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This paper develops an algorithm that can be used to solve the data association problem faced by a surveillance aircraft using Direction of Arrival angle measurements to locate a stationary RF signal source. The algorithm is based on statistical clustering of measurements with clusters being formed using a Mahalanobis distance association criterion. This approach accounts for angle measurement error statistics and avoids the computational complexity of an exhaustive combinatorial assignment. The optimal cluster is the one that maximized the target position log-likelihood function. This cluster is used to compute a target position estimate then removed from the set of measurements. The process is repeated until no additional clusters can be formed. Simulation results are shown where 100 measurements are distributed randomly across 7 target signal sources.