A MULTISENSOR-MULTITARGET DATA ASSOCIATION ALGORITHM FOR HETEROGENEOUS SENSORS

The problem of associating data from three spatially distributed heterogeneous sensors, each with a set of detections at the same time is dealt with here. The sensors could be active (three-dimensional or two-dimensional radars), or passive (electro-optical 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 sensors to identify the real targets, and to obtain their position estimates. Mathematically, this (static) measurement-target association problem leads to a generalized 3-D assignment problem, which is known to be NP-hard. We discuss an algorithm suited for estimating the positions of a large number of targets in a dense cluster using a fast, but nearly optimal 3-D assignment algorithm. Performance results on several representative test cases with 64 targets solved by the algorithm are presented.