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Nijia Qian, Guobin Chang*. (2021). Optimal filtering for state space model with time-integral measurements
- Release time:2021-02-19
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Impact Factor:
5.6Journal:
MeasurementKey Words:
State space model, Time integral measurement, Time difference measurement, Filtering, Colored noise, Cross correlated noiseAbstract:
Filtering for state space model with time-integral measurement is studied. In this model, the measurement is an integral function, rather than an algebraic function, of the state vector. To be more specific, in the problem studied, the measurement is the between-epoch integral of a linear combination of the state vector. This is a new measurement type which arises in several navigation applications with velocity as part of the state vector and with between-epoch displacement as the measurement of the state space model. A rigorous discrete state space model is constructed from first principles to correctly represent the problem to be solved. Compared to this rigorous model, the model used in conventional method is inferior due to the following: a) the covariance of the measurement noise is wrongly determined; b) the between-epoch correlation in the measurement noise is ignored; and c) the cross correlation between the process noise and the measurement noises is ignored. Optimal filtering algorithm is designed according to the rigorous model. A simulation study is conducted to validate the superior performance of the proposed method to the conventional method.Indexed by:
Journal paperDocument Code:
109209Discipline:
EngineeringFirst-Level Discipline:
Surveying and MappingDocument Type:
JVolume:
176Translation or Not:
noDate of Publication:
2021-02-19Included Journals:
SCILinks to published journals:
https://doi.org/10.1016/j.measurement.2021.109209
- Pre One:Nijia Qian, Guobin Chang*, Jingxiang Gao, Cheng Pan, et al. (2021). Vehicle's Instantaneous Velocity Reconstruction by Combining GNSS Doppler and Carrier Phase Measurements through Tikhonov Regularized Kernel Learning
- Next One:Liu Yang, Guobin Chang*, Nijia Qian, Jingxiang Gao. (2020). Improved atmospheric weighted mean temperature modeling using sparse kernel learning