The formulation of the sequential sliding innovation filter and its application to complex road maneuvering

Abstract

This study presents the development of a new filter, the sequential sliding innovation filter (SSIF), designed for estimating quantities of interest from noisy measurements. The SIF is formulated in a sequential manner, allowing for multiple updates of estimates, making it well-suited for systems with multiple measured states. The filter is applied to an unmanned ground vehicle (UGV) maneuvering in 2-D path in this study, and the results demonstrate that the SSIF outperforms conventional filter and Kalman Filter (KF) in terms of accuracy and efficiency. The SSIF has the potential for use in signal processing, tracking, and surveillance, making it a valuable tool in various fields.