Data association with ambiguous measurements

We address the problem of tracking a single object in the neighborhood of several other closely spaced, similar objects where the sensor used to do the tracking may randomly measure the wrong object. Unlike many tracking scenarios, there is no other environmental clutter producing additional erroneous measurements. The objects move together, and the sensor provides one measurement at every time step, either due to the object of interest or due to one of the other similar nearby objects. This situation of having a "mixed" set of measurements of unknown origin occurs in real world systems. While we consider the mixed-measurement problem in an example scenario, the algorithms developed can be applied to any number of associated systems with little alteration.