In this paper we describe computationally efficient assignment-based algorithms to solve the data association
problem in synchronous passive multisensor tracking systems. A traditional assignment-based solution to this
problem is to solve the measurement-to-measurement association using multidimensional (S-dimensional or SD
with S sensors) assignment formulation and the measurement-to-track association using two-dimensional
assignment formulation. Even though this solution has been proven to be effective, it is computationally very
expensive. One of the reasons is that in calculating the assignment cost of each possible candidate association one
requires to find the maximum likelihood (ML) estimate of the unknown target state. The algorithms proposed in
this paper use prior information of the targets that are being tracked to reduce the requirement for the costly ML
estimation. The first algorithm is similar to the traditional two step technique except that it uses the predicted
track information to avoid building the whole assignment tree in the measurement-to-measurement association.
In particular, based on the predicted track information first validation gates are constructed for every target.
Then, when forming the assignment tree, only the branches connecting measurements that satisfy the validation gate requirement are constructed. The second algorithm is a one-step algorithm in that it directly assigns the measurements to the tracks. We pose the data association problem as an (S + 1)-D assignment with the first dimension being the predicted state information of the tracks, and the rest of the S dimensions are the lists of measurements from the sensors. The costs of each possible (S + 1)-tuple are calculated based on the predicted track information, hence, the requirement for an ML estimate is eliminated. Further, we show that when the target maneuvers are not very high, and when the sensor measurements are uncorrelated the (S+1)-D assignment approximately decomposes into S individual 2-D assignments, resulting in huge computational savings.