Range Of Near-field Sources Research Articles

Multi-Dimensional Parameter Estimation in Polarimetric ULA with Cross-Distribution Dipole Pairs

This paper investigates the estimation of parameters—including the elevation angle, azimuth angle, polarization auxiliary angle, polarization phase difference, frequency and range of near-field sources in a Polarimetric Uniform Linear Array (P-ULA) with defective electromagnetic vector sensors. The cross-distribution dipole pairs are alternately placed in the xoy plane and yoz plane, respectively, and the whole array is divided into two subarrays, where subarray 1 consists of all of the dipole pairs placed in the xoy plane, while the dipole pairs placed in yoz plane are gathered in subarray 2. Specifically, the polarization auxiliary angle and the polarization phase difference, as well as the elevation and azimuth angles of the sources, are firstly estimated based on the Fourth-Order Cumulant (FOC) matrix in each subarray. Moreover, a decoupling method is developed to obtain the elevation and azimuth. Subsequently, the frequency and range are estimated based on the FOC matrix. Then, the parameter pair matching method is performed in order to match the pairs. Finally, an analysis of the Cramér-Rao Bound (CRB) is provided, and comparisons of the root mean square error with respect to the different input signal-to-noise ratios and number of snapshots, among different estimation methods, are implemented in the environment of additive white gaussian noise. The simulation results are provided in order to verify the effectiveness and feasibility of the proposed method for multi-dimensional parameter estimation.

Passive Localization of Mixed Near-Field and Far-Field Sources Without Eigendecomposition via Uniform Circular Array

In this paper, we employ the geometry of uniform circular array to achieve classification and localization of mixed near-field and far-field sources. Considering that the eigendecomposition of the covariance matrix requires high computational cost, we develop the propagator method to obtain the noise subspace and reduce complexity. Firstly, since the direction parameters of far-field sources at centrosymmetry sensors hold a conjugate structure while the covariance matrix of near-field sources holds a Hermitian structure, we exploit the covariance differencing approach to extract the pure near-field sources from mixed sources. Then, we improve the ESPRIT-like method and one-dimensional MUSIC method to determine the 2-D direction-of-arrival (DOA) and range of near-field sources, respectively. Finally, by calculating the noise power of mixed sources, we utilize the oblique projection approach to extract the pure far-field sources and exploit the 2-D MUSIC method to determine the 2-D DOA of far-field sources. Simulations demonstrate that the proposed algorithm can avoid the pseudo-peaks in the 2-D DOA spatial spectrum of far-field sources and provide the satisfactory performance of mixed source localization.

Mixed Far-Field and Near-Field Source Localization Using a Linear Electromagnetic-Vector-Sensor Array With Gain/Phase Uncertainties

This paper investigates the problem of mixed far-field (FF) and near-field (NF) source localization using a linear electromagnetic-vector-sensor array with gain/phase uncertainties. Firstly, several special fourth-order cumulant matrices are constructed, such that the shift invariance structure in the cumulant domain can be derived to estimate the DOA and polarization angles of each source at two electromagnetic vector sensors (EMVSs). Then, by computing the determinant of the coefficient matrix, the sources types can be classified with the prior knowledge of the number of both the FF and NF sources. On this basis, the range of NF sources and the DOAs of mixed sources at the phase reference point are captured subsequently. Finally, these estimates can be employed to generate the unknown gain/phase errors. Compared to the existing methods, the proposed one exploits both the spatial and polarization information of sources and provides a satisfactory parameters estimation performance under unknown phase/gain responses. Moreover, it does not need to perform any spectral search and not impose restriction on EMVSs placement, as well as realizes a more reasonable classification of the signal types. Simulations are carried out to verify the effectiveness of the proposed method.

Mixed source localization and gain-phase perturbation calibration in partly calibrated symmetric uniform linear arrays

This paper is concerned with mixed far-field and near-field source localization in the presence of gain-phase perturbations. Under some mild assumptions, we propose a new mixed source localization algorithm with the partly calibrated symmetric uniform linear array. By constructing a special second-order statistical vector, the DOAs and powers of all sources are first estimated by the modified sparse total least square (M-STLS) algorithm after compensating gain errors. Based on the estimated DOAs, the phase errors are successively obtained by the least squares criterion and a discriminant function formed by using the MUSIC null spectrum property. Finally, the mixed sources are classified and the range of near-field sources are achieved via one-dimensional spectral search. Meanwhile, the stochastic Cramér-Rao bound (CRB) for the considered problem is also given. The proposed algorithm can lead to a good mixed source classification and localization result. Numerical simulations validate the effectiveness of the proposed algorithm.

Mixed near-field and far-field source localization utilizing symmetric nested array

Existing mixed near-field and far-field source localization algorithms rely on uniform linear array (ULA), and the maximum number of sources that they can detect is less than the sensor number. As a comparison, recently proposed nested array can provide higher number of consecutive lags with the same sensor number, enabling us to extend it to mixed source localization scenario. Instead of utilizing generalized nested array, we design a symmetric nested array (SNA) for mixed source localization. By constructing a fourth-order cumulant matrix that only related to DOA information, the newly designed symmetric nested array exhibits at least of 4N+4MN−2M−3 consecutive lags with only 2M+2N−1 sensors. A compressive sensing (CS) based approach is exploited to achieve DOA estimation of all sources by fully using these consecutive lags. Based on the estimated DOAs, the range of near-field sources is obtained by 1-D spectral search, and the near-field and far-field sources are classified successively in range domain. By conducting numerical simulations, we show the superiority of the proposed algorithm in terms of estimation accuracy, number of required array sensors, and resolution ability compared with previous algorithms.

Simplified High-Order DOA and Range Estimation With Linear Antenna Array

In this letter, we propose a new efficient method to estimate the direction-of-arrival (DOA) and range of near-field sources in a decoupled way. First, a non-Hermitian cumulant matrix is constructed, whose eigenvectors associated with zero eigenvalues are used to directly estimate the DOA by using the MUSIC algorithm. Then, the ranges are estimated with the estimated DOA by orthogonalizing the remained eigenvectors. Compared with other modified 2-D MUSIC, the proposed algorithm can greatly reduce the computational complexity by avoiding a 2-D search with only one matrix and one eigenvalue decomposition. Simulation results show the effectiveness of the proposed method.

Low‐complexity estimation of signal parameters via rotational invariance techniques algorithm for mixed far‐field and near‐field cyclostationary sources localisation

In this study, the authors propose an efficient third-order cyclic moment-based estimation of signal parameters via rotational invariance techniques algorithm to cope with the mixed far-field and near-field cyclostationary sources localisation problem. The key point is that the auto-pairing parameters of near-field bearing and range is realised by estimating the unique non-singular matrix between the signal subspace and the matrix of direction vectors. This algorithm firstly constructs two special third-order cyclic moment matrices, in which the rotational factor is the function of the direction-of-arrival (DOA) and range of mixed far-field and near-field sources. By implementing the singular value decomposition and the high-resolution multiple signal classification spectral search, the authors estimate the azimuth DOAs for all the incoming sources. Then, the authors perform the estimation of the unique non-singular matrix between the signal subspace and the matrix of direction vectors. Finally, the range of near-field sources is determined from the obtained rotational factor. The proposed algorithm is computationally more efficient than the previous works, and it avoids pairing parameters. Computer simulations are carried out to demonstrate the performance of the proposed algorithm.

Passive Localization of Mixed Near-Field and Far-Field Sources Using Two-stage MUSIC Algorithm

Passive source localization is one of the issues in array signal processing fields. In some practical applications, the signals received by an array are the mixture of near-field and far-field sources, such as speaker localization using microphone arrays and guidance (homing) systems. To localize mixed near-field and far-field sources, this paper develops a two-stage MUSIC algorithm using cumulant. The key points of this paper are: (i) in the first stage, this paper derives one special cumulant matrix, in which the virtual ?steering vector? is the function of the common electric angle in both near-field and far-field signal models so that source direction-of-arrival (DOA) (near-field or far-field one) can be obtained from this electric angle using the conventional high-resolution MUSIC algorithm; (ii) in the second stage, this paper derives another particular cumulant matrix, in which the virtual ?steering matrix? has full column rank no matter whether the received signals are multiple near-field sources or multiple far-field ones or their mixture. What is more important, the virtual ?steering vector? can be separated into two parts, in which the first one is the function of the common electric angle in both signal models, whereas the second part is the function of the electric angle that exists only in near-field signal model. Furthermore, by substituting the common electric angle estimated in the first stage into one special Hermitian matrix formed from another MUSIC spectral function, the range of near-field sources can be obtained from the eigenvector of the Hermitian matrix. The resultant algorithm avoids two- dimensional search and pairing parameters; in addition, it avoids the estimation failure problem and alleviates aperture loss. Simulation results are presented to validate the performance of the proposed method.