Abstract
This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking.
Highlights
Airborne surveillance of moving ground targets is one of important capabilities of manned or unmanned aircraft for both military and civil applications such as search and rescue, border patrol, and infrastructure protection, in which the use of airborne ground moving target indicator (GMTI) radar system is of special interest
Considering a tracked object could manoeuvre with different movement types, the interactive multiple model (IMM) method was applied for state estimation with multiple state models [4]
These geographic information and multiple modes are incorporated into a constrained variable structure multiple model particle filter algorithm, aided by the particle swarm optimisation for accurate GMTI tracking
Summary
Airborne surveillance of moving ground targets is one of important capabilities of manned or unmanned aircraft for both military and civil applications such as search and rescue, border patrol, and infrastructure protection, in which the use of airborne ground moving target indicator (GMTI) radar system is of special interest. In [9], an interactive multiple model auxiliary particle filtering (IMMAPF) algorithm was applied while considering the road width and multiple state models which describe the different vehicle movements. In order to deal with the multiple movement types of a manoeuvring vehicle, multiple movement modes are considered and related state models are applied for the vehicle’s movement description in a particular terrain, instead of considering only a single movement mode as in [10] and [11] These geographic information and multiple modes are incorporated into a constrained variable structure multiple model particle filter algorithm, aided by the particle swarm optimisation (denoted as C-VSMM-PSO-PF) for accurate GMTI tracking.
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