A Newborn Track Detection and State Estimation Algorithm Using Bernoulli Random Finite Sets
In multi-target tracking (MTT) problems, there are many important issues that affect performance, including statistical filtering, measurement-target association, and estimating the number of targets. While newborn target detection and state estimation should also be considered as important factors in MTT, only a few studies have addressed these topics. In this paper, a novel newborn track detection and state estimation method is proposed using the concept of Bernoulli random finite sets. The posterior finite set statistical probability density function (FISST PDF) of a newborn target is analytically derived, and a tractable implementation scheme is proposed using importance sampling. Finally, the validity of the proposed method is demonstrated via integration with a Gaussian mixture probability hypothesis density (GM-PHD) filter and subsequent application to MTT problems.