Sang-Wook Shim

A Track Scoring Function Development for Airborne Target Detection Using Dynamic Programming

Track-before-detect techniques based on dynamic programming have provided solutions for detecting targets from a sequence of images. In its application to airborne threat detection, dynamic programming solutions should take into account the distinguishable properties of objects in a collision course. This paper describes the development of a new track scoring function that accumulates scores for airborne targets in Bayesian framework. Numerical results show that the proposed scoring function has slightly better detection capabilities.

Vision-based long-range target detection using coarse-to-fine particle filter

In this study, we develop a coarse-to-fine particle filter algorithm for track-before-detect in order to track a subpixel-sized, low signal-to-noise ratio target in sensor data. The proposed algorithm enhances tracking performance in the presence of target motion uncertainty and it also maintains the computational load without increasing the number of particles. This coarse-to-fine particle filter, which is newly applied to track-before-detect, has two recursive stages: a coarse stage for extensive searches of the target’s state space and a fine stage that narrows down the tracking results. During the coarse stage, particles are propagated with uniformly distributed noise to compensate for highly nonlinear target motion. The fine stage disturbs the particles filtered from the coarse stage using Gaussian distributed noise. Monte Carlo simulation results using artificial image sequences indicate improved performance with the proposed algorithm when uncertain large frame-to-frame pixel differences are caused by nonlinear target motions such as jittering effects. The algorithm is also applied to the real camera image frames to verify its detecting performance.

Track-Before-Detect Algorithm for Multiple Target Detection

Vision-based collision avoidance system for air traffic management requires a excellent multiple target detection algorithm under low signal-to-noise ratio (SNR) levels. The track-before-detect (TBD) approaches have significant applications such as detection of small and dim targets from an image sequence. In this paper, two detection algorithms with the TBD approaches are proposed to satisfy the multiple target detection requirements. The first algorithm, based on a dynamic programming approach, is designed to classify multiple targets by using a k-means clustering algorithm. In the second approach, a hidden Markov model (HMM) is slightly modified for detecting multiple targets sequentially. Both of the proposed approaches are used in numerical simulations with variations in target appearance properties to provide satisfactory performance as multiple target detection methods.