scholarly journals Joint Processing of DOA Estimation and Signal Separation for Planar Array Using Fast-PARAFAC Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhongyuan Que ◽  
Benzhou Jin ◽  
Jianfeng Li
Keyword(s):  
Doa Estimation ◽  
Least Square ◽  
Signal Separation ◽  
Joint Processing ◽  
Processing Theory ◽  
Propagator Method ◽  
Planar Array ◽  
Novel Method ◽  
Parallel Factor ◽  
Parafac Decomposition

A joint processing of direction of arrival (DOA) and signal separation for planar array is proposed in this paper. Through sensor array processing theory, the output data of a planar array can be reconstructed as a parallel factor (PARAFAC) model, which can be decomposed with the trilinear alternating least square (TALS) algorithm. Aiming at the problem of slow speed on convergence for the standard PARAFAC method, we introduce the propagator method (PM) to accelerate the convergence of the TALS method and propose a novel method to jointly separate signals and estimate the corresponding DOAs. Given the initial angle estimates with PM, the number of iterations of TALS can be reduced considerably. The experiments indicate that our method can carry out signal separation and DOA estimation for typical modulated signals well and remain the same performance as the standard PARAFAC method with lower computational complexity, which verifies that our algorithm is effective.

scholarly journals Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Heyun Lin ◽  
Chaowei Yuan ◽  
Jianhe Du ◽  
Zhongwei Hu
Keyword(s):  
Computational Complexity ◽  
Doa Estimation ◽  
Least Square ◽  
Estimation Accuracy ◽  
Tensor Model ◽  
Complete Study ◽  
Noncircular Signals ◽  
Parallel Factor ◽  
Parafac Decomposition ◽  
Extended Matrix

We provide a complete study on the direction-of-arrival (DOA) estimation of noncircular (NC) signals for uniform linear array (ULA) via Vandermonde constrained parallel factor (PARAFAC) analysis. By exploiting the noncircular property of the signals, we first construct an extended matrix which contains two times sampling number of the received signal. Then, taking the Vandermonde structure of the array manifold matrix into account, the extended matrix can be turned into a tensor model which admits the Vandermonde constrained PARAFAC decomposition. Based on this tensor model, an efficient linear algebra algorithm is applied to obtain the DOA estimation via utilizing the rotational invariance between two submatrices. Compared with some existing algorithms, the proposed method has a better DOA estimation performance. Meanwhile, the proposed method consistently has a higher estimation accuracy and a much lower computational complexity than the trilinear alternating least square- (TALS-) based PARAFAC algorithm. Finally, numerical examples are conducted to demonstrate the effectiveness of the proposed approach in terms of estimation accuracy and computational complexity.

Two-Dimensional DOA Estimation for Planar Array Using a Successive Propagator Method

2019 ◽  
Vol 28 (10) ◽  
pp. 1950161 ◽  
Author(s):  
Weiyang Chen ◽  
Xiaofei Zhang ◽  
Chi Jiang
Keyword(s):  
Computational Cost ◽  
Doa Estimation ◽  
Rotational Invariance ◽  
Eigenvalue Decomposition ◽  
Invariance Property ◽  
Two Dimensional ◽  
Propagator Method ◽  
Planar Array ◽  
Esprit Algorithm ◽  
Better Than

We consider the problem of two-dimensional (2D) direction of arrival (DOA) estimation for planar array, and propose a successive propagator method (PM)-based algorithm. The rotational invariance property of the propagator matrix is exploited to obtain the initial angle estimations, while the accurate estimates can be achieved through successive one-dimensional and local spectrum-peak searches. The proposed algorithm can obtain automatically paired 2D-DOA estimations, and it requires no eigenvalue decomposition of the covariance matrix of received data, which remarkably reduces the computational cost compared with traditional 2D-PM algorithm. In addition, the DOA estimation performance of the proposed algorithm is better than estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm and PM algorithm, and is close to 2D-PM algorithm which requires 2D spectrum-peak search. Numerical simulations demonstrate the effectiveness and improvement of the proposed algorithm.

scholarly journals Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

2021 ◽  
Vol 13 (15) ◽  
pp. 2964
Author(s):  
Fangqing Wen ◽  
Junpeng Shi ◽  
Xinhai Wang ◽  
Lin Wang
Keyword(s):  
Numerical Study ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Phase Errors ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Angle Pair ◽  
Parallel Factor ◽  
Parafac Decomposition

Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

High-Accuracy Fundamental Frequency Detection in Power System

2011 ◽  
Vol 383-390 ◽  
pp. 4962-4966
Author(s):  
Ling Li ◽  
Guo Bin Jin ◽  
Shao Ping Huang ◽  
Xiao Peng
Keyword(s):  
Power System ◽  
Fundamental Frequency ◽  
Frequency Measurement ◽  
Least Square ◽  
Signal Model ◽  
Least Square Estimation ◽  
Signal Process ◽  
Frequency Detection ◽  
Novel Method ◽  
Subspace Estimation

A novel method on frequency measurement based on improved TLS-ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) is proposed in this paper with the research on fundamental frequency measurement in power system. TLS-ESPRIT is belong to subspace estimation in modern signal process. Noise is included in signal model, so it is independent on noise. But the same multi-poles cannot be taken when signal is in noise and based on TLS-ESPRIT. Multiple poles restoring is presented to take the true poles accurately. It is revealed that fundamental frequency is detected accurately in harmonics, interharmonics, noise and frequency fluctuations and better anti-noise ability in particular better adaptiveness on time varying signal in amplitude by simulation results.

scholarly journals A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Jing Liu ◽  
ChongZhao Han ◽  
XiangHua Yao ◽  
Feng Lian
Keyword(s):  
Compressed Sensing ◽  
Phased Array ◽  
Reduction Method ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Sensing Matrix ◽  
Replacement Method ◽  
Precise Estimation ◽  
Novel Method ◽  
Measurement Vector

A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. The measurement vector is changed accordingly. It is proved that the original sparse signal could be reconstructed well from the newly changed measurement vector based on the new sensing matrix with large probability. This method is then extended to a more practical condition when highly coherent columns and incoherent columns are considered, for example, the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, where the compressed sensing method based on the original sensing matrix fails. The proposed method also obtains more precise estimation of DOA using one snapshot compared with the traditional estimation methods such as Capon, APES, and GLRT, based on hundreds of snapshots.

scholarly journals A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms

2017 ◽  
Vol 7 (4) ◽  
pp. 1906 ◽  
Author(s):  
Abouzid Houda ◽  
Chakkor Otman
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real Life ◽  
Signal Separation ◽  
Mixing Process ◽  
The Real ◽  
Input Signals ◽  
Novel Method

Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the  nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation.  But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used.

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