Bernoulli filtering on a moving platform

This paper considers the problem of tracking a target - which might or might not exist - from a platform whose position is not known perfectly and might contain substantial time dependencies. Most single and multi-target tracking algorithms implicitly or explicitly assume that the location of the sensor platform system is known perfectly. However, in practice the location of sensing platforms is often estimated, usually by fusing a set of sensor measurements from different sources. As a result, the error in the platform estimates could be significant and time correlated. These difficulties are compounded in single and multi-target tracking problems when the existence of a target is not guaranteed. In this paper, we consider the problem of tracking at most a single target from a poorly-localized UAV. We develop a formulation of the Bernoulli filter which incorporates both the target state and the state of the platform. However, because the dimension of the state is relatively large, we develop a suboptimal algorithm which, through neglecting the use of track information to improve the quality of the platform estimate, scales in a manner very similar to that of a conventional Bernoulli filter. The implementations of the different algorithms are tested in a simulation scenario of a UAV performing safety monitoring of a convoy.