FDA‐MIMO for joint angle and range estimation: unfolded coprime framework and parameter estimation algorithm

doi:10.1049/iet-rsn.2019.0479

FDA‐MIMO for joint angle and range estimation: unfolded coprime framework and parameter estimation algorithm

2020-04-22

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DOI：10.1049/iet-rsn.2019.0479

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The frequency diverse array multiple‐input multiple‐output (FDA‐MIMO) radar provides range estimation capability by exploiting a small frequency offset across the transmit sensors, which has been utilised in numerous applications. However, the estimation performance is basically limited by the array geometry and signal bandwidth. In this study, the authors propose a new FDA‐MIMO framework, i.e. the unfolded coprime array with ‘unfolded’ coprime frequency offsets (UCA‐UCFO) framework, for joint angle and range estimation without ambiguity. The array aperture and signal bandwidth are obviously expanded by employing UCA in the spatial domain and frequency domain, which results in significantly enhanced estimation accuracy and resolution. In addition, we construct the joint angle and range estimation problem as a two‐dimensional (2D)‐multiple signal classification spatial spectrum and transform 2D total spectrum search into a 1D local spectrum search by introducing a successive iteration (SUIT) algorithm. The SUIT algorithm can significantly relieve the computational burden but without performance degradation. The Cramér–Rao bounds of angle and range are provided as a performance benchmark. The analysis and simulations have validated the superiority and advantages of the UCA‐UCFO framework and SUIT algorithm with respect to location accuracy, resolution, and computational complexity.

FDA‐MIMO for joint angle and range estimation: unfolded coprime framework and parameter estimation algorithm

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