DOA Estimation Of Coherent Signals Research Articles

High-Accuracy DOA Estimation of Coherent Signals Based on the Time-Space Mutual Correlation Smoothing

High-Accuracy DOA Estimation of Coherent Signals Based on the Time-Space Mutual Correlation Smoothing

DOA Estimation of Coherent Signals Based on EPUMA Method With Frequency Beam Scanning Leaky-Wave Antennas

DOA Estimation of Coherent Signals Based on EPUMA Method With Frequency Beam Scanning Leaky-Wave Antennas

Two-dimensional DOA estimation of coherent signals for MIMO Radar Based on tensor decomposition

This paper studies the two-dimensional DOA estimation of coherent targets. Subspace algorithm is usually used to solve this problem. Compared with subspace algorithm, tensor decomposition has significant advantages in estimation accuracy and has been widely used in the field of signal processing. However, the research on tensor decomposition algorithm mainly focuses on incoherent targets. Therefore, we study how to use the advantages of tensor decomposition to improve the estimation accuracy of coherent targets. Firstly, we construct the echo signal model corresponding to the “Sum array” of MIMO radar, and then use the spatial smoothing to process the data to solve the problem of covariance rank deficiency. Then a third-order tensor is constructed according to the position relationship of the data. Finally, the DOA can be estimated from the decomposed tensor according to the position relationship of the data. Simulation results show that proposed algorithm has significant advantages in estimation accuracy.

An Efficient Improved OGWSBI Algorithm for Accurate Off‐Grid DOA Estimation of Coherent Signals

The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off‐grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher‐order off‐grid model and unitary transformation for off‐grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first‐order off‐grid model, the steering vector is reformulated by the second‐order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher‐order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.

DOA Estimation of Coherent Signals Based on the Sparse Representation for Acoustic Vector-Sensor Arrays

This paper focuses on the problem of the DOA estimation of coherent signals for the acoustic vector-sensor arrays (AVSAs) in the presence of the isotropic ambient noise. We propose a high-resolution DOA estimation method based on the acoustic intensity principle and the sparse representation technique. First, two cross-covariance matrices are constructed by employing the acoustic pressure and particle velocity components of the AVSA, which eliminates the isotropic noise. Then, in order to fully explore the DOA information of the particle velocity components, an augmented matrix is formed based on the two cross-covariance matrices. We observe an interesting fact that the left singular vector corresponding to the maximum singular value of the augmented cross-covariance matrix is the linear combination of all the signal steering vectors. Based on this fact, a high-resolution DOA estimation algorithm is developed via sparsely representing the left singular vector. This method does not require the prior knowledge of the noise variance or the number of signals to construct the sparse representation model. Simulation and experimental results demonstrate the proposed method outperforms the MUSIC method based on the forward/backward spatial smoothing and some existing sparse representation methods in estimation accuracy and angular resolution, especially in the cases of a low signal-to-noise ratio and/or coherent signals with small angular separations.

Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays.

This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data information of each subarray, we only perform difference-operation on the auto-correlations, while the cross-correlations are kept unchanged. Using the reconstructed submatrices, both the forward only ISD (FO-ISD) and forward backward ISD (FB-ISD) methods are developed under the proposed scheme. Compared with the existing spatial smoothing techniques, the proposed scheme can use more data information of the sample covariance matrix and also suppress the effect of additive noise more effectively. Simulation results show that both FO-ISD and FB-ISD can improve the estimation performance largely as compared to the others, in white or colored noise conditions.

DOA Estimation Algorithm in Switch Antenna Array with a Single Cycle of Sampled Data

For DOA estimation of coherent signals in switch antenna array (SAA), a new fast algorithm is proposed. Instead of conventional sub-space algorithm’s covariance matrix, a Toeplitz matrix is constructed with a single cycle of sampled data. It is proved theoretically that the ranks of the Toeplitz matrix is equal to the number of signal sources and has no relations with the coherency of the signal source. Through eigenvalue decomposition, signal and noise subspace are obtained respectively, then DOA estimation can be done by one-dimensional spectral peak searching according to the MUSIC algorithm. The theoretical analysis and simulation results demonstrate validity and superiority of the novel algorithm.

Efficient 2D DOA estimation of coherent signals in spatially correlated noise using electromagnetic vector sensors

This paper considers a new azimuth-elevation DOA estimation algorithm for multiple signals using electromagnetic vector sensor array. We firstly exploit the planar-plus-an-isolated sensor array geometry (Li et al. in IEE Proc Radar Sonar Navig 143(5):295---299, 1996) to define a full rank cross-covariance matrix. Then we develop an efficient ESPRIT-like algorithm using the so-called propagator to estimate the steering vectors of electromagnetic vector sensor, without performing eigen-decomposition into signal subspaces. Finally, we compute the vector cross product to obtain the closed-form azimuth-elevation angle estimates. The new algorithm does not require 2D iterative searching, and is applicable to coherent (fully correlated) signals and spatially correlated noise. In addition, the proposed algorithm offers enhanced estimation precision by sparse array aperture extension, but suffers no DOA cyclical ambiguity. Monte-Carlo simulations are presented to verify the effectiveness of the proposed algorithm.

Spatial difference smoothing for DOA estimation of coherent signals

A new method is proposed for direction-of-arrival estimation of coherent signals in the presence of correlated noise fields. The method comprises two steps: a first step for discriminating incoherent signals, and a second step for resolving correlated and coherent signals using spatial difference smoothing. It can eliminate spatially correlated noise and resolve more signals compared with the existing methods. The theoretical analysis and simulation results show that the proposed method is effective.

Direction-of-Arrival Estimation of Coherent Signals Using a Cylindrical Array

Mainly, a uniform linear array (ULA) has been used for DOA estimation of coherent signals because we can apply the spatial smoothing preprocessing (SSP) technique. However, estimation by a ULA has ambiguity due to the symmetry, and the estimation accuracy depends on the DOA. Although these problems can be solved by using a uniform circular array (UCA), we cannot estimate the DOA of coherent signals because the SSP technique cannot be applied directly to the UCA. In this paper, we propose to estimate 2-dimensional DOA (polar angles and azimuth angles) estimation of coherent signals using a cylindrical array which is composed of stacked UCAs.