Emil Semerdjiev

Application of a Monte Carlo method for tracking maneuvering target in clutter

The Monte Carlo methods provide a possibility for improved sub-optimal Bayesian estimation. In preceding studies the authors have suggested a new implementation of the general bootstrap simulation approach — the bootstrap multiple model (BMM) filter for tracking a maneuvering target. In the present paper this algorithm is further extended for operating in a cluttered environment. Probabilistic data association (PDA), taking into account the possible measurement-to-target association hypotheses, is incorporated into the BMM algorithm to overcome the measurement–origin uncertainty. By simulation the proposed BMM PDA algorithm is evaluated and compared with the well-known interacting multiple model (IMM) PDA filter. The obtained results demonstrate a superior tracking performance of the BMM PDA algorithm at the cost of an increase in computation.

Multiple Hypothesis Tracking Using Hough Transform Track Detector

The Multiple Hypothesis Tracking algorithm (MHT) is an effective algorithm for moving objects detection and tracking.1,2 Few versions of this complex algorithm are described and evaluated in 1,2,4. Its measurement oriented version is considered as the most effective from theoretical point of view, but its practical implementation is limited because of the required significant computational load in cluttered environment. Several techniques minimizing this load were proposed,1,2,4 but they do not provide general solution to these problems. A new problem solution is proposed in this paper. A Hough Transform (HT) track detector is used for preliminary filtering of arriving false alarms (FA). The tracks detected in this way are processed asynchronously with another standard MHT algorithm to include them in the overall MHT scheme. The standard and the proposed MHT-HT algorithm (MHT2-HT) are evaluated and compared in the paper. The proposed algorithm shows remarkably good performance in cluttered environment at the cost of delayed track detection process.

On-Line State Estimation of Maneuvering Objects by Sequential Monte Carlo Algorithm

A stochastic sampling algorithm for recursive state estimation of nonlinear dynamic systems is designed and realized in this study. It is applied to the problem of tracking two maneuvering air targets in the presence of false alarms. The performance of the proposed algorithm is evaluated via Monte Carlo simulation. The results show that the nonlinear Bayesian filtering can be efficiently accomplished in real time by simple Monte Carlo techniques.