This paper considers the passive-sensor data association problem based on multi-dimensional assignment (MDA), a prerequisite for data fusion. The S-D algorithm has been shown to be effective for solving the MDA problem. The bottleneck of the S-D algorithm lies in its cost computation, which consumes about 95%—99% of the CPU times. Since the number of costs in the MDA problem increases exponentially with the number of sensors, the S-D algorithm becomes quite inefficient when a large number of sensors are used. We propose an efficient data association technique, “S0-D+Seq(2-D)” algorithm, which decomposes the original problem to an S0-dimensional assignment and several 2-dimensional assignments. The S0-D+Seq(2-D) algorithm yields a total number of costs which only increases quadratically with the number of sensors. Simulation results show that the S0-D+Seq(2-D) algorithm achieves a significant reduction in CPU time compared to the S-D algorithm with similar association qualities.