V3I1P23

Application of Decoupled Kalman Filtering for Angular State Estimation of Snake-Like Maneuvering Targets Under Noisy Environments

Nguyen Manh Hung^1*

Abstract

This paper presents a method applying a decoupled Kalman filter to estimate seven angular kinematic parameters relevant to missile guidance against a nonlinear maneuvering target in the presence of measurement noise. These parameters include: line-of-sight (LOS) angle, LOS rate, LOS angular acceleration, missile body angle, missile body angular rate, antenna angle, and antenna angular rate. The target model is constructed based on a highly nonlinear “snake-like” motion, reflecting strong maneuvering characteristics commonly seen in modern evasive tactics. The measured signals are synthesized by adding white Gaussian noise with specified variances for each variable. The Kalman filter system is designed in a decoupled structure consisting of three sub-filters. Filter performance is evaluated through quantitative metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), and Standard Deviation (STD) over time. Simulation results confirm the method’s high accuracy and effectiveness under noisy measurements and strong target maneuvers, while also demonstrating computational advantages over the full Kalman filter.

Keywords:

Decoupled Kalman Filter, Line-of-Sight, Missile Body Angle, Antenna Angle, State Estimation, Noisy Measurements, RMSE, MSE, STD