balance is achieved between optimal exploring and
convergence rate. Finally univariate nonstationary growth model and integrated
navigation model are used for simulation experiment and the results indicate that this new
filter improves the precision of GPS/INS integrated navigation system.
목차
1. Introduction
2. Particle Filter
3. PSO-PF Algorithm
4. Building of Integrated Navigation Model
4.1. State and Measurement Equations
4.2 Discretization of State and Measurement Equations
5. DPSO-PF Algorithm
5.1. Improvement of DPSO-PF
5.2. Steps for DPSO-PF
6. Experimental Simulation
6.1. Simulation Test of Basic Algorithm Performance
6.2. Simulation Test of Performance In Integrated Navigation System
7. Conclusion
References