Improved Global Positioning System Receiver using Extended Kalman filter with Hybrid Optimization

Abstract

             For precise timing, positioning, and navigation in a variety of applications, Global Navigation Satellite Systems (GNSS) are crucial. However, adverse conditions including dynamic movement, variable noise statistics, and poor signal reception might make GNSS signal tracking problematic. To mitigate these challenges, the research proposes the adaptive Weighted Strong Extended Kalman Filter as a possible means of enhancing GNSS receiver tracking performance. The approach dynamically optimizes the Kalman filter parameters to improve the accuracy of GNSS receivers in dynamic environments. Setting up the Kalman filter's required parameters, such as the measurement noise covariance, process noise covariance, state covariance matrix, and starting state estimations, is the first stage of the study. This algorithm uses "Chimphopper Hybrid Optimization (ChHO)," a novel hybrid optimization technique, to improve receiver positional accuracy. The proposed hybrid optimization algorithm creates a powerful framework for GNSS receiver performance optimization by building on the benefits of the popular Grasshopper optimization algorithm (GOA) and Chimp optimization algorithm (ChoA). This presents a novel approach that blends cutting-edge optimization approaches with conventional Kalman filter techniques to enhance GNSS receiver positioning capabilities which result in more reliable and resilient GNSS receiver systems.