A Multisensor-Multitarget Data Association Algorithm for Heterogeneous Sensors

In this paper we present an algorithm to solve the static problem of associating data from three spatially distributed heterogeneous sensors, each with a set of detections at the same time. The sensors could be active (3D or 2D radars) or passive (EO sensors measuring the azimuth and elevation angles of the source). The source of a detection can be either a real target, in which case the measurement is the true observation variable of the target plus measurement noise, or a spurious one, i.e., a false alarm. In addition, the sensors may have nonunity detection probabilities. The problem is to associate the measurements from the sensors to identify the "real" targets, and to obtain their position estimates. Mathematically, this (static) measurement-target association problem leads to a generalized three-dimensional (3-D) matching problem, which is known to be NP-hard. In this paper, we present a fast, but near-optimal 3-D matching algorithm suited for estimating the positions of a large number of targets (≫50) in a dense cluster for real-time applications. Performance results on several representative test cases solved by the algorithm are presented.