Wind Turbine Micro-Doppler Prediction Using Unscented Kalman Filter

Abstract

With the increasing focus on green energy, wind turbines (WTs) have become common occurrences in most landscapes. The presence of WTs in the field of view of a radar will create very complicated clutter in a received signal. One of the major reasons for the complicated nature of WT clutter is the fact that it will consist of a wide band of Doppler frequencies. In addition, the Doppler band of frequencies keep changing based on wind speed and direction The tracking of dominant Doppler frequencies is one of the most important steps in the process of filtering WT clutter. In this work, we present an unscented Kalman filter (UKF) based solution to track the dominant Doppler frequencies in WT echoes. We have shown the efficiency of our algorithm by applying it to a wide range of real-measured data collected from various locations in Europe.INDEX TERMS Adaptive Kalman filtering, Adaptive signal processing, Doppler effect, filter, radar I. INTRODUCTION

A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model

“…To solve the problems associated with the extended Kalman filter, in 1995, Julier proposed the Unscented Kalman filter (UKF) [12]. The main difference between the EKF and UKF is the model linearization method [13]. The core of the UKF algorithm is the UT transform [14], which is used to linearize a nonlinear function by nonlinearly influencing the sampling points [15].…”