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Fully Adaptive Radar Resource Allocation



OBJECTIVE: Develop techniques for radar resource allocation for closed-loop radar detection and tracking 

DESCRIPTION: Onerous challenges imposed by an A2AD environment call for closed loop radar operation for concurrent detection and tracking of targets from single and distributed radar systems. In the context of the sense-learn-adapt framework or perception-action framework, this necessitates the use of past data to determine future radar illumination and data collection. Current techniques do not optimally and automatically balance the need to detect, track, and identify targets. Recently, cognitive radar approaches have been used to compute sensing actions that are expected to maximize the utility of the received data. Similarly, past efforts on information-theoretic sensor management have produced a framework for managing the resources of an agile sensor, where the utility of the sensing action is judged by the expected amount of information flow. This effort solicits the development of intelligent sensor management approaches for optimized sensing in a dynamic, complicated environment characterized as containing many moving targets, performing maneuvers that are intermittently obscured to the sensor. While previous efforts have focused on portions of this problem, this topic seeks approaches that address using multiple sensors for detection, and tracking of multiple targets from single and distributed radar in a closed loop manner. Specifically, we seek approaches to capture the scene probabilistically and use this information to drive future sensing actions, and lead to quantitative improvements in performance over current approaches as measured by standard tracking benchmarks such as time until correct detection and identification, track mean square error, and optimal sub-pattern assignment (OSPA). Ideally, we seek radar resource allocation techniques that incur a weak dependence on the number of sensors and the number of targets. The approach must afford application of ideas from cognitive sensing to guide agile sensor action at the next time step and beyond, such as selection of pointing, mode, waveform and PRF. 

PHASE I: Develop a closed loop sensor management framework for concurrent detection, and tracking of ground targets in a single sensor setting. A host of multi-objective optimization problems encountered in this context, need to be addressed. Performance analysis and benchmarking of the approach are called for using standard measures. 

PHASE II: Extend the approach to include distributed radars tracking multiple targets. Validation of the concepts need to be done via simulation and experimentation. 

PHASE III: Techniques from this effort will be fundamental to the performance evaluation and benchmarking of closed loop radar detection and tracking. Technology insertion opportunities include platforms such as AWACS and Global Hawk. 


1. 1. S. Haykin, Y. Xue, and P. Setoodeh, “Cognitive Radar: Step Toward Bridging the Gap Between Neuroscience and Engineering”, The Proceedings of the IEEE, vol. 100, no. 11, pp. 3102-3130, Nov. 2012.; 2. D. Fuhrmann, “Active-Testing Surveillance Systems, or, Playing Twenty Questions with Radar”, in Proc. 11th Annual Adaptive Sensor and Array Processing (ASAP) Workshop, MIT Lincoln Laboratory, Lexington, MA, Mar. 11-13, 2003.; 3. C. Kreucher, A. Hero, K. Kastella, and M. Morelande, “An Information-Based Approach to Sensor Management in Large Dynamic Networks”, The Proceedings of the IEEE, vol. 95, no. 5, pp. 978-999, 2007.; 4. S. Liu, S. Bhat, J. Zhang, Q. Ding, R. Narayanan, A. Papandreou-Suppappola, S. Kay, and M. Rangaswamy, “Design and Performance of an Integrated Waveform-Agile Multi-Modal Track-before-Detect Sensing System”, 2011 Asilomar Conference on Signals, Systems

KEYWORDS: Closed Loop Radar, Resource Allocation, Distributed Radar 

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