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.