Linear-correction Extended Kalman Filter for Target Tracking Using TDOA and FDOA Measurements

Abstract

Abstract-This

Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

“…To estimate the location and tracking of the RF emitter, Kalman filter (KF) and its expansion based on conventional geolocation measurements has been proposed in numerous studies [28][29][30]. In [31], the Extended Kalman filter (EKF) based on TDOA/FDOA was proposed to estimate target position and tracking. The main disadvantage of the EKF is that it relies on pre-estimation to estimate the variance of process and measurement noises, which may be difficult to accurately track the RFI position when predicting with a significant deviation from the actual values (measurement) [32].…”

“…Referring to the various derivations of multiple Gaussian-point filters, and through the following state-of-art of authors in [36], we can conclude a uniform algorithm of different square Kalman filters. The algorithm achieved will be a difference of others in how the points are, and the weights given in (31) are calculated [41]. The Gauss-Hermite rule can be created using a Gaussian weighted integral [49,50].…”

“…Using the algorithm of higher-degree filtering, we can use square conversions, cubes, and convert various sigma points to create points and weights. The numerical integration methods of the quadrature integral given in (31) are suitable when the measurement noises with covariance matrices Q k−1 and R k of process state and localization, respectively. The considered algorithm introduces three steps of the Gauss-Hermite quadrature filtering.…”