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Efficient and Unambiguous Two-Target Resolution via Subarray-Based Four-Channel Monopulse

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The four-channel monopulse (FCM) technique enables a phased array radar (PAR) to track the angular location of two targets with reduced complexity and thus has been extensively investigated in the… Click to show full abstract

The four-channel monopulse (FCM) technique enables a phased array radar (PAR) to track the angular location of two targets with reduced complexity and thus has been extensively investigated in the fields of radar multitarget tracking. However, the FCM technique has limited practical use because it was originally designed for a four-channel monopulse radar and is suitable only for a PAR configured with a rectangular planar array. In addition, the FCM technique is unable to resolve two targets when they have identical azimuthal or elevation angles due to angular ambiguity. In this paper, we propose a subarray-based FCM (SFCM) method to achieve an efficient, unambiguous, and fast two-target resolution, which is applicable to a PAR with a regular shape, i.e., circular arrays, elliptical arrays, and regular-octagonal arrays. Specifically, the proposed SFCM method consists of two stages: coarse estimation and precise estimation. Coarse estimation is performed by an inscribed rectangular array to initially estimate the angles of the two targets, and then precise estimation is achieved by a closed-form solution. Moreover, the proposed SFCM method can avoid the angular ambiguity issue via coordinate rotation. Furthermore, we prove that the optimal rotation angle is $45^\circ$ for minimizing the estimation error. In the performance analysis, we evaluate the effectiveness, computational complexity and Doppler effect for the SFCM method. Numerical results demonstrate that the proposed method outperforms its counterpart FCM method in accuracy, applicability, and robustness.

Keywords: four channel; channel monopulse; method; subarray based; estimation

Journal Title: IEEE Transactions on Signal Processing
Year Published: 2020

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