Auxiliary particle Bernoulli filter for target tracking

Target tracking is a popular topic in various surveillance systems. As a data association free method, the Bernoulli filter can directly estimate target state from plenty of uncertain measurements. However, it is not obvious for existing Bernoulli filters to select proposal distribution with small variance of weights. To address this problem, a novel auxiliary particle (AP) Bernoulli filter and its implementation are proposed in this paper. We employ the AP method in the Bernoulli filtering framework in order to choose robust particles from a discrete distribution defined by an additional set of weights, which reflect the ability to represent measurements with high probability. Limitation to the number of particles, the promising particles are used to propagate by extracting indices. On the other hand, the particles without significant contribution to approximation are discarded. In such case, the computational complexity of this filter is reduced. With the unscented transform (UT), the dynamics of maneuvering target are effectively estimated. The simulation results show advantages in comparison to the standard Bernoulli filter for general target tracking.