Optimization of multiframe target detection schemes

We optimize the performance of multiframe target detection (MFTD) schemes u nder extended Newman-Pearson (NP) criteria. Beyond the per-track detection performance for a specific target path in conventional MFTD studies, we opt imize the overall detection performance which is averaged over all the pote ntial target paths. It is shown that the overall MFTD performance is limite d by the mobility of a target and also that optimality of MFTD performance depends on how fully one can exploit the information about the target dynam ics. We assume a single target situation and then present systematic optimi zation by formulating the MFTD problems as binary composite hypotheses test ing problems. The resulting optimal solutions suggest computationally effic ient implementation algorithms which are similar to the Viterbi algorithm f or trellis search. The optimal performances for some typical types of targe t dynamics are evaluated via Monte-Carlo simulation.