Broadband sparse sensing : a polynomial matrix approach to co-prime and super nested arrays

Coventry, William and Clemente, Carmine and Soraghan, John; (2019) Broadband sparse sensing : a polynomial matrix approach to co-prime and super nested arrays. In: 2019 IEEE Radar Conference. Institute of Electrical and Electronics Engineers Inc., USA. ISBN 9781728116792 (https://doi.org/10.1109/RADAR.2019.8835665)

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Abstract

Passively monitoring the spectrum for detection and localisation of radar sources is ever more fraught with difficulty due to the advent of low probability of intercept (LPI) radar technology. A key aspect of LPI radar waveform design are the spread spectrum modulation schemes; instead of concentrating power over a narrow-bandwidth, this power can be spread across a broad-bandwidth making the source difficult to detect using conventional ESM methods. Such sources prompt the need for new detection, and direction of arrival estimation methods. Moreover, broadband antennas and their subsequent processing systems are expensive in terms of both cost and power-forcing a real world feasible limit on the number of antennas in a system. In addition, a fine spacing is required for ambiguity free direction of arrival estimation of higher frequency sources while a wide aperture is required for sufficient resolution of lower frequency sources. In this paper we present a novel sparse broadband direction of arrival method based upon co-prime and super-nested array geometries, using polynomial matrix methods whereby a new virtual array is formed containing many more virtual elements than in the physical array.

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