Abstract
Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.
Highlights
Reliable state estimation from noisy measurements is one of the essential requirements in numerous real time scientific and engineering problems
In order to manage the non-linearity within the system model and outliers within observations, a robust reweighted iterative extended Kalman filter (RRIEKF) is proposed
Once the system states are updated using weighted least squares (WLS), the updated states are used in the iterated extended Kalman Filter (IEKF) observation model using Equation (9) in an iterative manner to determine the optimal point
Summary
Reliable state estimation from noisy measurements is one of the essential requirements in numerous real time scientific and engineering problems. Calibration errors, measuring system failure and measurement accuracy limits are some of the main causes that can bias the source system observations In applications, those depend upon the range sensors [3], contamination within measurements grows as the relative distance between the measuring system and object increases, e.g., infrared (IR) distance measurement sensors. E.g., vision sensor measurements [4], the amount of contamination grows with the size of data In systems such as image processing [5], control systems [6], communication systems [7] and tracking systems [8], observations are contaminated with multiplicative noise. The presence of outliers severely degrades the reliability of the state estimation process of non-linear dynamic systems. The accuracy of estimated states directly depends upon the error models of system dynamics which are established based on predefined assumptions. For reliable state estimations, the process must likely be able to handle the divergence from model assumptions
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