Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

Abstract

The precision geolocation and target tracking problem has been addressed in this paper using High-Degree Non-linear Filtering based on hybrid Time Difference of Arrival (TDOA), Frequency Difference of Arrival (FDOA) measurements using Low Earth Orbit (LEO) satellite with slant range. In order to update the noise covariance and estimation process at each measurement, the Gauss Hermite H ∞ Filter based on hybrid TDOA/FDOA geolocation measurements are proposed in this work. Numerous scenarios with the different rotation speed of Radio Frequency (RF) emitter has been considered. Multi LEO satellites have used to estimate and track the location of the unknown Unmanned Aerial Vehicle (UAV) under uncertainties of measurements. The uncertainties of measurements have been considered because the position and velocity of sensors are not fixed, which may affect the emitter location estimation measurements. The Cramer-Rao Lower Bound (CRLB) is used as a metric for measuring and analyzing the performance of the H ∞ /GHKF 3 rd degree and H ∞ /GHKF 5 th degree algorithm, as well as compare it with state-of-the-art algorithms. The simulation results of the proposed algorithm indicate that the significant improvement in performance for example, 10% based on TDOA, 40% for FDOA, and 50% on TDOA/FDOA have been achieved.

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

“…Figure 9a shows the geometry of the geolocation technique based on the TDOA and FDOA measurements. Figure 9b shows the CRLB comparison between TDOA, FDOA, and the hybrid (TDOA/FDOA) measurements [21]. The cross ambiguity function (CAF) is used to determine the time of the emitted signal of TDOA and FDOA using at least two sensors.…”

“…The improvement in the emitter geolocation method using the Tri-combination of geolocation measurements was the authors' own elaboration, and they compare it to a hybrid measurement, which was achieved in [21]. Figure 11 illustrates the CRLB verification for combining TDOA/FDOA/DDR and comparing it with the hybrid TDOA/FDOA as well as TDOA and FDOA individually.…”

“…In Figures 34 and 35, it is interesting to observe how estimation accuracy was improved using H ∞ mixed with Gauss Hermite Quadrature Kalman filters in regards to the geolocation and target tracking based on TDOA/FDOA measurements derived and implemented by the authors [21]. Table 4 displays a comparison between different filters employed in the optimal state estimation for an emitter source.…”

“…From the methods and approaches mentioned, it is observed that the current interest in using geolocation and target tracking approaches for RF emitters is increased rapidly. In response to requests imposed by advanced applications that need to geolocate and track the RF emitter at high speeds of movement and rotation, Quadrature Information Kalman Filters (QIKFs) and Gauss-Hermite Quadrature Filter (GHQF), as well as H ∞ , have been developed to solve the non-linearity and uncertainty problem [21][22][23][24]. To process such complex algorithms, we have started by introducing a summary of the most popular methods for geolocation and target tracking [12,13,25].…”

“…Based on what was introduced, the main contribution of this survey is presenting the state-of the-art of conventional methods and techniques, compared with more advanced and modern approaches in geolocation and tracking that were developed under uncertainty, biases and coloured noises [21,22,30]. In the end, the original application related to LEO signals of opportunity was also addressed and discussed by providing the readers with several ways to improve the existing methods and solve new problems in theory and practice.…”