Near-field Source Localization Research Articles

A 3-D spatial–temporal near-field parameter estimation method with two correlation operations

This paper proposes a narrowband near-field (NF) source localization algorithm based on two correlation operations. By utilizing the correlation of incident signal in the time and spatial domains, the algorithm achieves direction-of-arrival (DOA) with high-precision and low-complexity and range estimation under the virtual single snapshot. To do so, in the processing of NF source received signals, this paper first utilizes the symmetry of the cross-cross array and obtains a covariance matrix containing only azimuth and elevation information through two correlation operations. Since the covariance matrix has a standard two-level Toeplitz matrix form, the estimated parameters can be directly extracted from the noise-free covariance matrix without pairing using the two-level Vandermonde decomposition technique. Finally, the estimated value of the range is obtained using a one-dimensional (1-D) spectral peak search. Computer simulation results verify the superior performance of the proposed algorithm in terms of complexity and estimation accuracy.

A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error

The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.

Comments on “Mixed Near-Field and Far-Field Sources Localization Using the Uniform Linear Sensor Array”

Comments on “Mixed Near-Field and Far-Field Sources Localization Using the Uniform Linear Sensor Array”

Improved Sparse Symmetric Arrays Design for Mixed Near-Field and Far-Field Source Localization

Improved Sparse Symmetric Arrays Design for Mixed Near-Field and Far-Field Source Localization

Joint MWC and Linear Array for Mixed Near-Field and Far-Field Source Localization

Joint MWC and Linear Array for Mixed Near-Field and Far-Field Source Localization

An Improved Gray Wolf Algorithm for Radiation Near-Field Source Localization in Exact Model

An Improved Gray Wolf Algorithm for Radiation Near-Field Source Localization in Exact Model

Passive localization of wideband near-field sources using Sparse Bayesian learning and envelope alignment

Most studies about passive localization of near-field signals are limited to narrowband sources. In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) and range estimation of wideband near-field sources. Using the specific structure of symmetric linear array, the direction information is extracted and the off-grid Sparse Bayesian learning (SBL) framework is established for DOA estimation. The generalized approximate message passing (GAMP) strategy is applied for fast implementation. Then the envelope aligned approach is proposed for the range estimation in time domain and the linear searching concept is applied to accelerate the computation. Compared with the existing wideband near-field localization methods, the proposed algorithm has better performance of accuracy and resolution probability. Simulation results validate the feasibility and effectiveness of the proposed algorithm.

Dimension decomposition algorithm for multiple source localization using uniform circular array

A dimension decomposition (DIDE) method for multiple incoherent source localization using uniform circular array (UCA) is proposed. Due to the fact that the far-field signal can be considered as the state where the range parameter of the near-field signal is infinite, the algorithm for the near-field source localization is also suitable for estimating the direction of arrival (DOA) of far-field signals. By decomposing the first and second exponent term of the steering vector, the three-dimensional (3-D) parameter is transformed into two-dimensional (2-D) and one-dimensional (1-D) parameter estimation. First, by partitioning the received data, we exploit propagator to acquire the noise subspace. Next, the objective function is established and partial derivative is applied to acquire the spatial spectrum of 2-D DOA. At last, the estimated 2-D DOA is utilized to calculate the phase of the decomposed vector, and the least squares (LS) is performed to acquire the range parameters. In comparison to the existing algorithms, the proposed DIDE algorithm requires neither the eigendecomposition of covariance matrix nor the search process of range spatial spectrum, which can achieve satisfactory localization and reduce computational complexity. Simulations are implemented to illustrate the advantages of the proposed DIDE method. Moreover, simulations demonstrate that the proposed DIDE method can also classify the mixed far-field and near-field signals.

A Novel Low-Complexity Method for Near-Field Sources Based on an S-IMISC Array Model

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model.

Mixed Near-Field and Far-Field Sources Localization via Oblique Projection

This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the accuracy of NFS localization. First, the FFS’s direction-of-arrival (DOA) estimate is acquired utilizing a multiple signal classification (MUSIC) spectral peak search. To classify mixed sources more effectively, we utilize the oblique projection technique, which can successfully prevent FFS information from influencing the estimation of NFS parameters. A HOC matrix with solely NFS DOA information is built by choosing array elements in a specific sequence. The estimation of the NFS DOA is then derived using the estimation of signal parameters via a rotational invariance technique (ESPRIT)-like algorithm. Finally, the NFS range is acquired by a MUSIC search. The performance of the presented algorithm is discussed in several aspects. Compared to existing matrix difference methods, the presented algorithm, which adopts the oblique projection method, achieves superior results in the separation of mixed sources. Without excessively increasing the computational complexity, it not only ensures the performance of localization parameter estimation for FFS but also estimates the NFS with higher precision. The numerical simulations attest to the superior performance of the presented algorithm.

Source localization based on steered frequency-wavenumber analysis for sparse array.

When using a sparse array, locating the target signal of a high-frequency component is difficult. Although forecasting the direction in a sparse situation is challenging, the frequency-wavenumber (f-k) spectrum can simultaneously determine the direction and frequency of the analyzed signal. The striation of the f-k spectrum shifts along the wavenumber axis in a sparse situation, which reduces the spatial resolution required to determine the target's direction using the f-k spectrum. In this study, f-k spectra of a high-frequency signal were used for near-field source localization. Snapping shrimp sounds (5-24 kHz) from SAVEX15 (a shallow-water acoustic variability experiment conducted in May 2015) were used as the data source, and a simulation was used to evaluate the proposed method. Beam steering was performed before creating the f-k spectrum to improve spatial resolution. We found that the spatial resolution was improved, and the location of the sound source could be determined when a signal with beam steering was utilized. The shrimp sound from SAVEX15, a near-field broadband signal, was used to determine the shrimp's location (range, 38 m; depth, 100 m) and the tilt of the vertical line array. These results suggest that the proposed analysis helps to accurately estimate the location of sound source.

Mixed source localization considering mutual coupling and unknown nonuniform noise under exact spatial geometry

A mixed far-field (FF) and near-field (NF) source localization method in the presence of unknown mutual coupling and nonuniform noise is proposed in this paper, where the exact spatial geometry considering propagation attenuation is adopted. First, the nonuniform noise covariance and FF direction of arrival (DOA) are estimated by the iterative least-squares subspace (ILSS) technique and the principle of rank reduction (RARE), respectively. Then, the NF source components are extracted by applying the oblique projection technique. Finally, by choosing three relative sensor positions and constructing four special cumulant matrices properly, NF DOA and range information are obtained in closed-form expressions, where a scheme to avoid ambiguity is exploited. Simulation result show that the proposed method can yield an improved performance in comparison with existing state-of-the-art methods.

Trilinear decomposition based near-field source localization with MIMO velocity vector sensor arrays

This paper studies the near-field (NF) parameter estimation problem for a multiple-input multiple-output (MIMO) array system, which employs multiple pairs of orthogonal velocity sensors at both the transmitter and the receiver. A trilinear decomposition method is proposed to estimate the four-dimensional (4-D) parameters, including the direction of departure (DOD), the range from transmitter to target (RFTT), the direction of arrival (DOA), and the range from target to receiver (RFTR). Firstly, the output of the matched filter at the receiver is formulated in a third-order parallel factor (PARAFAC) model; secondly, the initial coarse estimates of DOD, RFTT, DOA and RFTR embedded in the velocity vector sensors are obtained through trilinear decomposition, and then more accurate estimates of DOD, RFTT, DOA and RFTR are achieved from the steering vector. The proposed method is search-free and has a close form, with automatically paired results. Its performance is demonstrated via numerical examples.

Symmetric Extended Nested Array for Passive Localization of Mixture Near- and Far-Field Sources

Nowadays, symmetric nonuniform linear arrays (SNLAs) have been considered for mixture near-field (NF) and far-field (FF) source localization since it can provide larger physical array aperture and higher degree-of-freedom (DOF) than uniform linear arrays (ULAs). In this communication, a symmetric extended nested array (SENA) is proposed on the basis of compressed symmetric nested array (CSNA). The closed-form expression for the SENA and its difference coarray (DCA) is presented in detail. For the sake of comparison to the existing SNLAs with sensors, the longer consecutive DCA lags and larger physical array aperture are obtained with optimal design to achieve better estimation results. Numerical results demonstrate that utilizing SS-MUSIC and MUSIC algorithms, the SENA outperforms exiting SNLAs in terms of the DOA and range estimate accuracy for mixture field sources.

Three-Dimensional Mixed Far-Field and Near-Field Sources Localization Utilizing Cross Tripole Array

Three-Dimensional Mixed Far-Field and Near-Field Sources Localization Utilizing Cross Tripole Array

Mixed-field source localization based on robust matrix propagator and reduced-degree polynomial rooting

This paper develops two new algorithms based on multiple signal classification (MUSIC) and matrix propagator for near-field (NF) and far-field (FF) source localization, which substantially reduce the computational complexities and can be applied no matter whether the source number is known as a priori. In the developed algorithms, two special fourth-order cumulant (FOC) matrices are constructed. Then the noise subspace is obtained by incorporating lower-upper (LU) decomposition into the matrix propagator approach. This avoids the computationally burdensome eigenvalue decomposition (EVD) or singular value decomposition (SVD) as well as the dependence of source number. The direction-of-arrivals (DOAs) of FF and NF sources are distinguished by the oblique projection and then estimated based on the MUSIC spectrum. To further improve the computational efficiency, an alternating root-propagator scheme is proposed to determine DOAs, where the real lower-degree polynomial is formulated, which increases the computational speed by a factor about eight compared to the standard root-MUSIC. Finally, the NF ranges are calculated from the FOC and the DOA estimates via the least square (LS) criterion. The effectiveness and performance of the proposed approach are validated by using numerical simulation.

Localization for mixed near-field and far-field sources under impulsive noise

Localization for mixed near-field and far-field sources under impulsive noise

Unified Near-Field and Far-Field TDOA Source Localization Without the Knowledge of Signal Propagation Speed

Unified Near-Field and Far-Field TDOA Source Localization Without the Knowledge of Signal Propagation Speed

Parameters Estimation of 3-D Near-Field Sources With Arbitrarily Spaced Electromagnetic Vector-Sensors

In this Letter, we consider the problem of parameters estimation for near-field sources (NFSs) in three-dimensional (3-D) space with arbitrarily spaced electromagnetic vector-sensors (EMVSs), by taking advantage of the high-order statistics (HOS) of the array observations. In particular, we devise several fourth-order cumulant (FOC) matrices and exploit them to capture the two-dimensional direction-of-arrival (2D-DOA) and polarization information at each EMVS with subspace techniques. On this basis, a linear equation is developed to determine the location parameters of NFSs. Unlike the existing methods, the proposed one is not necessary to use uniformly spaced linear or planar arrays and is applicable to arbitrary EMVS array geometries. Moreover, it is also able to achieve the 3-D localization of NFSs, as well as estimating the polarization parameters accurately. Simulation results are presented to validate the performance of the proposed algorithm.

Conjugate Augmented Decoupled 3-D Parameters Estimation Method for Near-Field Sources

A near-field source localization method is proposed for 2-D direction-of-arrival (DOA) and range estimation based on a symmetrical cross array. It first employs the conjugate symmetry property of signal autocorrelation for different time delays to construct a conjugate augmented spatial–temporal cross-correlation matrix, then the extended steering vector is decoupled to avoid the usual multidimensional search based on the properties of the Khatri–Rao product, and finally three 1-D MUSIC-type searches are employed to obtain the results. The proposed method can realize automatic pairing of multiple parameters associated with each source and it also works in the underdetermined case. Furthermore, the stochastic Cramer–Rao lower bound with different time delays is derived. Compared with two existing methods, simulation results demonstrate that the proposed method provides satisfactory estimation performance for both the DOA and range parameters at low signal-to-noise ratio and with a small number of snapshots.