A frequency diverse array (FDA) multiple‐input multiple‐output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle‐range‐dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast‐convergence trilinear decomposition (FC‐TD) algorithm to jointly estimate FDA‐MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC‐TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramér‐Rao bounds are presented and simulation results are provided to validate the proposed FC‐TD algorithm effectiveness.

中文翻译：

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用于FDA‐MIMO雷达角度和距离估计的快速收敛三线性分解算法

频分阵列（FDA）多输入多输出（MIMO）雷达在发射元件上采用较小的频率增量，以产生角度范围相关的波束图，以进行目标角度和范围检测。关节角度和距离估计问题是一个三线性模型。由于过度的迭代，传统的三线性交替最小二乘（TALS）算法涉及高计算量。我们提出了一种快速收敛的三线性分解（FC-TD）算法，以共同估算FDA-MIMO雷达目标角度和距离。我们首先使用传播器方法来获得数据域中的粗略角度和范围估计。接下来，将粗略估计值用作初始化参数，而不是传统的TALS算法随机初始化，以减少迭代并加速收敛。最后，精确的角度和范围估计值会导出并自动配对。与传统的TALS算法相比，提出的FC‐TD算法具有较低的计算复杂度，并且不会降低估计性能。此外，提出了Cramér-Rao边界，并提供了仿真结果以验证所提出的FC-TD算法的有效性。