Far-field Source Localization Research Articles

A Novel Modified Symmetric Nested Array for Mixed Far-Field and Near-Field Source Localization

A Novel Modified Symmetric Nested Array for Mixed Far-Field and Near-Field Source Localization

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

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

Enhanced Direction of Arrival Estimation via Fast Sensor Array Processing

The subspace-based methods are widely studied for target detection and localization. Nonetheless, the most of the methods exploit the complete sensor array to get the noise subspace which pilots a heavy computational burden. The most of the target detection methods endure either a heavy computational burden or low resolution. To evade these issues, we apply the Nystro¨ m method to construct the noise subspace based on the randomly selected array sensor outputs. Thus we present a high-resolution and low-complexity improved fast modified root-MUSIC (FMRM) method for the target detection and localization of the multiple narrowband far-field sources. Computer simulation demonstrates that the proposed FMRM method exploiting only one-fourth of sensor array output can offer the same estimation performance as that of the standard root-MUSIC method.

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.

A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival

In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the “Minimum Sphere Method” is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the “Minimum Sphere Method” is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization.

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.

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

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.

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.

Mixed far-field and near-field source separation and localization based on FOC matrix differencing

This paper investigates the classification and localization of the mixed far-field sources (FFSs) and near-field sources (NFSs) on a symmetrical uniform linear array and proposes a new mixed source localization algorithm. Firstly, we analyze the persymmetric structure property of the far-field (FF) steering vector in matrix differencing. Based on that, we implement the matrix auto-differencing operation on the fourth-order cumulant (FOC) matrix, which eliminates the contribution of the FF signals from the mixed signals. Based on the reserved near-field (NF) parts, an effective subspace-based is performed to estimate NF parameters. Secondly, to reconstruct the NF FOC matrix, we propose a new method to estimate the NF kurtosis from the non-Hermitian statistics matrix. Then, the related NF components can be removed from the mixed signals to avoid interfering with FF direction-of-arrival (DOA) estimations. Based on the subspace technique, the FF DOAs can be determined by the one-dimensional spectrum search. The proposed algorithm does not require additional classification operations, even when NFSs have the same DOAs as FFSs. Finally, several simulations demonstrate the better estimation performance of the proposed algorithm than existing localization algorithms.

A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources

A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources

A Source Localization Method Using Complex Variational Mode Decomposition.

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources’ direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.

Symmetric thinned coprime array with reduced mutual coupling for mixed near‐field and far‐field sources localization

As we all know, the non-uniform array is widely used in the mixed near-field (NF) and far-field sources localization. The nested array contains a uniform linear array, it is easy to be affected by mutual coupling, so we propose a symmetric thinned coprime array (STCA) to reduce the mutual coupling effect of mixed source localization. In this paper, the STCA configuration consists of two sparse uniform linear arrays, the first subarray is composed of 2N − 1 sensors, and the sensor element spacing is Md. The second subarray is composed of 2 M + 2 ⌊ M 2 ⌋ sensors, and the sensor element spacing are Nd and Md respectively. The two subarrays form the symmetric coprime array, which shares a reference sensor. Under the same number of physical array sensors, compared with the latest symmetric nested array, although STCA has lower consecutive lags, the array sensors have larger inter-sensor spacing and larger physical array aperture, which can significantly reduce the mutual coupling effect between physical sensors, and better estimation performance of direction-of-arrival (DOA) estimation can be achieved. Finally, simulation results show that STCA can achieve better performance than other symmetric nested arrays under the same array sensors and mutual coupling effects.

An improved functional beamforming algorithm for far-field multi-sound source localization based on Hilbert curve

In view of the mismatch problem between the sound sources and searching grid points (non-ideal case), an improved functional beamforming (FBF) algorithm based on the Hilbert curve, so-called HFBF, for far-field localization of multi-sound sources is presented in this paper. In contrast with the traditional FBF, the HFBF may generate the searching grids on a sound source plane and optimize them by reducing the searching plane size, adjusting the function order and the focusing vector direction, so as to improve the efficiency of grid searching and the estimation accuracy of sound source parameters. A computational procedure of the HFBF is proposed and applied to identify two independent sound sources. In terms of the locating error and the estimated error of sound pressure level (SPL), the performances of four algorithms are compared by simulations and experiments. The results suggest that the accuracies of the location coordinates and the estimated SPL values are significantly improved by the Hilbert-based grid searching. The conclusions can be drawn that, for multi-source localization, the HFBF can somewhat break through the Rayleigh limit. If the exponent value is selected properly, the estimated SPL error of the HFBF can be less than 0.1 dB. The HFBF has a larger range of available exponent values, which is negatively correlated with the frequency of a sound source. Generally speaking, in the non-ideal cases, the proposed HFBF is more accurate and effective than the FBF. It can be applied to a large-scale space, such as aircraft, train, traffic environment, etc., for far-field localization of multi-sound sources in engineering.

Phase Compensation-Based Localization of Mixed Far-Field and Near-Field Sources

For mixed far-field (FF) and near-field (NF) source localization, most existing algorithms are developed for the scenario where the number of available data samples <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> is much larger than the number of sensors <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> . Different from these methods, this letter attempts to deal with the scenario where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> is large and of the same order of magnitude as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> . To obtain a satisfied performance in such a scenario, the phase compensation result of the spike covariance matrix is first utilized to construct a modified spectral function and successively realize the DOA estimation of FF sources. Then, we exploit the oblique projection operation to extract the NF sources. Finally, DOA and range estimation for NF sources is achieved via two one-dimensional (1-D) spectral searches, where the reconstructed manifold vector combining the phase compensation result is used. The effectiveness of the proposed algorithm is demonstrated by computer simulations.

Mixed Near-Field and Far-field Source Localization Using COLD Arrays

Mixed Near-Field and Far-field Source Localization Using COLD Arrays

Mixed Near-Field and Far-Field Source Localization Based On Convolution Neural Networks via Symmetric Nested Array

In this paper, the convolution neural networks (CNN) are developed for the classification and localization of mixed near-field and far-field sources by using the geometry of symmetric nested array. We first transform the received data into frequency domain. Then, we preprocess the phase difference matrix to decouple mixed sources. Considering that the counter-diagonal elements in the phase difference matrix only contain the direction of arrival (DOA) parameter of each mixed source, we utilize the upper right elements as the input of CNN to estimate the DOA of each mixed source. In order to avoid the influence of noise, we construct a particular range vector without the estimated DOA and employ the output of autoencoder to classify the mixed sources. Finally, we further exploit the output of autoencoder and apply the CNN without bias vectors to estimate the range of near-field sources. In contrast to the traditional learning-based approaches regarding the parameter estimation as a classification problem, the proposed approach considers the parameter estimation as a regression problem and can significantly reduce the complexity of networks. Simulation results demonstrate that the proposed learning-based method can improve the precision for the mixed source localization. Moreover, the proposed method is robust to the off-grid parameters and the different numbers of mixed sources.