A multisensor-multitarget data association algorithm for heterogeneous sensors

The problem of associating data from three spatially distributed heterogeneous sensors with three simultaneous detections for all three is discussed. The sensors can be active or passive. 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. The sensors may have nonunity detection probabilities. The problem is to associate the measurements from sensors to identify the real targets, and to obtain their position estimates. Mathematically this leads to a generalized 3D assignment problem, which is known to be NP-hard. An algorithm suited for estimating the positions of a large number of targets in a dense cluster, using a fast, but nearly optimal, 3D assignment algorithm, is presented. Performance results for several representative test cases with 64 targets are presented