A BOT algorithm based on sliding window n-step forward prediction

Bearing-only tracking (BOT) involves nonlinearity. Extended Kalman filter (EKF) is usually employed for the advantages of efficient computation and real time operation. However, the observability badly declines at a high observation rate, which degrades the filtering performance. In order to address this issue, a BOT algorithm based on sliding window n-step forward prediction is proposed. It enhances the observability effectively, and improves the performance of BOT filter at a high observation rate. Target tracking experiments under different scenarios show that the proposed algorithm has good accuracy and stability, and is suitable for application at a high observation rate.