TY - GEN
T1 - On state estimation of dynamic systems by applying scalar estimation algorithms
AU - Shen, Kai
AU - Neusipin, K. A.
AU - Proletarsky, A. V.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/12
Y1 - 2015/1/12
N2 - The scalar estimation algorithms are low-sensitive to input noise statistics due to adaptive adjustment of the gain coefficient depending on current estimation errors. Scalar approaches to state vector estimation differ from others by its capability to form estimation equation independently for each observable component of the state vector. In order to increase the accuracy of scalar estimation algorithms, the quantitative criteria of observability was proposed. By applying error-models of inertial navigation systems, the formulae of observability degree of misalignment angle and drift rate were deduced. For the purpose of analyzing the capacity of suggested approaches, laboratory tests based on actual inertial navigation systems were applied. The analyzed results indicate that the growth of sampling time within a certain range generates the increase of the degree of observability.
AB - The scalar estimation algorithms are low-sensitive to input noise statistics due to adaptive adjustment of the gain coefficient depending on current estimation errors. Scalar approaches to state vector estimation differ from others by its capability to form estimation equation independently for each observable component of the state vector. In order to increase the accuracy of scalar estimation algorithms, the quantitative criteria of observability was proposed. By applying error-models of inertial navigation systems, the formulae of observability degree of misalignment angle and drift rate were deduced. For the purpose of analyzing the capacity of suggested approaches, laboratory tests based on actual inertial navigation systems were applied. The analyzed results indicate that the growth of sampling time within a certain range generates the increase of the degree of observability.
KW - Degree of observability
KW - Dynamic system
KW - Inertial navigation system
KW - Observability of system
KW - Scalar estimation algorithm
UR - http://www.scopus.com/inward/record.url?scp=84922552064&partnerID=8YFLogxK
U2 - 10.1109/CGNCC.2014.7007228
DO - 10.1109/CGNCC.2014.7007228
M3 - Conference contribution
AN - SCOPUS:84922552064
T3 - 2014 IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2014
SP - 124
EP - 129
BT - 2014 IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2014
Y2 - 8 August 2014 through 10 August 2014
ER -