## 6 Conclusions  
The most conventional and recent techniques of geolocation and target tracking have been reviewed in this paper. Furthermore, different geolocation algorithms (such TS, MLE, WLS, KF, EKF, AEKF, UKF, GHQF, \(H_{\infty}\), GA, DL, etc.) demonstrated various levels of performances depending on the localization environment and sensors network architecture. Different scenarios and cases could be classified and carried out; static beacons (sensors) with stationary RFI detection and localization, static beacons with mobile RFI detection and localization, as well as mobile sensor networks with mobile RFI detection and localization or detection and tracking. Each of the techniques and methods mentioned from basic least square to recursive LS and other variants, in addition to Kalman filter and more sophisticated versions, such as UKF and GHKF, the dynamics of the RFI and non-linearity degrees of the state estimation problem have a direct impact on the algorithm selection and implementation. Each method has its own advantages and drawbacks. Important factors of real-time processing or digital processing present constraints that engineers in this field must adapt to using the most optimized techniques, e.g., the introduction of different optimization techniques, machine learning and artificial intelligence (AI). Depending on the initial conditions, assumptions and scenario limits and constraints, the same technique or method could perform well or poorly, as explained. Especially when selecting the best measurement model and data to be processed, discussions about \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Altitude of** & **Number of** & **Frequency Band** & **Status (2021)** \\
**Constellations** & **Orbit (Km)** & **Satellites** & **Frequency Band** & **Status (2021)** \\ \hline SpaceX- & \multirow{2}{*}{350–550–1150} & 12 + 30,000 & \multirow{2}{*}{Ku, Ka, and V} & \multirow{2}{*}{1735 in Orbit} \\ Starlinks & & & (Upcoming) & \\ Orbcomm & 650 & 50–52 & VHF & Completed \\ Globalstar & 1414 & 48 & L, S, and C & Completed \\ Iridium Next & 780 & 66 & La and Ka & Completed \\ Oneweb & 1220 & 648 & Ku and Ka & 218 in Orbit \\ Boeing & - & 2956 & V and C & Not \\  & & & & yet launched \\ Samsung & - & 4600 & V & Not \\  & & & & yet launched \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Basic information on satellite formation, satellite constellations, principles, satellite altitude, frequency bands, and most popular LEO sat. status of the satellite. Figure 36: Geometry of sensor and target location in 3D using absolute and differential SOP processing from multiple LEO constellations. TDOA, FDOA, AOA, PDOA and multiple combinations of these techniques are possible solutions depending on the sensors network key parameters, nodes, beacons, number of nodes, static, mobile beacons, distance between RFI source and beacons, and nodes, etc. Therefore, in order to develop realistic algorithms with real-time and real environment capabilities and efficiency, uncertainty of the environment, randomised changes, and time varying noises modelling have to be considered in order to fulfill the best performances and especially robustness and adaptive behavior under real conditions. Finally, using novel sources of information and novel signals, called signals of opportunity, represent novel areas of research and investigation. Therefore, Multi UAV-RFI detection and tracking under the LEO satellite sensors network have been considered as one of the most challenging and innovative problems to be solved in the last few decades. One of the strongest filtering algorithms that authors have developed and selected was the Gauss Hermite Quadrature filters package mixed with \(H_{\infty}\) as a potential optimal multiple environment localization and tracking method. Up to today, many real tests and data collections have been made available to perform different algorithms and benchmarks between different approaches.