Analysis of Positioning Accuracy in Passive Localization Systems Using Extended Kalman Filter

In this paper, we explore challenges in passive localization systems and propose an Extended Kalman Filter (EKF) based approach to enhance positioning accuracy. We provide detailed analysis of the EKF mathematical model, including state transition matrices and observation equations, and demonstrate its implementation in passive localization scenarios through sensor fusion techniques. The implementation typically involves linearizing nonlinear system dynamics using Jacobian matrices and recursively updating state estimates through prediction-correction cycles. Furthermore, we examine the advantages of EKF in handling nonlinear systems and its limitations regarding computational complexity and linearization errors. To address these constraints, we propose modification strategies such as adaptive noise covariance tuning and innovation-based validation gates for improved performance across diverse operational environments. Ultimately, this research aims to contribute novel methodologies to passive localization system development, focusing on enhanced accuracy and reliability through optimized filtering algorithms.