Christopher S

Data fusion technique combines data from two similar sensors placed in different location in order to reduce the error in filtered state estimate. In this paper, state vector fusion (SVF) and measurement fusion (MF) are used to fuse the bearing measurement and also to fuse the derived heading measurement. The derived heading from bearing measurement increases the accuracy of target state estimate. Here, the Lagrange three point difference (LTPD) method has been proposed to derive heading from the set of bearing measurements. Two sensors with single target scenario are considered and the heading parameters are derived for each sensor. The bearing and derived heading measurements from two different sensors are fused using SVF or MF and then nonlinear Extended Kalman filter (EKF) is used to obtain the optimized state estimate. Simulations have been carried out in order to compare the SVF and MF fusion techniques for the bearing measurements as well as the derived heading parameters using existing centered difference (CD) and proposed LTPD.

In general, bearing measurements combined with the derived heading increases the accuracy of target state estimate in bearing only tracking (BOT). Here, Lagrange five point difference (LFPD) method is proposed for deriving the heading measurements. This method is compared with Lagrange three point difference (LTPD) and centered difference (CD) method by considering the scenario of two sensors tracking a single target. The bearing and derived heading measurements from two sensors are fused using measurement fusion (MF) technique. The nonlinear Extended Kalman filter (EKF) is implemented using fused measurement to obtain the optimized target state estimate. Performance analyses are made between LFPD compared with LTPD and CD by considering without and with fusion technique. Simulation results indicate MF-LFPD performs comparatively better.