Radar Waveforms to Discern Remote Object Attributes

The objective of this effort is to develop signal waveform characteristics and processing algorithms that will deduce sensor-invariant attributes of a tracked object so that it can be classified, discriminated and evaluated for engagement. Under our proposed effort, we will develop enhanced radio frequency (RF) signal waveforms and associated processing algorithms, using a physics-based approach, to improve Aegis BMD engagement capability in raid environments. Our innovative techniques employ novel RF waveform characteristics such as modulation, timing, and phasing to deduce target characteristics such as its reflective, inertial, and material properties that can significantly enhance radar effectiveness and increase the probability of engagement success. The goal of our new RF waveform design effort is to require minimal changes to the radar hardware and use RF data processing algorithms that can be implemented in existing signal processors. We plan to develop a non-operational model showing how the new RF waveform characteristics can deduce such target characteristics as reflectivity, material construction, and others characteristics.