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Informaţii generale |
| Autor |
Jwo, Dah-Jing; Hu, Chia-Wei; Tseng, Chien-Hao |
| Publicat |
InTech Open Access Publisher, 2013
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| Abstract |
This paper conducts a performance evaluation
for the ultra‐tight integration of a Global positioning
system (GPS) and an inertial navigation system (INS),
using nonlinear filtering approaches with an interacting
multiple model (IMM) algorithm. An ultra‐tight GPS/INS
architecture involves the integration of in‐phase and
quadrature components from the correlator of a GPS
receiver with INS data. An unscented Kalman filter
(UKF), which employs a set of sigma points by
deterministic sampling, avoids the error caused by
linearization as in an extended Kalman filter (EKF). Based
on the filter structural adaptation for describing various
dynamic behaviours, the IMM nonlinear filtering
provides an alternative for designing the adaptive filter in
the ultra‐tight GPS/INS integration. The use of IMM
enables tuning of an appropriate value for the process of
noise covariance so as to maintain good estimation
accuracy and tracking capability. Two examples are
provided to illustrate the effectiveness of the design and
demonstrate the effective improvement in navigation
estimation accuracy. A performance comparison among
various filtering methods for ultra‐tight integration of
GPS and INS is also presented. The IMM based nonlinear
filtering approach demonstrates the effectiveness of the
algorithm for improved positioning performance. |
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International Journal of Advanced Robotic Systems
Autor: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publicado: 2004
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