Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability

Stochastic Modeling for Mobile Manipulators

Mobile manipulators are valuable and highly desired in many fields, especially in industrial environments. However, determining the end effector position has been challenging for scenarios where the base moves at the same time that the arm follows commands to perform specific tasks. Earlier works have attempted to dynamically evaluate the problem of positioning error for mobile manipulators, but there is still room for further improvement. In this thesis, we devise a dynamical model that leverages stochastic search strategies for mobile manipulators. More specifically, we develop a dynamic model that estimates the position of the robot using an Unscented Kalman filter. Simulations using the Robot Operating System (ROS) and Gazebo were carried out to evaluate our model. Our results for the stochastic method show that it outperforms a deterministic approach (spiral search) under specific Kalman filter covariances of the process and observation noises. Compared to the state of the art, our proposed approach is more robust and efficient, proving to work under different arrangement scenarios with significant better performance