PMID- 25951339
OWN - NLM
STAT- MEDLINE
DCOM- 20151222
LR  - 20150630
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 15
IP  - 5
DP  - 2015 May 5
TI  - A novel artificial fish swarm algorithm for recalibration of fiber optic 
      gyroscope error parameters.
PG  - 10547-68
LID - 10.3390/s150510547 [doi]
AB  - The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm 
      intelligent techniques, which is widely utilized for optimization purposes. Fiber 
      optic gyroscope (FOG) error parameters such as scale factors, biases and 
      misalignment errors are relatively unstable, especially with the environmental 
      disturbances and the aging of fiber coils. These uncalibrated error parameters 
      are the main reasons that the precision of FOG-based strapdown inertial 
      navigation system (SINS) degraded. This research is mainly on the application of 
      a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients 
      recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack 
      of using artificial fishes' pervious experiences, lack of existing balance 
      between exploration and exploitation, and high computational cost) of the 
      standard AFSA during the optimization process. To solve these weak points, 
      functional behaviors and the overall procedures of AFSA have been improved with 
      some parameters eliminated and several supplementary parameters added. Second, a 
      hybrid FOG error coefficients recalibration algorithm has been proposed based on 
      NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to 
      maximum utilization of the involved approaches for FOG error coefficients 
      recalibration. After that, the NAFSA is verified with simulation and experiments 
      and its priorities are compared with that of the conventional calibration method 
      and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error 
      coefficients recalibration.
FAU - Gao, Yanbin
AU  - Gao Y
AD  - Institute of Inertial Navigation and Measurement & Control Technology, College of 
      Automation, Harbin Engineering University, Harbin 150001, China. 
      gaoyanbin@hrbeu.edu.cn.
FAU - Guan, Lianwu
AU  - Guan L
AD  - Institute of Inertial Navigation and Measurement & Control Technology, College of 
      Automation, Harbin Engineering University, Harbin 150001, China. 
      guanlianwu2013@gmail.com.
AD  - NavINST-Navigation and Instrumentation Research Group, Department of Electrical 
      and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada. 
      guanlianwu2013@gmail.com.
FAU - Wang, Tingjun
AU  - Wang T
AD  - No. 16 of China Aerospace Science and Technology Corporation, Xi'an 710001, 
      China. wangtingjun0309@126.com.
FAU - Sun, Yunlong
AU  - Sun Y
AD  - Institute of Inertial Navigation and Measurement & Control Technology, College of 
      Automation, Harbin Engineering University, Harbin 150001, China. 
      sunyunlong@hrbeu.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150505
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - *Artificial Intelligence
PMC - PMC4481985
EDAT- 2015/05/08 06:00
MHDA- 2015/12/23 06:00
PMCR- 2015/05/01
CRDT- 2015/05/08 06:00
PHST- 2015/03/10 00:00 [received]
PHST- 2015/04/23 00:00 [revised]
PHST- 2015/04/27 00:00 [accepted]
PHST- 2015/05/08 06:00 [entrez]
PHST- 2015/05/08 06:00 [pubmed]
PHST- 2015/12/23 06:00 [medline]
PHST- 2015/05/01 00:00 [pmc-release]
AID - s150510547 [pii]
AID - sensors-15-10547 [pii]
AID - 10.3390/s150510547 [doi]
PST - epublish
SO  - Sensors (Basel). 2015 May 5;15(5):10547-68. doi: 10.3390/s150510547.