Guo Fu-cheng

Maneuvering emitter tracking by a single observer using IMM algorithm based on unscented transformation

The unscented transformation (UT) based interacting multiple model (IMM) algorithm is explored to handle maneuvering emitter tracking by a single passive observer, which processes Doppler frequency changing rate and angle measurements concurrently, thus the observer maneuver is not required. Computer simulations are performed to compare the unscented Kalman filter-interacting multiple model (UKF-IMM) estimator with the traditional extended Kalman filter-interacting multiple model (EKF-IMM) estimator. Simulation results reveal that the UKF-IMM estimator is more stable and effective

A new method of bearings-only target localization based on parameter transformation

Bearings-only target localization problem is a highly non-linear parameter estimation problem. From the theorem of parameters transformation and particle kinematics, a new parameters transformation filter (PTF) for bearings-only (BO) localization of fixed target in two-dimensional space and three dimensions space was proposed in this paper. An invertible mapping function was found, which transformed the nonlinear filtering problem of bearings-only to a linear filtering problem. Simulation results show that it has a lower localization error than the generally used method known as the modified gain extended Kalman filter (MGEKF) in bearings-only localization example.

Maneuvering Emitter Tracking by a Single Passive Observer Using SRUKF Based IMM Algorithm

The interacting multiple model (IMM) algorithm is combined with the square root unscented Kalman filter (SRUKF), and explored to track maneuvering emitter with a single passive observer, which processes Doppler frequency changing rate and angle measurements concurrently, thus the observer maneuver is not required. Computer simulations are performed to compare the SRUKF based IMM(SRUKF-IMM) tracker with the traditional extended Kalman filter based IMM (EKF-IMM) tracker. Simulation results reveal that the SRUKF-IMM tracker is more stable and effective.