An explicit pattern matching assignment algorithm

Sharing data between two tracking systems frequently involves use of an object map: the transmitting system sends a frame of data with multiple observations, and the receiving system uses an assignment algorithm to correlate the information with its local observation data base. The usual prescription for this problem is an optimal assignment algorithm (such as JVC or auction) using a cost matrix based upon chi-squared distances between the local and remote observation data. The optimal assignment algorithm does not actually perform pattern matching, so this approach is not robust to large registration errors between the two systems when there exist differences in the number of observations held by both systems. Performance of a new assignment algorithm that uses a cost function including terms for both registration errors and track to track random errors is presented: the cost function explicitly includes a bias between the two observation sets and thus provides a maximum likelihood solution to the assignment problem. In practice, this assignment approach provides near perfect assignment accuracy in cases where the bias errors exceed the dimension of the transmitted object map and there exist mismatches in the numbers of observations made by the two systems. This performance extends to many cases where the optimal assignment algorithm methodology produces errors nearly 100% of the time. The paper includes the theoretical foundation of the assignment problem solved and comparison of achieved accuracy with existing optimal assignment approaches.