Intelligence Kalman Filter Optimization for Delay-Tolerant Networks in Space and Remote Sensing Applications

The practical applications of space and remote structures and operations that have seen development call for a unique efficient and appropriate communication system that is applicable in conditions of limited communication availability. There is an approach called Delay Tolerant Networks (DTNs) that can significantly help counter the problems caused by network delays and interruptions. Under such circumstances, the KF, a highly accurate and efficient recursive estimator, can significantly enhance the functionality of DTNs by recalculating and forecasting the state of the network over time. This research aims to evaluate the effectiveness of the Kalman Filter optimization on the improvement of reliability effectiveness of the DTNs, especially in space and other remote connections. As a result, through the usage of KF-based algorithms, the present research assumes the task of enhancing c routing, resource allocation, and error correction techniques when delays and communication uncertainties occur in a telecommunication system. The suggested optimization framework incorporates the dynamic nature of the network and delivers flexible solutions to preserve good data throughput at unfavourable conditions. Based upon simulative analysis, both in terms of quantitative and qualitative analysis the study assesses the performance enhancement in terms of PDR, latency and energy efficiency of KF-optimized DTNs to conventional methods. The results show that the proposed Kalman Filter optimization can solve major issues in space-based and remote communication networks and contribute to improved and increased scalability of systems in important areas of application, such as satellite communication and disaster relief.