Message passing based multitarget tracking with merged measurements

This paper considers the problem of multitarget tracking (MT) under situations where sensors have limited resolution, which leads to the presence of merged measurements (MMs). In general, an algorithm for MT under MMs can be derived by extending its standard MT counterpart which assumes that each measurement can come from at most one target. However, such an extension is by no means trivial due to the fact that one must consider data association between target groups to measurements, which results in exponential computational increasing along with the number of targets. In order to address such a difficulty, this paper proposes to adopt the message passing (MP) algorithm, and a new factor graph is constructed for MT under MMs. Then the sum–product algorithm (SPA) and max-sum algorithm (MSA) is jointly exploited for belief propagation, where the SPA is adopted for calculating the messages used for prediction and update, and the MSA is employed for efficiently perform data association. The analytical Gaussian mixture (GM) implementation is also devised for the proposed algorithm. Computational burden analyses show that the computational complexity of proposed algorithm scales linearly with respect to the number of targets and measurements. The performance of proposed algorithm is demonstrated via simulations.