Tracking of spawning targets with multiple finite resolution sensors

In this paper tracking multiple spawning targets with multiple finite-resolution sensors is presented with emphasis on the measurement-to-track association with possibly unresolved measurements. The goal is to initialize new tracks of spawned targets before they are resolved from the mother platform´s so that one has the ability to carry out early discrimination when they become resolved. The multiple scan data association problem is first formulated as a multidimensional assignment problem with explicit new constraints for the unresolved measurements. The top M hypotheses tracking (TMHT) is presented where the state estimates and their covariances are modified based on the M best hypotheses through the assignment solutions. A modification to the assignment problem is developed that leads to a linear programming (LP) where the optimal solution can be a noninteger in [0, 1]. The fractional optimal solution is interpreted as (pseudo)probabilities over the N-1 frame sliding window. The best hard (binary) decision assignment solution and the M best via TMHT are compared with the soft decision (LP) solution for two-dimensional tracking scenarios with various sensor configurations. Based on the simulation results, the soft assignment approach has better track maintenance capability than the single best hard assignment and a performance close to TMHT. Its computational load is slightly higher than the single best hard assignment an much lighter than TMHT.