Data Fusion for Missile Defense

Information Extraction & Transport (IET), Inc., proposes an innovative yet practical approach to Ballistic Missile Defense System (BMDS) Midcourse Defense Segment (MDS) object tracking and discrimination. The proposed approach is to integratemultiple-sensor MDS object tracking and discrimination capabilities, making the best use of the MDS interception opportunities utilizing all the available information provided by a diverse set of MDS ground-, sea-, and space-based sensors. Reliable objectdiscrimination may be obtained by only a selective set of sensors (such as XBR) at a relatively short range. However, to relay the accurate discrimination information to in-flight MDS interceptor elements, the entire MDS tracking system must performoptimal or near-optimal real-time multiple-object tracking. For that purpose, we propose to model the feature signals by stationary Gauss-Markov stochastic processes, particularly for narrow-band radar and infrared/electro-optical (IR/EO) sensor featuresignals, to enhance track-to-measurement association performance to obtain higher track purity, which is essential for any object discrimination algorithm to make correct decisions. IET also proposes to use multiple-sensor-invariant feature parameters toenhance track-to-track association (including Target Object Map) performance, so that all the essential object discrimination information accumulated by all the MDS sensor assets can be used in the MDS intercept opportunities. IET expects the successfulimplementation of the algorithms detailed in this proposal to enhance both track-to-measurement and track-to-track association performance thereby improving multiple-sensor midcourse object discrimination capability, and, ultimately, the ability of themidcourse interceptor to correctly identify and kill lethal objects in a shorter time. Beyond the BMD applications, our feature-aided tracking approach can be applied to a wide range of target tracking, surveillance, monitoring, and target recognition andidentification problems. The potential applications include military, law-enforcement, and facility and home security systems, using a wide range of distributed sensors.