2-D Doa Estimation of LFM Signals for Uca Based on Time-Frequency Multiple Invariance Esprit

Cui, Kaibo; Wu, Weiwei; Huang, Jingjian; Chen, Xi; Yuan, Naichang

doi:10.2528/pierm16112208

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Compared with 1D DOA estimation, 2D DOA estimation can provide more accurate signal location information, which is more practical and applicable. Employing a uniform circular array (UCA) can extend MUSIC and ESPRIT to 2D, namely, 2D-MUSIC [8,9] and UCA-ESPRIT [10,11]. However, in the 2D DOA estimation scenario, the computational complexity of conventional 2D estimation algorithms is much higher.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Chen

Joo

2021

International Journal of Antennas and Propagation

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This paper presents a novel efficient high-resolution two-dimensional direction-of-arrival (2D DOA) estimation method for uniform circular arrays (UCA) using convolutional neural networks. The proposed 2D DOA neural network in the single source scenario consists of two levels. At the first level, a classification network is used to classify the observation region into two subregions (0°, 180°) and (180°, 360°) according to the azimuth angle degree. The second level consists of two parallel DOA networks, which correspond to the two subregions, respectively. The input of the 2D DOA neural network is the preprocessed UCA covariance matrix, and its outputs are the estimated elevation angle to be modified by postprocessing and the estimated azimuth angle. The purpose of the postprocessing is to enhance the proposed method’s robustness to the incident signal frequency. Moreover, in the inevitable array imperfections scenario, we also achieve 2D DOA estimation via transfer learning. Besides, although the proposed 2D DOA neural network can only process one source at a time, we adopt a simple strategy that enables the proposed method to estimate the 2D DOA of multiple sources in turn. Finally, comprehensive simulations demonstrate that the proposed method is effective in computation speed, accuracy, and robustness to the incident signal frequency and that transfer learning could significantly reduce the amount of required training data in the case of array imperfections.

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Section: Introductionmentioning

confidence: 99%

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Chen

Joo

2021

International Journal of Antennas and Propagation

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“…Besides the disturbance of cross-terms induced by the choice of the time–frequency points, this approach mainly suffers from time consuming and model biases. To improve the performance of wideband DOA estimation, several modified algorithms based on the short-time Fourier transform (STFT) [12] and Wigner–Ville distribution (WVD) [13] have been presented. Nevertheless, the narrow time-window in STFT limits the time–frequency resolution, and the WVD requires great computational cost and suffers from cross-terms interference when nonstationary signals are multicomponent.…”

Section: Introductionmentioning

confidence: 99%

Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

Cui

Wang

et al. 2019

Sensors

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An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.

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“…Because the robotic application needs 360° information from sensors, we choose UCA to estimate the DOA of the source signals. Generally speaking, the classical DOA estimation techniques can be divided into two categories: the algorithms based on time difference of arrival (TDOA) [9,10,11] and the algorithms based on high-resolution spectrum estimation [12,13,14]. For the former methods, the DOA estimation of source signals is carried out by finding the time difference of the homologous signals.…”

Section: Introductionmentioning

confidence: 99%

A Robust Direction of Arrival Estimation Method for Uniform Circular Array

et al. 2019

Sensors

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Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

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2-D Doa Estimation of LFM Signals for Uca Based on Time-Frequency Multiple Invariance Esprit

Cited by 8 publications

References 26 publications

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

A Robust Direction of Arrival Estimation Method for Uniform Circular Array

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