"Model-guided improvements in shipboard IRST discrimination algorithms," F. J. Iannarilli, Proc. IRIS Symp. on Targets, Backgrounds, and Discrimination, (1994).
Discrimination of anti-ship missiles (ASMs) using Shipboard Infrared Search and Track (SIRST) systems against maritime clutter is a challenge receiving substantial current effort. The intrinsic limits on signal information content in the passive IR militate for new approaches that transcend classical methods which emphasize clutter characteristics and utilize overly simplistic target signal representations. Such methods are brittle in the face of "real-world" first-order and additionally to potentially significant "second-order" target signal behaviors. This paper presents initial findings of relative discrimination performance improvement for newly developed algorithms which place their emphasis on target ASM signal characteristics. Specifically, a Track-Before Detect (TBD) algorithm, augmented to accommodate and/or exploit target intensity fluctuations, is compared via simulation to the baseline TBD. The significance of the results is that the subject class of algorithms may be generalized to: (a) accommodate second-order target behaviors which degrade performance of "uninformed" algorithms; and (b) exploit atypical behaviors without requiring them to occur.
The identification and characterization of these target behaviors has been greatly facilitated with the recent introduction of an "end-to-end" "Shipboard IRST Engagement Model" (SIRSTEM), developed by Aerodyne Research and sponsored by ONR. SIRSTEM complements the existing "IRTool" by Arete Associates, currently in use by the SIRST development community, by providing a high fidelity time-dependent target signature modeling capability. This capability includes the effects of sea and near-horizon backgrounds and refractive raybending. The paper additionally presents the time dependent characteristics which differentiate likely target signals from background clutter, as ascertained using SIRSTEM.