Near-field Sources Research Articles

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.

Localization of Signals in the Near-Field of an Antenna Array

The problem of locating signals transmitted in the proximity of an antenna array has been studied extensively in the signal processing literature. In this paper, we consider the standard array manifold models used in these works and show that they differ, sometimes significantly, from the model based on electromagnetic theory. In particular, the standard models do not correspond to the equations governing an electromagnetic field near an antenna or an array. They also do not take into account the characteristics of the near-field source, such as the type and orientation of the transmitting antenna, which may have a profound impact on the signals received by the array. We use selected numerical examples based on a numerical electromagnetic code to illustrate the various issues raised herein.

Moving target modelling and indication in MIMO GEO SAR

Multiple-input multiple-output geosynchronous synthetic aperture radar (MIMO GEO SAR), composed by several mini-satellites in GEO orbits, has more spatial degrees of freedom, which helps to suppress clutter and improves the performance of ground moving target indication (GMTI). However, in order to ensure the satellite safety, the satellite spacing in MIMO GEO SAR is very large. It results in the failure of far-field assumption and causes the moving target defocusing because of the mismatch between the steering vector in far-field sources and the moving target echo. Here, a new signal model based on the near-field sources is first given according to the GEO SAR satellite-ground geometry relationship. Then, considering the phase difference between the different channels, the spatial steering vector is derived and the weight vector for adaptive processing is proposed to suppress clutter and indicate the moving target. Finally, computer simulations are carried out for the validation of the proposed method.

Susceptibility inversion of near-field magnetic sources and its application

Susceptibility inversion of near-field magnetic sources is of great application significance in the field of magnetic target detections, such as mine clearances and buried pipeline detections. This paper proposes a method of three-dimensional inversion imaging of magnetic sources in near-field with unknown shape, position and magnetization state. The method transforms the total field norm anomaly ΔT on the observation surface into the total field vector anomaly ||Ta|| and then constructs an objective function aiming at minimizing the deviations between the measured and predicted magnetic anomalies. The magnetic dipole equivalence principle is utilized to calculate the kernel matrix. The Gauss-Newton and conjugate gradient algorithms are utilized together to solve the inversion optimization process. The range of the magnetic susceptibility is constrained by using nonlinear transformations. Simulations and measurement experiments demonstrate the proposed method is capable to realize 3D inversions of curved pipes, intersecting pipes, separated magnetic sources, and “L” shaped steel pipes under near-field conditions. Through the actual magnetic field measurement experiments and the implementation of the proposed optimization inversion algorithm, accurate inversion of the magnetic susceptibility distribution and magnetic imaging of the “L” shaped steel tube was realized, testifying that the proposed method is feasible to be applied to the detection of buried pipelines.

Passive Localization of Near-Field Sources Based on Overlapped Subarrays

In this paper, a new algorithm for near-field source localization is proposed based on a uniform linear array (ULA). First, the direction-of-arrivals (DOAs) of near-field sources at two symmetric phase points are obtained by using two overlapped subarrays of the ULA and second-order statistics-based method. Next, the DOA of each source at the phase origin is determined by exploiting the geometric relationships between the source and two phase points. Finally, with the DOA estimates, the ranges of near-field sources can be estimated by one-dimensional search. The proposed method involves neither two-dimensional search nor higher-order cumulant calculations and avoids parameter pair-matching process. Moreover, it can achieve higher accuracy than the existing efficient method. Numerical simulations are presented to verify the performance of the proposed method.

Near-Field Cross-Correlation Analysis for MIMO Wireless Communications

We propose a source-based approach to the analysis and evaluation of cross correlation in generic near-field (NF) interaction scenarios. The proposed theory integrates electromagnetics and stochastic considerations to derive a general formula for Rx port-to-port cross correlation. The derivation is quite general and is expressed in terms of the stochastic correlation tensor of arbitrary random NF source cloud close to the Rx multiple-input-multiple-output (MIMO) antenna terminals. The theory is then simplified by computing the response to a random NF point source, allowing fast and efficient computations when the NF source is replaced by an equivalent infinitesimal dipole model. The resulted formula is then exploited to devise a new cross-correlation performance evaluation metric suitable for the design of optimum NF spatial diversity applications antennas. The proposed approach and performance evaluation metric are anticipated to play a direct role in NF communication systems antenna design in particular, and stochastic electromagnetics in general.

Assessment of Children Exposure Variability to Near-Field Sources using Stochastic Dosimetry.

In this paper, the exposure of a child to a hairdryer model is evaluated. Nowadays, the assessment of children exposure to near-field sources has become in fact a topic of high interest, because it was found that even domestic appliances could be relevant for children exposure level. Therefore, the aim of the present work is to use a method based on stochastic dosimetry to assess the exposure variability due to near-field sources, not limiting it only on some worst-case exposure scenario. In particular, electric field amplitudes induced in specific tissues composing the central nervous system and the peripheral nervous system (following the ICNIRP guidelines) were analyzed. The results highlight a high exposure variability depending on the hairdryer position in respect with the child.

ULA‐based near‐field source localisation in cognitive femtocell network: a comparative study of genetic algorithm hybridised with pattern search and swarm intelligence

The rapidly proliferating wireless technology brings an overwhelming increase in wireless applications at a continuous pace. In this framework, a major proportion of overall voice and data traffic originates from indoor users, directing the research world towards the deployment of low-power, low-cost, and low-range femtocell networks in the existing macrocells. However, this development will result in cross-tier interference as well as interference to the primary or licensed users. Cognitive femtocell networks smartly address this challenge through their intelligent sensing and decision-making capabilities. This study proposes a joint spectrum sensing technique, which involves individual sensing by multiple cognitive femtocell base stations (CFBSs), each equipped with a uniform linear array (ULA) of passive sensors. The local observations of CFBSs are then evaluated at the fusion centre to make final decision applying majority rule. In addition to the detection of a number of active primary femtocell networks (PFNs), the proposed near-field source localisation technique provides four-dimensional parameter estimation of each detected PFN signal, i.e. amplitude, angle-of-arrival, frequency, and range. Finally, the proposal is supported by three different implementations, i.e. Hybrid Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony. It also outsmarts an existing single-ULA based spectrum sensing technique in the literature.

Circular array design based on Bayesian Cramer–Rao bound

In this paper, we consider circular array design in the presence of a far-field or a near-field signal source. The location of the source is introduced to our optimization problem by its probability density function (PDF) as a priori information. We consider Bayesian Cramer–Rao bound as the cost function to be minimized to specify the best locations of the sensors on a circular boundary. In the far-field case, a closed form solution is derived for an arbitrary PDF of the bearing. In the near-field scenario, we divide the design problem into three categories: known source bearing, known source range, and the general case. We present some examples to exhibit the process of array design by the proposed method. Finally, we show the array optimized by the proposed method outperforms arrays with other configurations in source localization.

Quaternion-MUSIC for near-field strictly noncircular sources with large-scale polarization array

In this paper, a novel localization method is proposed for DOA, range and polarization estimation of near-field noncircular sources in massive multiple-input-multiple-output (MIMO) systems. Compared with traditional MUSIC-based algorithms, the proposed algorithms can separate the polarization parameters from the spatial spectrum function, avoiding the four-dimensional (4-D) spectrum search and realizing the fast localization of the near-field source with high accuracy. First, the dimension-reduced MUSIC (DR-MUSIC) algorithm is proposed for DOA and range estimation with low computational complexity, and given a closed-form expression of polarization estimation. Next, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. In addition, the analysis of the computational complexity and simulations of the proposed method are provided, showing that the proposed method yields a better performance than DR-MUSIC in massive MIMO systems.

An effective localization method for mixed far-field and near-field strictly non-circular sources

In this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the non-circularity of the sources, an extended signal is generated by concatenating the received array data and its conjugate counterparts. Then the DOAs of far-field signals are estimated based on the extended covariance matrix with the traditional MUSIC algorithm. After eliminating the far-field components from the extended signal subspace, the extended covariance matrix of the near-field signals is obtained. Thus a near-field estimator is constructed based on symmetric property of the extended array manifold where the generalized ESPRIT method is adopted to estimate the DOAs of near-field sources. Finally, the range estimator is derived using the DOA estimations of near-field sources. Simulation results are provided to validate that the proposed method has achieved a better performance than existing ones and is quite suitable for massive MIMO (multiple-input multiple-out) system.

Photoluminescence Activation of Organic Dyes via Optically Trapped Quantum Dots.

Laser tweezers afford quantum dot (QD) manipulation for use as localized emitters. Here, we demonstrate fluorescence by radiative energy transfer from optically trapped colloidal QDs (donors) to fluorescent dyes (acceptors). To this end, we synthesized silica-coated QDs of different compositions and triggered their luminescence by simultaneous trapping and two-photon excitation in a microfluidic chamber filled with dyes. This strategy produces a near-field light source with great spatial maneuverability, which can be exploited to scan nanostructures. In this regard, we demonstrate induced photoluminescence of dye-labeled cells via optically trapped silica-coated colloidal QDs placed at their vicinity. Allocating nanoscale donors at controlled distances from a cell is an attractive concept in fluorescence microscopy because it dramatically reduces the number of excited dyes, which improves resolution by preventing interferences from the whole sample, while prolonging dye luminescence lifetime due to the lower power absorbed from the QDs.

A new closed-form algorithm for 3-D single near-field source

ABSTRACT Recently, a single source location problem has attracted special attention in many previous works. A method was lately proposed to estimate three-dimensional (3-D) parameters (i.e. range, azimuth angle, elevation angle) for single source using a geometry restriction-free Uniform Circular Array (UCA), whereas it required a strong presupposition. In this letter, considering the problem of estimating 3-D parameters of a single near-field source, we expand the signal model to a generalised one based on the previous work. This expansion is more available to common situations including near-field condition. In addition, in contrast to the previous work, the proposed method is shown to be more accurate and computationally efficient. Numerical simulations are provided to verify the performance of the proposed method.

Classification and Localization of Mixed Sources after Blind Calibration of Unknown Mutual Coupling

In order to deal with the problem of passive mixed source localization under unknown mutual coupling, the authors propose an effective algorithm. This algorithm provides array blind calibration as well as classification and localization of mixed sources in this paper. In practice, an ideal sensor array without the effects of unknown mutual coupling is rarely satisfied, which degrades the performance of most high-resolution algorithms. Firstly, the directions of arrival of far-field sources and the number of nonzero mutual coupling coefficients are estimated directly through the rank-reduction type method. Then, these estimates are adopted to reconstruct the mutual coupling matrix. In addition, the fourth-order cumulant technique is required to eliminate the Gauss colored noise effects caused by mutual coupling calibration of the raw received data vector. Finally, in an algebraic way, the results of rapid classification and localization of near-field sources are obtained without any spectral search. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

Generalised two stage cumulants‐based MUSIC algorithm for passive mixed sources localisation

In this study, the authors address the problem of passive mixed near-field and far-field sources localisation using a uniform linear array (ULA) in which the signals received by the array may come from mixed sources. This study presents a new two stage cumulant-based multiple signal classification (MUSIC) algorithm for passive source localisation using fourth-order cumulants of a ULA data. The significant characteristic of the proposed algorithm is that it constructs a new special cumulant matrix to acquire more information of signals received by a ULA. Consequently, the proposed algorithm gives high direction of arrival (DOA) and range estimation accuracy, and alleviates the array aperture loss. Monte Carlo simulations are established to verify the effectiveness of the proposed method in increasing direction of arrival and range estimation accuracies.

Application of EMR Signature in Health Assessment and Monitoring of IGBT-Based Converters

In this paper, a non-invasive method based on electromagnetic radiation (EMR) signature is proposed for the health assessment of the converters. Converters use insulated gate bipolar transistor (IGBT) because of its robustness. However, it suffers from internal degradation due to the rapid power cycle and thermal stress. Degradation of IGBT primarily increases the turn- off time as a consequence of elevated junction temperature. The coupling of IGBT with the parasitics of the circuit results in EMR and depends on the turn- off time of IGBT. It has been found that the EMR signature reduces with the increase in turn- off time. This concept is taken forward for the health assessment of the converters. However, in a practical scenario, the EMR generated from multiple converters get mixed up. This is commonly known as a problem of near-field source localization. We use a uniform linear array to capture the EMR signals near the converters. Subsequently, ESPRIT and MUSIC algorithms are used to localize all the converters. An inverse transformation of the localization algorithm separates the EMR signature of all the individual converters. The proposed health assessment algorithm computes the degradation level of converters, and the experimental results validate the proposed approach.

Localization of mixed near-field and far-field multi-band sources based on sparse representation

In this paper, a novel source localization algorithm using a uniform linear array is proposed for scenarios where both the near-field (NF) and far-field multi-band sources may exist simultaneously. The proposed method is performed in two stages. In the first stage, we firstly exploit some spatial correlations of each frequency output component to construct a virtual array output and represent the virtual array output on a corresponding overcomplete basis or dictionary which is only related to the direction-of-arrivals (DOAs) of sources. And then we can establish a multiple-dictionary sparse representation model. Finally, we estimate DOAs of the incident sources by solving the weighted $$\ell _1$$-norm minimization problem. In the second stage, every frequency output component is firstly represented on a corresponding mixed overcomplete basis with the estimated DOAs, and then a multiple-dictionary sparse representation model is created. At last, the ranges of the NF sources are estimated using the weighted $$\ell _1$$-norm minimization, and the types of sources are also distinguished. The proposed algorithm avoids parameter pair-matching and two-dimensional search, and does not require a prior knowledge of source number. Simulation results indicate that the proposed algorithm can provide an improved localization accuracy and locate more sources than the existing techniques for the case of multi-band sources.

Underwater Robot Detection System Based on Fish’s Lateral Line

This paper introduces the near-field detection system of an underwater robot based on the fish lateral line. Inspired by the perception mechanism of fish’s lateral line, the aim is to add near-field detection functionality to an underwater vehicle. To mimic the fish’s lateral line, an array of pressure sensors is developed and installed on the surface of the underwater vehicle. A vibrating sphere is simulated as an underwater pressure source, and the moving mechanism is built to drive the sphere to vibrate at a certain frequency near the lateral line. The calculation of the near-field pressure generated by the vibrating sphere is derived by linearizing the kinematics and dynamics conditions of the free surface wave equation. Structurally, the geometry shape of the detection system is printed by a 3D printer. The pressure data are sent to the computer and analyzed immediately to obtain information of the pressure source. Through the experiment, the variation law of the pressure is generated when the source vibrates near the body, and is consistent with the simulation results of the derived pressure calculation formula. It is found that the direction of the near-field pressure source can distinguished. The pressure amplitude of the sampled signals are extracted to be prepared for the next step to estimate the vertical distance between the center of the pressure source and the lateral line.

Joint Space and Time Processing for Unknown Mutual Coupling Blind Calibration and Mixed Sources Identification Using Uniform Circular Array

In classification and localization of mixed far-field and near-field sources, the unknown mutual coupling degrades the performance of most high-resolution algorithms. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. This paper proposes an effective algorithm of mixed sources identification using uniform circular array under unknown mutual coupling. Firstly, according to rank reduction and joint space–time processing, the directions of arrival of far-field sources is estimated directly without mutual coupling elimination. Addition, the joint space–time processing can improve the estimation results in the case of low signal noise ratio of incoming signal sources and small number of snapshots. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through spatial search after mutual coupling effects and far-field components elimination. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

Closed-Form Expression of Stochastic CRB for Mixed Near-Field and Far-Field Sources in Multipath Propagation Environments

In recent years, the source localization has had an increased interest in regard to the presence of mixed near-field sources (NFSs) and far-field sources (FFSs). The problem of localization of multiple mixed NFSs and FFSs, including uncorrelated, partially correlated, and coherent signals, has not been studied. In this letter, for the first time, the mathematical model of received signals is introduced in the presence of mixed sources for complex multipath environments. Then, a closed-form expression is extracted for the stochastic Cramer–Rao bound associated with the above scenario.