Linear Sensor Array Research Articles

Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation.

A compressive sensing joint sparse representation direction of arrival estimation (CSJSR-DoA) approach is proposed for wireless sensor array networks (WSAN). By exploiting the joint spatial and spectral correlations of acoustic sensor array data, the CSJSR-DoA approach provides reliable DoA estimation using randomly-sampled acoustic sensor data. Since random sampling is performed at remote sensor arrays, less data need to be transmitted over lossy wireless channels to the fusion center (FC), and the expensive source coding operation at sensor nodes can be avoided. To investigate the spatial sparsity, an upper bound of the coherence of incoming sensor signals is derived assuming a linear sensor array configuration. This bound provides a theoretical constraint on the angular separation of acoustic sources to ensure the spatial sparsity of the received acoustic sensor array signals. The Cramr–Rao bound of the CSJSR-DoA estimator that quantifies the theoretical DoA estimation performance is also derived. The potential performance of the CSJSR-DoA approach is validated using both simulations and field experiments on a prototype WSAN platform. Compared to existing compressive sensing-based DoA estimation methods, the CSJSR-DoA approach shows significant performance improvement.

Range and Direction of Arrival Estimation of Near-Field Sources in Sensor Arrays using Differential Evolution Algorithm

any algorithms have been proposed in recent years for source localization in near field. Some are based on subspace methods and some use evolutionary computing techniques. This article presents the performance of Differential Evolution (DE) algorithm for range and DOA estimation of near field narrow band sources, impinging on a uniform linear array (ULA) of passive sensors. Mean square error (MSE) is used as a fitness function because it requires only a single snapshot to converge and perform better even in negative SNR. The main contribution of this work is to explore the effectiveness of DE without hybridization for uniform linear arrays. The results of DE are compared with the results of Genetic Algorithm (GA). The effectiveness of both the algorithms is tested on the basis of a large number of Monte-Carlo simulations and their statistical analysis. KeywordsEvolution, Direction of Arrival, Evolutionary Computing, Near field, Source Localization.

A sparse recovery technique for the turbulent boundary layer wavenumber-frequency spectrum with non-uniformly spaced array of pressure transducers

Historically, determination of wavenumber-frequency spectra of turbulent wall pressure fluctuations has employed the use of evenly spaced linear arrays of sensors. The reason for this is that even spacing of pressure samples in both the temporal and spatial domains facilitates analysis by direct application of 2-D Fourier transforms. The spacing of pressure measurements directly impacts the resolution of the resulting wavenumber-frequency spectra. This rigid spacing requirements of traditional methods may preclude analysis altogether with even a single sensor failure within the array. Rather than determining sensor locations based on the simplest analysis method, the sparseness of the wavenumber-frequency spectra can be leveraged to allow non-uniform sampling in both temporal and spatial domains. Sparse recovery techniques as an analysis tool for wavenumber-frequency spectra will be demonstrated both theoretically and experimentally for uniformly spaced arrays, uniformly spaced arrays with missing measure...

A Two‐Dimensional Plum‐Blossom Sensor Array‐Based Multiple Signal Classification Method for Impact Localization in Composite Structures

AbstractThe growing use of composite materials on aircraft structures has attracted much attention for the impact monitoring as a kind of structural health monitoring method. Uniform linear sensor array (ULSA)‐based multiple signal classification (MUSIC) technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, the monitoring range of ULSA‐based MUSIC method is 0°–180°, and its beamforming properties degrade at angles close to 0° and 180°. Besides, the ULSA‐based MUSIC methods proposed require the knowledge of the direction dependent velocity profile obtained by additional experiments. This article presents a novel two‐dimensional (2‐D) plum‐blossom sensor array (PBSA)‐based MUSIC method. First, the velocity propagating at the specific direction is estimated by impact signal itself using PBSA directly. Second, 2‐D PBSA‐based MUSIC method well realizes omnidirectional 0°–360° impact localization of composite structures. Experimental results show its successful performance on epoxy laminate plate and complex composite structure.

Angle-Polarization Estimation for Coherent Sources with Linear Tripole Sensor Arrays

We propose a parallel factor (PARAFAC) analysis-based angle and polarization estimation algorithm for multiple coherent sources using a uniformly-spaced linear tripole sensor array. By forming a PARAFAC model using the spatial signature of the tripole array, the new algorithm requires neither spatial smoothing nor vector-field smoothing to decorrelate the signal coherency. We also establish that the angle-polarization parameters of K coherent signals can be uniquely determined by PARAFAC analysis, as long as the number of tripoles . In addition, the proposed algorithm can offer enhanced angle and polarization estimation accuracy by extending the interspacing of the tripoles beyond a half wavelength.

Electrostatic monitoring of coal velocity and mass flowrate at a power plant

ABSTRACTIt is necessary to monitor and balance the pulverized coal velocity and flowrate among the conveying pipelines leading to injection burners and to optimize the air-coal ratio for safety, economical operation, and reduction of pollution of coal-fired boilers in power plants. A novel electrostatic sensing system is reported for monitoring the velocity and relative mass flowrate of pulverized coal. The design, implementation, and on-plant trials of the system are reported. The velocity of pulverized coal was determined by autocorrelating the output derived from a linear electrostatic sensor array. The relative concentration and mass flowrate were obtained by the root mean square values of the output of two ring-shaped electrodes with the consideration of the pulverized coal velocity. The instrumentation was evaluated on a full-scale 600 MW power generation unit. The results demonstrate that the technique provides reliable measurement of pulverized coal velocity, relative flowrate, and split distribution among the injection pipes from the same pulverizing mill. The prototype achieved split balance and optimized air/coal ratio in combination with the pulverized coal balancing control valve and proportional-integral-derivative controller in fuel injection pipes in a power plant.

Compressive Imaging of Moving Object Based on Linear Array Sensor

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

A Linear Array of Wind and Pressure Sensors for High Resolution in situ Measurements in Winter Tornadoes

A Linear Array of Wind and Pressure Sensors for High Resolution in situ Measurements in Winter Tornadoes

Fiber Bragg Grating Sensors-Based In Situ Monitoring and Safety Assessment of Loess Tunnel

Compared with electrical strain gauges, fiber Bragg grating (FBG) sensing technology is a relatively novel method for tunnel structural health monitoring, which has a number of advantages including high accuracy, multiplexing, electromagnetic interference resistance, and good repeatability. In order to study the internal force of the tunnel liner and detect the potential safety hazards, series of strain monitoring tests of a loess tunnel, taking into account the complex stress and strain variation of the loess during tunnelling, were performed by employing the tandem linear FBG sensor arrays controlled by the wavelength division multiplexing (WDM) technology. The concrete strain has obvious linear characteristics over time in the early stage and then gradually tends to a stable value. Moreover, after the necessary temperature compensation, loess tunnel structure safety was assessed through the analysis of real-time strain and internal force of the liner concrete, and the FBG monitoring data and safety assessment results indicate that the safety factors of various liner sections all meet the code requirements, which verify the safety and stability of the tunnel liner structure. The FBG sensors-based in situ monitoring technology can be well applied in the loess tunnel structure safety assessment.

Field-programmable gate array-based large-capacity sensing network with 1642 ultra-weak fiber Bragg gratings

A field-programmable gate array (FPGA)-based large-capacity sensing network with ultra-weak fiber Bragg gratings (FBGs) is proposed and experimentally studied. The demodulation system is constructed to interrogate 1642 serial time-division-multiplexing FBGs with a peak reflectivity of about −40 dB and equal separations of 2.5 m. Two semiconductor optical amplifiers and an InGaAs linear sensor array controlled by an FPGA are introduced to the demodulation system to achieve fast, precise, and flexible interrogation. The low crosstalk and spectral distortion are investigated through both theoretical analysis and experiments.

Comparison between DOA Estimation Subspace methods for incoherent and coherent signals

This paper considers the problem of estimating the direction of arrival (DOA) for the both incoherent and coherent signals from narrowband sources, located in the far field in the case of uniform linear array sensors. Three different methods are analyzed. Specifically, these methods are Music, Root-Music and ESPRIT. The pros and cons of these methods are identified and compared in light of different viewpoints. The performance of the three methods is evaluated, analytically, when possible, and by Matlab simulation. This paper can be a roadmap for beginners in understanding the basic concepts of DOA estimation issues, properties and performance.

A real-time plant discrimination system utilising discrete reflectance spectroscopy

An advanced, proof-of-concept real-time plant discrimination system is presented that employs two visible (red) laser diodes (635nm, 685nm) and one near-infrared (NIR) laser diode (785nm). The lasers sequentially illuminate the target ground area and a linear sensor array measures the intensities of the reflected laser beams. The spectral reflectance measurements are then processed by an embedded microcontroller running a discrimination algorithm based on dual Normalised Difference Vegetation Indices (NDVI). Pre-determined plant spectral signatures are used to define unique regions-of-classification for use by the discrimination algorithm. Measured aggregated NDVI values that fall within a region-of-classification (RoC) representing an unwanted plant generate a spray control signal that activates an external spray module, thus allowing for a targeted spraying operation. Dynamic outdoor evaluation of the advanced, proof-of-concept real-time plant discrimination system, using three different plant species and control data determined under static laboratory conditions, shows that the system can perform green-from-green plant detection and accomplish practical discrimination for a vehicle speed of 3km/h.

Impact imaging of aircraft composite structure based on a model-independent spatial-wavenumber filter

The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method.

Real-time classification of humans versus animals using profiling sensors and hidden Markov tree model

Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation–maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.

Analysis of the horizontal two‐dimensional near‐surface structure of a winter tornadic vortex using high‐resolution in situ wind and pressure measurements

AbstractThe horizontal two‐dimensional near‐surface structure of a tornadic vortex within a winter storm was analyzed. The tornadic vortex was observed on 10 December 2012 by the high‐resolution in situ observational linear array of wind and pressure sensors (LAWPS) system in conjunction with a high‐resolution Doppler radar. The 0.1 s maximum wind speed and pressure deficit near the ground were recorded as 35.3 m s−1 and −3.8 hPa, respectively. The horizontal two‐dimensional distributions of the tornadic vortex wind and pressure were retrieved by the LAWPS data, which provided unprecedented observational detail on the following important features of the near‐surface structure of the tornadic vortex. Asymmetric convergent inflow toward the vortex center existed. Total wind speed was strong to the right and rear side of the translational direction of the vortex and weak in the forward part of the vortex possibly because of the strong convergent inflow in that region. The tangential wind speed profile of the vortex was better approximated using a modified Rankine vortex rather than the Rankine vortex both at 5 m above ground level (agl) and 100 m agl, and other vortex models (Burgers‐Rott vortex and Wood‐White vortex) were also compared. The cyclostrophic wind balance was violated in the core radius R0 and outside the core radius in the forward sector; however, it was held with a relatively high accuracy of approximately 14% outside the core of the vortex in the rearward sector (from 2 R0 to 5 R0) near the ground.

Discrimination between authentic and false tax stamps from liquor bottles using laser-induced breakdown spectroscopy and chemometrics

This work describes the preliminary application of a compact and low-cost laser-induced breakdown spectroscopy (LIBS) instrument for falsification detection of tax stamps used in alcoholic beverages. The new instrument was based on a diode-pumped passively Q-switched Nd:YLF microchip laser and a mini-spectrometer containing a Czerny–Turner polichromator coupled to a non-intensified, non-gated, and non-cooled 2048pixel linear sensor array (200 to 850nm spectral range). Twenty-three tax stamp samples were analyzed by firing laser pulses within two different regions of each sample: a hologram and a blank paper region. For each acquired spectrum, the emitted radiation was integrated for 3000ms under the continuous application of laser pulses at 100Hz (integration of 300 plasmas). Principal component analysis (PCA) or hierarchical cluster analysis (HCA) of all emission spectra from the hologram or blank paper region revealed two well-defined groups of authentic and false samples. Moreover, for the hologram data, three subgroups of false samples were found. Additionally, partial least squares discriminant analysis (PLS-DA) was successfully applied for the detection of the false tax stamps using all emission spectra from hologram or blank paper region. The discrimination between the samples was mostly ascribed to different levels of calcium concentration in the samples.

Fast and efficient DOA estimation method for signals with known waveforms using nonuniform linear arrays

In this paper, a new approach is proposed to estimate the direction of arrival (DOA) of multiple non-coherent source signals with known waveforms but unknown gains based on a nonuniform linear sensor array. Unlike some previous methods, which estimate the DOA using spatial signatures of the signals with known waveforms, the proposed method first uses the known waveforms and mutually independent sensor measurement noises to establish a maximum likelihood estimation problem corresponding to multiple linear regression models, each containing the DOA and the gain information of all the source signals. Then, regression analysis is performed to estimate the coefficients of each linear regression model, and the well-known generalized least squares is used to obtain the estimates of the angles and gains from the estimated regression coefficients. The proposed method does not require a search over a large region of the parameter space, which is normally needed in ML-based DOA estimation methods. The effect of correlated sources on the performance of the parameter estimation is also studied. It is shown that the DOA and gain estimates are asymptotically optimal as the sources tend to be uncorrelated. Finally, simulation results that demonstrate the estimation performance of the proposed method are given.

Direction of Arrival Estimation Based on DDOA and Self-Organizing Map

An effective two-level self-organizing map (SOM) neural network for direction of arrival (DOA) of sound signals estimation is proposed. The approach is based on the distance difference of arrival (DDOA) and a uniform linear sensor array in a 2D plane; it performs a nonlinear mapping between the DDOA vectors and angles of arrival (AOA). We found that the topological order of DDOA vectors and AOAs of same signals is uniform; thus, the topological order preserving of SOM network makes it valid to estimate AOA through DDOA. From the results of simulations and lake experiments, it is shown that the network has the advantage of accuracy and robustness, can be trained in advance, and is easy to implement.

A Mathematical Algorithm of Locomotive Source Localization Based on Hyperbolic Technique

Recent trend shows that sensors situated on an axis in two-dimensional scenario measuring the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) of the emitting signal from a moving source can estimate the emitting signal’s position and velocity from the intersection point of hyperbola, which derives from TDOA and FDOA. However, estimating the location of an emitter based on hyperbolic measurements is a highly nonlinear problem with inconsistent data, which are created due to the measurement noise, the deviation between assumption model and actual field of the velocity, and so forth. In addition, the coefficient matrix of TDOA and FDOA equations set is singular in the linear sensor array network (LSAN). In this paper, a noniterative and simpler method is proposed to locate the instantaneous position of the moving source in LSAN by estimating the position and velocity based on TDOA and FDOA which does not have the convergence problem. In addition, the method avoids the singularity problem of LSAN by introducing the nuisance variables. The proposed method achieved the theoretical lower bound for near to far field with same and different velocity and different baseline of sensors in low to moderate noise.

Underwater Target Detection Based on Electric-Field Sensor Line Array

The electric-field, caused by corrosion and prevention of corrosion method was used to overcome the deficiency for acoustic detection system in underwater target detection. The research on electric-field detection was based on electric-field sensor linear array and digital signal processing technology. Firstly, the electric-field distribution feature was simulated by electric-dipole model in shallow sea. The electric-field sensor vertical line arrays were laid regularly to obtain electric-field signal. Then, the generalized likelihood ratio (GLRT) method was used to process target detection and parameter estimation. Finally, the simulation experiment was done and indicated that the method could accurately realize parameter estimation on target initial position, velocity and direction, etc all when the signal-to-noise ratio (SNR) was greater than 0dB. The error became larger as the SNR reduced. The GLRT still could detect target when the SNR reduced to-10dB and partial parameter estimation error became larger.