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.

中文翻译：

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FDA-MIMO用于关节角度和距离估计：展开的互质框架和参数估计算法

频率多样化阵列多输入多输出（FDA-MIMO）雷达通过利用跨发射传感器的小频率偏移来提供距离估计功能，这一点已在许多应用中得到利用。但是，估计性能基本上受阵列几何形状和信号带宽的限制。在这项研究中，作者提出了一种新的FDA-MIMO框架，即具有“未折叠的”互质数频偏（UCA-UCFO）框架的未折叠的互质数组，用于关节角度和范围估计而没有歧义。通过在空间域和频域中采用UCA，可以显着扩展阵列孔径和信号带宽，从而显着提高估计精度和分辨率。此外，我们将联合角度和距离估计问题构造为二维（2D）多个信号分类空间频谱，并通过引入连续迭代（SUIT）算法将2D总频谱搜索转换为1D局部频谱搜索。SUIT算法可以显着减轻计算负担，但不会降低性能。角度和范围的Cramér-Rao边界作为性能基准。分析和仿真已经验证了UCA-UCFO框架和SUIT算法在位置精度，分辨率和计算复杂性方面的优势和优势。SUIT算法可以显着减轻计算负担，但不会降低性能。角度和范围的Cramér-Rao边界作为性能基准。分析和仿真已经验证了UCA-UCFO框架和SUIT算法在位置精度，分辨率和计算复杂性方面的优势和优势。SUIT算法可以显着减轻计算负担，但不会降低性能。角度和范围的Cramér-Rao边界作为性能基准。分析和仿真已经验证了UCA-UCFO框架和SUIT算法在定位精度，分辨率和计算复杂性方面的优势和优势。