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
Abstract 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
보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Control and Automation
간기
월간
pISSN
2005-4297
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Control and Automation Vol.9 No.10