In 1960, Rudolf E. Kalman created what is known as the Kalman filter, which is a way to estimate unknown variables from noisy measurements. The algorithm follows the logic that if the previous state of the system is known, it could be used as the best guess for the current state. This information is first applied a priori to any measurement by using it in the underlying dynamics of the system. ...

A filter for estimating spacecraft attitude quaternion and gyro drift from vector measurements in the presence of white noises in the gyro error, in the drift dynamics, and in the line-of-sight measurement error is developed. The variance parameters of the white noises are unknown, and are modeled as non-anticipative second order stochastic processes. The approach taken in this work consists in...

In this note, a robust finite-horizon Kalman filter is designed for discrete time-varying uncertain systems with both additive and multiplicative noises. The system under consideration is subject to both deterministic and stochastic uncertainties. Sufficient conditions for the filter to guarantee an optimized upper bound on the state estimation error variance for admissible uncertainties are es...

In this note, a robust finite-horizon Kalman filter is designed for discrete time-varying uncertain systems with both additive and multiplicative noises. The system under consideration is subject to both deterministic and stochastic uncertainties. Sufficient conditions for the filter to guarantee an optimized upper bound on the state estimation error variance for admissible uncertainties are es...

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AI...

Using a non-uniformly distributed aperture radar system for forming a SAR image will result in data correlations between the SAR image resolution cells. Thus, this requires that a more robust filter than the Matched Filter, i.e. the MMSE or Wiener Filter to be used in the receiver processing. As the Wiener Filter involves a computationally expensive matrix inverse operation, it can be avoided b...

On theory of multisensor state fusion estimation, more research is a single rate synchronization problem, however, it is the multirate asynchronous problem often encountered in practice. Therefore, research on the state fusion estimation of asynchronous multirate multisensor have more practice application value. In this paple, by expand the dimension of the system state and measurements and by ...