Radar Data Fusion for Single Integrated Air Picture (SIAP) for Ground-based Midcourse Defense (GMD) - Real-Time Multi-Source Data Fusion (RT-MSDF) Sys

Real-time fusion of data collected from a variety of radars that acquire information from multiple perspectives and/or different frequencies, is being shown to provide a more accurate picture of the adversary threat cloud than any single radar or group of radars operating independently. This assumes that the proper constraints (decision algorithms) are applied to the midcourse ballistic vehicle target acquisition, tracking, and discrimination process. These constraints include the application of robust target tracking, distributed multi-source data fusion, and clutter rejection algorithms as well as ensuring spatial and temporal registration of radars, efficient real-time data throughput within and between sensor platforms, enhanced processing speed and capacity, and sensor calibration. Distributed, decision-level fusion using intelligent systems and multi-modality sensor inputs can provide additional advantages for midcourse ballistic target tracking and discrimination. The goal of the data fusion process is to operate on a combination of sensor measurements, features, track states, and object type and identification likelihoods to produce a SIAP of the air space to a high degree of accuracy. Technologies that enable this synergistic fusion and interpretation of data at several levels from disparate GMD radars and other sensors should enhance system acquisition, tracking and discrimination of threat objects in a cluttered environment and provide enhanced battle space awareness. This effort is to develop algorithms, software, and/or hardware necessary to collect, process, and fuse information from multiple radars (either at the same or different frequency) to form a more accurate SIAP. The approach leverages and extends the results of complementary research that is ongoing to provide effective Data Fusion and Registration (DataFusR) technologies, which will enable sensor and battlespace systems to autonomously perform real time registration and fusion of multiresolution radar data for precision target geolocation and identification. The proposed RT-MSDF concept extends the more general solution recently developed by ANDRO for this problem, which applies a multi-source data fusion (MSDF) simulation approach to automatically select the most viable registration/fusion scheme(s) to achieve the objectives of forming an SIAP. This R&D will focus on the following additional areas: XG distributed aperture radar, ultra-wideband (UWB) imaging, web-based broadband sensor fusion, and sparse band processing (SBP) technologies for multiresolution target feature (frequency/waveform diversity) exploitation; robust centralized, distributed, and composite target tracking algorithms; multi-modality (multispectral radar, IR, EO, and other) sensor data registration and fusion (including, but not limited to novel extensions of Bayesian network, Dempster-Schafer, neural network, and knowledge-based techniques) to support real-time requirements and to achieve overall performance; methods for enhancing processing speed and capacity; spatio-temporal sensor calibration to reduce bias errors; and new techniques for graphically visualizing fused target track results. The research to be performed in this effort will be in direct support of the MDA/GM (Ground Based Midcourse) and MDA/AS (Advanced Systems) acquisition programs such as the Project Hercules Program. Proof-of-principle demonstrations of advanced data fusion concepts will be performed using simulated sensor data.