GTD Model Parameters Estimation Based on Improved LS-ESPRIT Algorithm

Zheng, Shuyu; Zhang, Xiaokuan; Zong, Binfeng; Li, Jiang

doi:10.1109/piers-fall48861.2019.9021405

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Firstly, we compared the above two algorithms with the root-MUSIC algorithm (Algorithm 1). Then, we compared the proposed algorithms with the existing methods, including the FFT algorithm, the CZT algorithm [ 24 ], the MUSIC algorithm, the ESPRIT algorithm, the PCA-MUSIC algorithm [ 18 ], and the WNNM ESPRIT algorithm [ 19 ]. The performance of all algorithms was evaluated by 2000 independent Monte Carlo iterations for each signal-to-noise ratio (SNR) in MATLAB.…”

Section: Simulations and Analysismentioning

confidence: 99%

“…In order to reduce the influence of noise on the frequency estimation accuracy, ref [ 18 ] proposed the principal components analysis (PCA) algorithm to reduce the dimension of the data, which effectively improved the frequency estimation accuracy. Based on the low rank characteristics of the signal matrix, ref [ 19 ] proposed the nuclear norm to restore the signal matrix, which improved the anti-noise performance of the algorithm. Although the above methods were able to reduce the influence of noise on the accuracy of frequency estimation, they were unable to distinguish signals with close frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Root-MUSIC Algorithm Based on Matrix Reconstruction for Frequency Estimation

Zhu

Zhang

et al. 2023

Sensors

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In recent years, frequency-modulated continuous wave (FMCW) radar has been widely used in automatic driving, settlement monitoring and other fields. The range accuracy is determined by the estimation of the signal beat frequency. The existing algorithms are unable to distinguish between signal components with similar frequencies. To address this problem, this study proposed an enhanced root-MUSIC algorithm based on matrix reconstruction. Firstly, based on the sparsity of a singular value vector, a convex optimization problem was formulated to identify a singular value vector. Two algorithms were proposed to solve the convex optimization problem according to whether the standard deviation of noise needed to be estimated, from which an optimized singular value vector was obtained. Then, a signal matrix was reconstructed using an optimized singular value vector, and the Hankel structure of the signal matrix was restored by utilizing the properties of the Hankel matrix. Finally, the conventional root-MUSIC algorithm was utilized to estimate the signal beat frequency. The simulation results showed that the proposed algorithm improved the frequency resolution of multi-frequency signals in a noisy environment, which is beneficial to improve the multi-target range accuracy and resolution capabilities of FMCW radar.

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Section: Simulations and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Root-MUSIC Algorithm Based on Matrix Reconstruction for Frequency Estimation

Zhu

Zhang

et al. 2023

Sensors

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“…Meanwhile, there exists a mismatch pairing of the range-velocity estimates when there are multiple targets. Therefore, researchers have proposed algorithms to improve the accuracy of two-dimension (2D) frequency estimation [18][19][20][21][22][23][24][25][26]. The 2D frequencies are estimated by the 2D-CZT algorithm [18] and the pairing of range-velocity estimates is solved by using the correlation of signals [19].…”

Section: Introductionmentioning

confidence: 99%

“…The 2D-RootMUSIC algorithm [21] performs two 1D root estimates of frequencies and then performs range-velocity pairing by using the noise subspace orthogonal to the range-velocity steering vector. The 2D-ESPRIT algorithm [22,23] derives the 2D frequency parameters by utilizing the rotation invariance and translation invariance of the 2D signal matrix. The Modified Matrix Enhancement and Matrix Pencil (MMEMP) algorithm [24,25] is an improvement of the Matrix Enhancement and Matrix Pencil (MEMP) algorithm [26], which allows joint estimation of 2D frequencies.…”

Section: Introductionmentioning

confidence: 99%

2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar

Wen,

Yi,

Zhang

et al. 2023

Applied Sciences

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Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar.

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“…At present, many algorithms have been applied to solve this problem. In [13][14][15][16], a multiple signal classification (MUSIC) algorithm was proposed to extract the parameters of the GTD model. This method provides good range resolution, but its ability to estimate parameters at low signal-to-noise ratio (SNR) values is poor.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of the GTD model based on performance enhanced MUSIC algorithms

XU,

Zhang,

Zong

et al. 2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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Scattering centers are important features of targets at high frequency regions and the geometric theory of diffraction (GTD) scattering center model is the typical one to describe scattering centers. Therefore, it is quite vital to estimate parameters of GTD model accurately. The classical multiple signal classification (MUSIC) algorithm can effectively estimate these parameters at high signal-to-noise ratios (SNR) but it suffers from poor parameter estimation performance at low SNR scenarios. To solve this problem, we propose a modified MUSIC algorithm which enhances the noise robustness. The modified MUSIC algorithm construct a new total covariance matrix R by averaging the auto-correlation matrix of the original back-scattered data and the auto-correlation matrix of its conjugate data. Then, we take even powers of R ,which can broaden the differences between the eigenvalues of noises and signals and avoid overlapping spectral peaks. The theoretical computational complexities of the main modified step are discussed in this paper. Simulation results verify that the proposed algorithms achieve superior accuracy in parameter estimation of the GTD model and obtain better noise robustness. What is more, a target recognition method based on the GTD model and the artificial intelligence algorithm are proposed in this paper. Simulation results validate the effectiveness of this method.

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GTD Model Parameters Estimation Based on Improved LS-ESPRIT Algorithm

Cited by 8 publications

References 9 publications

Enhanced Root-MUSIC Algorithm Based on Matrix Reconstruction for Frequency Estimation

Enhanced Root-MUSIC Algorithm Based on Matrix Reconstruction for Frequency Estimation

2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar

Parameter estimation of the GTD model based on performance enhanced MUSIC algorithms

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