TY - JOUR
T1 - Cooperative Localisation of AUVs using Moving Horizon Estimation
AU - Wang, Sen
AU - Chen, Ling
AU - Gu, Dongbing
AU - Hu, Huosheng
PY - 2014/1
Y1 - 2014/1
N2 - This paper studies the localization problem of autonomous underwater vehicles (AUVs) constrained by limited size, power and payload. Such AUVs cannot be equipped with heavy sensors which makes their underwater localization problem difficult. The proposed cooperative localization algorithm is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper is twofold: 1) The observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. It is further compared with observability of linearized system to verify that a nonlinear state estimation is necessary. 2) Moving horizon estimation is integrated with extended Kalman filter (EKF) for three dimensional localization using single beacon, which can alleviate the computational complexity, impose various constraints and make use of several previous range measurements for each estimation. The observability and improved localization accuracy of the localization algorithm are verified by extensive numerical simulation compared with EKF.
AB - This paper studies the localization problem of autonomous underwater vehicles (AUVs) constrained by limited size, power and payload. Such AUVs cannot be equipped with heavy sensors which makes their underwater localization problem difficult. The proposed cooperative localization algorithm is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper is twofold: 1) The observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. It is further compared with observability of linearized system to verify that a nonlinear state estimation is necessary. 2) Moving horizon estimation is integrated with extended Kalman filter (EKF) for three dimensional localization using single beacon, which can alleviate the computational complexity, impose various constraints and make use of several previous range measurements for each estimation. The observability and improved localization accuracy of the localization algorithm are verified by extensive numerical simulation compared with EKF.
UR - https://www.researchgate.net/publication/311888178_Cooperative_Localization_of_AUVs_Using_Moving_Horizon_Estimation
UR - https://pdfs.semanticscholar.org/dcc4/4f85988937f7b882dca26905a6e66d4c14f3.pdf
UR - http://www.academia.edu/28807639/Cooperative_Localization_of_AUVs_Using_Moving_Horizon_Estimation
U2 - 10.1109/JAS.2014.7004622
DO - 10.1109/JAS.2014.7004622
M3 - Article
SN - 2329-9274
VL - 1
SP - 68
EP - 76
JO - IEEE/CAA Journal of Automatica Sinica
JF - IEEE/CAA Journal of Automatica Sinica
IS - 1
M1 - 7004622
ER -