Comparison of robustized assignment algorithms

Several assignment methods are compared in terms of problem size, computational complexity and misassignment as a function of sparsity and gating. Specific real world applications include multi-target multi-sensor tracking/fusion and resource management with sparse cost matrices. The cost matrix computational complexity is also addressed. Both randomly generated cost matrices and measured data sets are used to test the algorithms. It is shown that, both standard and some new greedy, assignment algorithms significantly degrade in performance with fully gated columns and/or rows. However, it is shown that it is possible to modify specific algorithms to regain the lost optimality.