Performance Prediction for Airborne Multistatic Radar

Period of Performance: 05/07/2013 - 08/14/2015

$750K

Phase 2 SBIR

Recipient Firm

Black River Systems Co., Inc.
162 Genesee Street
Utica, NY 13502
Principal Investigator

Abstract

ABSTRACT: Development and deployment of airborne multistatic radar systems and the algorithms that control them can be greatly aided by accurate modeling and performance prediction. Capturing physical, electromagnetic and environmental real-world effects of multistatics in a simulation capability is imperative to achieve the desired benefits of this effort. Highly parameterized transmitter and receiver models with well-defined command and feedback interfaces, which are capable of real-time simulation, offer great utility to sensing system and control algorithm designers. Providing the ability to simulate the wide array of situations multistatic systems have to face (ex. varying transmitter cooperativeness, limitations on available emissions, hostile environments where passive operation is critical) allows for critical design decisions to be assessed. Relevant performance metrics must be developed and utilized to handle the complicated task of predicting the value of decision trades in radar design and algorithmic control. Black River proposes to develop an extensible model of passive multistatic radar with AMTI, GMTI, and SAR modes integrated with a closed-loop sensor manager simulation and tracking model, and a suite of performance measurement tools. A user interface is included to facilitate sensor architecture and system parameter trade studies, illuminator model library definition, and the construction of sensing environment test conditions. BENEFIT: An accurate and efficient simulation capability of airborne multistatic systems will help researchers and developers to streamline and enhance the processes of requirements definition, data collection / validation planning, and development of passive radar sensors and their intelligent control algorithms. Commercial entities could exploit this tool to identify applications where existing active sensors could be replaced with new passive systems satisfying system requirements, but which can be produced cheaper, with reduced payload (size weight, and power), and enhanced reliability.