## 7 Future Works  
Based on the technical notes, observations and analysis made in this survey, it is important to select the best next move in this field with highly innovative impacts. Therefore, future possible directions to overcome RFI detection and tracking problems that may impact LEO SatCom and Global Navigation systems could be summarized in the following points:  
* The development of new numerical methods and non-linear approximation (deterministic and stochastic) integration carrying out faster convergence and numerical stability especially for the geolocation of stationary RFI.
* The development of adaptive and learning monitoring of RFI signals parameters with simultaneous state and parameter learning algorithms, such as Gaussian processes, supervised and unsupervised techniques, etc.
* The development of machine learning and artificial intelligence algorithms such as: reinforcement learning and information reinforcement learning (based on information theoretical learning) with partial knowledge or full unknown models for RFI detection, identification and tracking.
* In addition, robust techniques such as the GHKF-H infinite presented and proposed should be optimized by using Fuzzy Logic for parameter tuning and other RL algorithms for best reactions to the environment uncertainty. To the best of our knowledge, this has not been done before in this field.
* Another issue is the development of real-time implementation of all these algorithms, such as defining and classifying the best performances in real-time and their respective feasibility.To achieve this kind of classification, it might be interesting to propose a computational time complexity for each algorithm for each case study: stationary target, dynamical target, maneuvering target, stationary sensors network, mobile sensors network, LEO satellite sensors network.
* More complex problems could be addressed by assuming multiple RFI-targets instead of only one, with multiple combinations of LEO satellite networks. The problem would be similar to multiple target tracking with only partial knowledge and uncertain data association algorithms to be developed.
* An original and interesting problem would be optimal sensors placement, or optimal constellation selection for optimizing RFI-targets detection and tracking using \(H_{\infty}\) and Gauss Hermite Quadrature filters package algorithms to overcome high non-linearity degrees and sensor locations uncertainty.  
In the end, an additional focus on highly accurate quadrature Kalman filters derived into stochastic forms could be the best next step to increase the accuracy of target state estimation, as well as the best estimate of RFI signals detected in the environment.  
Conceptualization, A.E. and H.B.; methodology, A.E.; validation, A.E., H.B., G.A.E. and R.L.J.; writing--original draft preparation, A.E.; writing--review and editing, H.B. and G.A.E.; supervision, R.L.J. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ) as well as four main strategic partners, namely, Thales, Telesat, Vigilant Global, and ATEM as a part of the AVIO-601 project at Laboratory of Space Technologies, Embedded System, Navigation and Avionic (LASSENA) of Ecole de Technologie Superieure (ETS).  
Not applicable.  
Copyright was obtained from IEEE for Figures 4b, 6b, 17 and 20).  
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The authors declare no conflict of interest.