Modified input estimation technique for tracking manoeuvring targets

Abstract

A new input estimation (IE) model for problems in tracking manoeuvring targets is proposed. The proposed model is constructed by combining the two models of uncertainties, Bayesian and Fisher. The conventional model, which describes targets with manoeuvre, is based on the state vector of target position and velocity. The acceleration is treated as an additive input term in the corresponding state equation. The proposed method is a Kalman filter-based tracking scheme with the IE approach. The proposed model is a special augmentation in the state-space model which considers both the state vector and the unknown input vector as a new augmented state vector. In the proposed scheme, the original state and acceleration vectors are estimated simultaneously with a standard Kalman filter. The proposed tracking algorithm operates in both the non-manoeuvring and the manoeuvring modes and the manoeuvre detection procedure is eliminated. The theoretical development is verified by simulation results, which also contain some examples of tracking typical target manoeuvres. The results are compared with a traditional IE method. A comparison based on the Monte-Carlo simulation is also made to evaluate the performances of the proposed method in three scenarios: low, medium and high manoeuvring target.