Iterative Smooth Variable Structure Filter for Parameter Estimation

The Continuous-time Smooth Variable Structure Filter

State and parameter estimation techniques are important tools which provide accurate estimates of system states. This is important for the reliable and safe control of mechanical and electrical systems. Most estimation techniques are derived in discrete-time, due to the wide use of digital computers. However, continuous-time derivations do exist, and are particularly useful for studying estimat...

The Smooth Particle Variable Structure Filter

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding mode concepts, form...

Neural Networks and the Smooth Variable Structure Filter

Robust Visual Simultaneous Localization and Mapping for MAV Using Smooth Variable Structure Filter

The work presented in this paper is a part of research work on autonomous navigation for Micro Aerial Vehicles (MAVs). Simultaneous Localization and Mapping (SLAM) is crucial for any task of MAV navigation. The limited payload of the MAV makes the single camera as best solution for SLAM problem. In this paper the Large Scale Dense SLAM (LSD-SLAM) pose is fused with inertial data using Smooth Va...

Low-Complexity Iterative Sinusoidal Parameter Estimation

Sinusoidal parameter estimation is a computationally-intensive task, which can pose problems for real-time implementations. In this paper, we propose a low-complexity iterative method for estimating sinusoidal parameters that is based on the linearisation of the model around an initial frequency estimate. We show that for N sinusoids in a frame of length L, the proposed method has a complexity ...