MLPDA (Maximum Likelihood Probabilistic Data Association) has attracted a great deal of attention as an effective target track extraction method in high false density environments. However, to extract an accelerated target track on a 2-dimensional plane, the computational load of the conventional MLPDA is extremely high, since it needs to search for the most-likely position, velocity and acceleration of the target in 6-dimensional space. In this paper, we propose VG-MLPDA (Variable Gating MLPDA), which consists of the following two steps. The first step is to search the target's position and velocity among candidates with the assumed acceleration by using variable gates, which take into account both the observation noise and the difference between assumed and true acceleration. The second step is to search the most-likely position, velocity and acceleration using a maximization algorithm while reducing the gate volume. Simulation results show the validity of our method.

This paper proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodology. Through several simulations, validity of this algorithm has been confirmed.

A new approach for multi-target tracking in an occlusion environment is presented. In pedestrian tracking using a video camera, pedestrains must be tracked accurately and continuously in the images. However, in a crowded environment, the conventional tracking algorithm has a problem in that tracks do not continue when pedestrians are hidden behind the foreground object. In this study, we propose a robust tracking method for occlusion that introduces a degeneration hypothesis that relaxes the track hypothesis which has one measurement to one track constraint. The proposed method relaxes the hypothesis that one measurement and multiple trajectories are associated based on the endpoints of the bounding box when the predicted trajectory is approaching, therefore the continuation of the tracking is improved using the measurement in the foreground. A numerical evaluation using MOT (Multiple Object Tracking) image data sets is performed to demonstrate the effectiveness of the proposed algorithm.

The effectiveness of random-switching control, by which the switching-noise spectrum is spread and its level is reduced, is briefly described through experimental results. The noise spectrum by random switching is analyzed in general approach including a noise-generation model and a switching function with random process. Taking the normal distribution as an instance, the discussion on the amount of random perturbation is made quantitatively. The validity of the analysis is confirmed experimentally by a series of pulse serving as ideal switching-noise.

A ghost reduction algorithm for multiple angle sensors tracking objects under dual hypotheses is proposed. When multiple sensors and multiple objects exist on the same plane, the conventional method is unable to distinguish the real objects and ghosts from all possible pairs of measurement angle vectors. In order to resolve the issue stated above, the proposed algorithm utilizes tracking process considering dual hypotheses of real objects and ghosts behaviors. The proposed algorithm predicts dynamics of all the intersections of measurement angle vector pairs with the hypotheses of real objects and ghosts. Each hypothesis is evaluated by the residuals between prediction data and intersection. The appropriate hypothesis is extracted trough several data sampling. Representative simulation results demonstrate the effectiveness of the proposed algorithm.