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Reduced complexity DOA and DOD estimation for a single moving target in bistatic MIMO radar

机译：降低了双基地MIMO雷达中单个运动目标的复杂度DOA和DOD估计

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摘要

In this work, we propose a reduced dimension and low complexity algorithm to estimate the direction-ofarrival (DOA), direction-of-departure (DOD) and the Doppler shift of a moving target for a multiple-inputmultiple-output (MIMO) radar. We derive two cost functions based on two different objective functions. We solve each of the derived cost function with a low complexity fast-Fourier-transform (FFT)-based solution in three dimensions. We further carry out a derivation to reduce the three-dimensional search to two-dimensional (2D) search and solve it with a 2D-FFT. Another reduced dimension algorithm is derived using the generalized eigenvalue method which finds the estimate of unknown parameters in one dimension with less memory constraints. This way, we propose three algorithms based on the first cost function and another three algorithms based on the second. Simulation results are used to validate the proposed algorithms. We compare the mean-square-error (MSE) performance and computational complexity of our proposed algorithms with existing ones as well. We show that our proposed algorithms have better MSE performance than existing ones and achieves the Cramer-Rao lower bound (CRLB) for all unknown target parameters. The proposed algorithms exhibit lower computational complexity than the existing ones and also provide an estimate for the Doppler shift. (C) 2019 Elsevier B.V. All rights reserved.
机译：在这项工作中，我们提出了一种降维和低复杂度的算法来估计多输入多输出（MIMO）雷达的运动目标的到达方向（DOA），离开方向（DOD）和多普勒频移。我们基于两个不同的目标函数得出两个成本函数。我们使用低复杂度的基于快速傅里叶变换（FFT）的三维方法来解决每个导出的成本函数。我们进一步进行了将三维搜索简化为二维（2D）搜索并使用2D-FFT求解的推导。使用广义特征值方法可以导出另一种降维算法，该算法可以在较少存储约束的情况下找到一维未知参数的估计值。这样，我们提出了三种基于第一个成本函数的算法，另外三种基于第二种成本函数的算法。仿真结果用于验证所提出的算法。我们还比较了所提出算法与现有算法的均方误差（MSE）性能和计算复杂性。我们表明，我们提出的算法比现有算法具有更好的MSE性能，并且针对所有未知目标参数均实现了Cramer-Rao下界（CRLB）。所提出的算法比现有算法具有较低的计算复杂度，并且还提供了对多普勒频移的估计。 （C）2019 Elsevier B.V.保留所有权利。

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