Abstract
This paper considers performance criteria for the identification of sensor error models and the procedure for their calculation. These criteria are used to investigate the efficiency of the identification problem solution, depending on the initial data, and to carry out a comparative analysis of various suboptimal algorithms. The calculation procedure is based on an algorithm that solves the joint problem of hypothesis recognition and parameter estimation within the Bayesian approach. A performance analysis of the models traditionally used to describe errors of inertial sensors is given to illustrate the application of the procedure for the calculation of performance criteria.
Highlights
Algorithms based on system description in state space are widely used in control and estimation problems
The approach proposed in [21,22,23] delivers a global selection criterion based on the wavelet variance that can be used to design an algorithm for automatic identification of a model structure
The Bayesian approach-based algorithm for identification of a sensor error model was considered in [42], but in this paper we focus on obtaining performance criteria for solution of the identification problem, propose methods for their calculation and give an example of their practical application
Summary
Algorithms based on system description in state space are widely used in control and estimation problems. Bayesian filtering algorithms, such as the Kalman filter, unscented Kalman filter, and particle filter [1,2,3,4], which require the derivation of system models in state space in the form of random processes, are often used Such a problem arises during the processing of redundant measurements, in particular, navigation data fusion, which involves signals from multiple sensors [5,6,7]. The Bayesian approach-based algorithm for identification of a sensor error model was considered in [42], but in this paper we focus on obtaining performance criteria for solution of the identification problem, propose methods for their calculation and give an example of their practical application. Part 4 illustrates the proposed method by the example of the identification problem solution for models traditionally used to describe the errors of navigation sensors
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