In this paper we describe a novel data association, termed m-best SD, that determines in O(mSkn3) time the m-best solutions to an SD assignment problem. THis algorithm is applied to the following problem. Given line of sight measurements from S sensors, sets of complete position measurements are extracted, namely, the 1st, 2nd,..., m-th best sets of composite measurements are determined solving a static SD assignment problem. Utilizing the joint likelihood functions used to determine the m-best SD assignment solutions, the composite measurements are then quantified with a probability of being correct using a JPDA-like technique. Lists of composite measurements from successive scans, along with their corresponding probabilities, are then used in turn with a state estimator in a dynamic 2D assignment algorithm to estimate the states of the moving targets over time. The dynamic assignment cost coefficients are based on a likelihood function that incorporates the 'true' composite measurement probabilities obtained from the (static) m-best SD assignment solutions. We demonstrate this algorithm on a multitarget passive sensor track formation and maintenance problem, consisting of multiple time samples of line of sight measurements originating from multiple synchronized high frequency direction finding sensors. Another significance of this work is that the m-best SD assignment algorithm provides for an efficient implementation of a multiple hypothesis tracking algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses.