TDOA-based adaptive observer trajectory optimisation algorithm for tracking in passive coherent location system

Abstract

In the passive coherent location system with fixed observers, the target tracking performance is limited by the inappropriate localisation geometry. This study proposes an adaptive observer trajectory optimisation (AOTO) algorithm in terms of minimising the geometric dilution of precision (GDOP) based on the time difference of arrival (TDOA) measurements gained from multiple emitters. The target states are estimated by the extended Kalman filter (EKF) algorithm. Simulation results demonstrate that the AOTO algorithm can effectively improve the tracking accuracy compared with the corresponding tracking approaches with fixed observers.

Keywords

• kalman filters
• target tracking
• nonlinear filters
• time-of-arrival estimation
• observers
• direction-of-arrival estimation
• minimisation
• tdoa-based adaptive observer trajectory optimisation algorithm
• passive coherent location system
• fixed observers
• target tracking performance
• extended kalman filter algorithm
• aoto algorithm
• tracking accuracy
• tracking approaches
• localisation geometry
• geometric dilution minimization
• target state estimation
• time difference of arrival measurements
• multiple emitters