## 1 Introduction
Figure 1: Modeling scenario using Orolia LEO Simulator. (**a**) Orolia’s Skydel simulator. (**b**) Proposed Scenario. system's performance, resiliency, and accuracy in complex GNSS space-based applications, also generating low earth orbit simulation [6]. Time of Arrival (TOA), Time Difference of Arrival (TDOA), Frequency of Arrival (FOA), Frequency Difference of Arrival (FDOA), Angle of Arrival (AOA), Received Signal Strength (RSS), and Power Difference of Arrival (PDOA), etc. are electromagnetic propagation parameters that are used by various approached of geolocation, and tracking [7; 8; 9]. The TOA measurement is related to knowing the distance between the source and the sensor and radio propagation velocity, where the radio propagation velocity is well known as \(2.998\times 10^{8}\) m/s. The common lack of synchronization between the source and the receiver is one practical obstacle in TOA measurements. In other meanings, receivers are frequently unaware of the specific start time of source transmission. Because of the uncertainty surrounding the transmission instant start time, all received TOA measurements have a common temporal offset, which can contribute to severe localization accuracy [10]. Subtraction of pairwise TOA measurement results in correlated noise in TDOA. The sensors synchronization is taken into consideration in the TDOA model. In a two-dimensional (2D) scenario, to achieve better precision for TDOA-based geolocation, at least three sensors are needed for receiving similar signals that broadcast from the emitter [11]. The FOA model depends on the carrier frequency, radio propagation velocity, and Doppler shift between emitter and sensor when both or one of them is moving [12]. The FDOA is a difference in pairwise FOA measurement caused by the Doppler shift of the received signal due to differences between sensors and emitter velocities [13]. A signal observation that might be received from a sensor is not enough to compute a better estimation of the emitter source. In the TDOA method, the emitter might estimate over many scattered points of a hyperbola curve. This problem requires increasing the number of sensors or sensing points that mainly target the geolocation measurement of received signals. From that, more than one hyperbola will produce a precise target location estimation [14; 15]. In addition, combining two or more geolocation measurement models will achieve the best measurement of electromagnetic propagation parameters and reduce the sensors or sensing points needed [16; 17; 18; 19; 20; 21]. From the literature review, numerous geolocation and target-tracking algorithms based on electromagnetic propagation parameters have been considered [22; 23; 2]. In [24], the authors have studied the accuracy of emitter geolocation using the CAF approach. They proposed an approach to eliminate mirrored locations in tri-satellite TDOA localization based on TDOA measurements taken at many times to attain precision. In addition, the authors in [25] addressed the problem of stationary ground-based location using one transmitter (emitter) and two airborne mobile receivers. They have explored the effect of spatially multiplexed Multiple-Input-Multiple-Output (MIMO) signals on the CAF calculation of TDOA and FDOA, and the consequent effect on geolocation; this method did not carry out any modeling and experimenting of the scenario before arriving at the solution. Moreover, [PERSON] et al. [20], proposed an algebraic method using combined TDOA and FDOA with a differential Doppler rate that increases the moving source geolocation accuracy. The method proposed by the author relied on the pseudo-linear set of equations and a two-step weighted least squares (WLS) estimator. In [26], the authors proposed a Linear Least Squares (LLS) approach. This study analyzed the performance of the proposed algorithm, which considered four static sensors to geolocate mobile sources using the LLS method based on the TOA/RSS measurement.