2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar

Fang, Jiaxiong; Liu, Yonghong; Jiang, Yuqian; Yang, Lu; Zhang, Zehao; Chen, Hua; Wang, Laihua

doi:10.3390/s20082177

Cited by 4 publications

(2 citation statements)

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“…In FMCW radar systems, to solve this problem of estimators based on FFT, subspace-based algorithms have been studied [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. In [ 19 , 20 ], Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based algorithms are employed to accurately estimate the direction of arrival (DOA) of targets.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], multiple signal classification (MUSIC) algorithms, which are representative subspace-based algorithms, have been proposed. In [ 25 ], authors have proposed the distributed 2D MUSIC algorithm with coordinated transformation in a distributed way.…”

Section: Introductionmentioning

confidence: 99%

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FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area

Kim

Jin

Lee

et al. 2022

Sensors

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We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.

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