Short-time Matrix Pencil Method Research Articles

Clutter Effect on a Combination of Microwave Imaging and Target Identification Using Ground-Penetrating Radar

This paper presents an experimental investigation of the effect of clutter on the performance of microwave imaging and target identification using ground-penetrating radar (GPR) for buried objects. Signals collected by the GPR were formed to be B-scan and 3D images to carry out the microwave imaging of buried objects. Poles extracted using the short-time matrix pencil method from a late-time response of the received signal collected at a specific position were exploited to identify buried objects. The experiment was set up by constructing a large box filled with sand where an L-shaped metallic sheet was buried. The GPR was installed along with a 2-dimensional scanner above the box to image and identify the buried metallic sheet. In order to investigate the effect of the clutter on the performance of the GPR-based microwave imaging and target identification, small rocks were arranged on the sand surface, and their density was then varied. The experimental results have shown that clutter density significantly affects the sharpness of the B-scan and 3D images. The rocks result in an additional surface layer in the images. Moreover, the results have also shown that the clutter does not affect the performance of the identification of the buried object. From the results, the poles obtained from the GPR scanning inside and outside the buried object region differed. This confirms that the poles of the buried object should be different from those without the object. The poles of the buried object remain constant, although rocks on the sand surface exist since the clutter affects only the early-time response (not the late-time response). With varying clutter densities, the underlying poles were significantly changed, but this does not affect the identification of the buried object.

Waveform Measurement Unit-Based Fault Location in Distribution Feeders via Short-Time Matrix Pencil Method and Graph Neural Network

This article proposes the use of the Short-Time Matrix Pencil method (STMPM) and Graph Neural Network (GNN) for fault location in active distribution feeders based on an emerging class of sensors, known as Waveform Measurement Units (WMUs). WMUs record synchronized voltage and current waveforms in the time domain with high sampling rates. The proposed fault location framework consists of two stages. In the first stage, STMPM is adopted to capture the dominant modes of the transient changes of WMUs' sinusoidal signals due to faults in different locations of the distribution grid. The second stage is to use a grid-informed GNN model to identify the fault location and type using the captured features of the signal before, during, and post-fault with STMPM. GNN can capture the spatial-temporal relationship between data from different sensors in different locations to enhance situational awareness and fault location accuracy. The proposed method is examined on a modified IEEE network with distributed energy resource (DER) and for transient symmetrical and asymmetrical faults under different loading, DER generation level, noises, and sensors' sampling rate conditions. The results show the merits of the proposed two-stage fault location framework compared to the conventional approaches; while a challenging problem is addressed in active distribution grids.

Automatic Object Discrimination Based on Natural Resonant Features of Dielectric Coated Object

An electromagnetic signal scattered from a radar target consists of resonance information, which can be utilized to achieve automatic target discrimination. To extract this resonant information, the short-time matrix pencil method (ST-MPM) is one of the robust approaches. The library of unique extinction pulses (E-pulses) is generated using the target’s resonant poles. Then, the quantitative discrimination measures of E-pulse discrimination number (EDN) and target discrimination number (TDN) are calculated. Recently, the radar targets are evolved with complex structures along with dielectric-coated materials, thus creating difficulty in target recognition. This communication focuses on the discrimination of dielectric-coated composite objects using extracted resonant poles and automatic discrimination measures. The demonstration of a discrimination approach is done based on simulation and measurement tests using complex scale models of aircraft. The robustness of the TDN measure is also investigated by analyzing noise-contaminated object returns for a range of signal-to-noise ratios (SNRs). This analysis shows that the TDN provides superior discrimination for all considered target sets.

The Identification of Travelling Waves in a Voltage Sensor Signal in a Medium Voltage Grid Using the Short-Time Matrix Pencil Method

Most of the fault wave localization methods are based on the analysis of line current transformed by current transformers and are limited to high voltage grids. Fault wave localization in medium voltage grids is still being developed. This paper presents a new real-time algorithm for the identification of travelling waves in a distribution grid using voltage signal and the short-time matrix pencil method. To obtain the secondary side voltage waveforms at substation, the model of a resistive voltage sensor based on the broadband measurements from 10 Hz to 20 MHz was developed. The tested sensor amplifies the frequencies associated with travelling waves more than utility frequency allowing for the identification. Short-circuit simulations on the IEEE 34-bus feeder was performed to test the algorithm. The developed method can detect even the waves of low amplitude.

Traveling wave arrival time detection for two-terminal non-contact measurement based on short time matrix pencil scheme

This paper presents an accurate/efficient scheme to distinguish the first and successive time of arrival signals from the fault points of the transmission lines based on non-contact measurement of traveling wave dataset and short time matrix pencil method. In multiple reflection and refraction of traveling waves at characteristic impedance, the first and successive waves overlap at the local and remote terminals, introducing some difficulties in detecting the reflected wave accurately. In such a situation, the detection of the first reflected wave from the second successive wave becomes a challenge, which leads to difficulties in the detection process. Therefore, with the use of a non-contact measurement signal of the overhead transmission line, and with the short-time matrix pencil method of the non-contact dataset, the waves corresponding to the first and successive time reflected signals are distinguished. The simulation results are discussed, and the validity measurement results from the traveling wave generator have been evaluated and analyzed effectively. This indicates that the proposed scheme is robust and accurate for the time of arrival detection for fault location in overhead transmission lines using a non-contact traveling wave dataset with a short time matrix pencil scheme.

Robust Chipless RFID Detection Using Complex Natural Frequency Along With the k-Nearest Neighbor Algorithm

This paper proposes a detection technique for the chipless RFID system. In the proposed technique, the experiments for measuring the scattering responses of all possible tags were conducted first in a free-space anechoic chamber in order to avoid the effect of unwanted signals due to the environment. The responses of all possible tags were exploited in order to extract poles, including natural frequencies and damping factors, by using the short-time matrix pencil method. All extracted poles successively chosen from the late-time portions were exploited to create the decision boundary by using the k- nearest neighbor algorithm. In order to validate the robustness of the proposed detection technique, experiments with the chipless RFID system with tags attached to containers, i.e. parcel and plastic boxes, were conducted. Poles extracted from the response of the tag attached to a container were fed to the decision boundary for ID detection. The poles that had fallen into the region labeled as logic “1” were detected as logic “1,” and vice versa . The experiment results showed that the attachment of the tags to the containers caused a change in the poles. The conventional technique using only natural frequencies has exhibited poor performance regarding tag ID detection. On the other hand, the proposed detection technique achieved a 100% detection rate, although the extracted poles used to detect the bit logic were disturbed by the container. The experimental results confirm the superiority of the proposed system over conventional chipless RFID detection. Moreover, the proposed technique can be considered a robust detection technique.

Application of short‐time matrix pencil method for high‐frequency damage detection in structural system

Structural health monitoring (SHM) has been becoming a key element of monitoring and maintenance in modern and smart cities with critical infrastructures. Within the past decade, SHM approaches are widely used in bridges, roads, skyscrapers, critical facilities, and buildings to enhance the public safety and cost efficiencies of infrastructure maintenance under extreme events, such as earthquakes. Several advanced wave-based methods, such as sliding discrete Fourier transform, discrete wavelet transform (DWT), and Hilbert transform were broadly used to detect different failure modes of systems. One of the most important deficiencies associated with the state-of-the-art approaches is their high level of sensitivity to the sampling frequency, measurement noise, and damage-induced pulse parameters. To fill this technical gap, for the first time, this paper proposes a high-frequency damage detection approach based on the short-time matrix pencil method (STMPM) that has been used in electrical engineering lately. The advantage of STMPM compared with the similar approaches is using the complex frequencies that are not used before in damage detection applications. In this study, pounding between two adjacent buildings is considered as a case study of high-frequency damage scenario. The proposed STMPM-based detection technique is compared with DWT with commonly used mother wavelets. According to the simulation results, the STMPM-based detection is more robust compared with DWT-based techniques and has lower sensitivity to the noise parameters and sampling rate.

Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar

This paper presents the mapping of the physical and dielectric properties of layered soil by using ground-penetrating radar (GPR). Poles extracted from the GPR signals by using the short-time matrix pencil method, along with preprocessing, including filtering and antenna calibration, were employed in order to map the physical and dielectric properties of the layered soil. With the proposed system, the conjugate gradient method was also introduced to solve the time-domain inverse problem faced in the antenna calibration. Experimentations were conducted on four different days in Nakhon Ratchasima province in Thailand, which is a potential area for the occurrence of hardpan. These were done based on the hypothesis that the soil properties, such as water content and dielectric constant, should change when the experimentation day changes in spite of the experimentations in the same area. In the experimentations, soil samples were collected using a core method, and their physical and dielectric properties were measured by using a standard laboratory method and a commercial dielectric probe kit. The measured soil properties and extracted poles for each experiment and each soil layer are shown and analyzed. The results indicate that the real part of the dielectric constants, strongly related to water content, can be mapped using extracted natural frequency. In order to map the dielectric properties, i.e. the water content and dielectric constants of the layered soil, the experimental results were fitted to $2^{\mathrm {nd}}$ -order polynomial curve. The largest regression value of the fitted curve was 0.9994. The bulk density, which is a physical property of the soil, distinguishes the soil type here differentiated by different soil layers. According to the experimental results, the hardpan occurred at the second soil layer because its bulk density was higher than 1.8 g/cm 3 . The bulk density was mapped by extracted poles, including damping factors and natural frequencies.

On the Resolution Improvement of Radar Target Identification with Filtering Antenna Effects

An investigation on the improvement of the resolution of a radar target identification system is presented in this paper. Degradation of resolution is mainly due to influence factors associated with antennas, including the strong coupling between transmitting and receiving antennas and the variation in the antenna response. A filtering technique was therefore introduced to mitigate the underlying problem. In the technique, the antenna effects were filtered out of the total response backscattered from the objects in the radar target identification system. The short-time matrix pencil method (STMPM) was then employed to extract the poles from the backscattered response in order to identify the object. Simulation and experimentation examples are illustrated to confirm the improvement of the resolution by filtering the antenna effects. The simulation and experimentation were divided into several categories, that is, different antennas and differently shaped objects, in order to validate the advantage of filtering the antenna effects. They were setup in order to demonstrate that the poles obtained from performing the STMPM without the filtering technique were mainly because of the antenna rather than the object’s characteristic. The results showed that the resolution of the identification was significantly increased when performing pole extraction and filtering the antenna effects.

Traveling-Wave Detection Technique using Short-Time Matrix Pencil Method

This paper proposes a new technique for detection of traveling-wave arrival times in power systems. Precise estimation of wave arrival times improves most of the traveling-wave-based fault location methods. The proposed technique is based on the short-time matrix pencil method that decomposes the measurement samples in a sliding window into time-indexed complex frequencies. Traveling-wave arrival times are estimated based on the fact that the extracted damping factors (i.e., the real part of the complex frequencies) are zero when the arriving wave is located in the middle of the sliding window. The proposed technique is compared to the existing transient-detection techniques such as discrete wavelet transform with commonly used mother wavelets and Hilbert transform (HT), using MATLAB Wavelet Toolbox and MATLAB HT. The sensitivity analysis based on the simulation results demonstrates that the proposed technique is less affected by the measurement noise, sampling frequency, and fault parameters.

Accurate Extraction of Early-/Late-Time Responses Using Short-Time Matrix Pencil Method for Transient Analysis of Scatterers

We present a new efficient technique for accurately distinguishing the early-time and late-time modes of the transient response from multiscatterer targets. In multiple resonant-based scatterers where the complex natural resonances (CNRs) are used as the ID of the target, the early-time response of the second scatterer might overlap with the late-time of the first, introducing some difficulties in the detection of the second scatterer and additionally, identification of the first one. In such circumstances, the separation of the late-time CNRs (ID of the scatterer) from the early-time poles (scattering centers) is challenging. The smaller the distance between two scatterers, the more difficult the detection process is. Here, by sliding a window along the time axis and applying short-time matrix pencil method (STMPM), the poles corresponding to the early-time and late-time responses are distinguished. It will be shown that when the early-time response is located at the center of the sliding window, its CNRs are located on the imaginary axis of the pole diagram. By monitoring the zero-crossing points in time damping-factor diagram, the locations of the early-time responses (or equivalently scattering centers) can be detected. Some scenarios are discussed, simulated, and confirmed by measurement results to show the effectiveness of the proposed technique in practical applications.

On the Application of Short-Time Matrix Pencil Method for Wideband Scattering From Resonant Structures

A time-frequency technique called short-time matrix pencil method (STMPM) is efficiently employed to analyze wideband scattering from resonant structures. After investigating various parameters of the technique such as time-window length and filtering parameter, some scenarios are studied. First, the proposed technique is applied to the backscattered response from two dipoles spaced 20 cm from each other. The dominantly excited resonances and scattering centers are successfully derived from the time-frequency diagram. Finally, the proposed technique is applied to the time-domain backscattered response from an open-ended circular cavity. Different scattering mechanisms such as resonant, scattering center, and dispersion are studied in the time-frequency diagram of the response. The results are compared with those of the short-time Fourier transform (STFT) and reassigned joint time-frequency (RJTF) techniques. Good resolution in time and frequency domains and its capability of finding low-energy excited poles of the scatterer are the major features of the proposed technique. By utilizing the proposed technique, the damping factor or equivalently the quality factor of the resonances are straightforwardly obtained, which can be useful in the study of resonant-based scatterers.

A Space–Time–Frequency Anticollision Algorithm for Identifying Chipless RFID Tags

In this paper, the short-time matrix pencil method (STMPM) is efficiently employed as a time-frequency analysis methodology for the identification of multiple chipless radio-frequency identification (RFID) tags presented in the main beam of the reader antenna. Each tag represents a unique ID in the backscattered signal. Here, the data are embedded on the tag as the complex natural resonances (CNRs) of the structure. In the cases where more than one tag exist in the main beam of the reader antenna, the frequency-domain response is not sufficient to successfully distinguish the IDs of the tags. By applying the STMPM technique to the time-domain response, the poles and their residues are obtained at each snapshot of time. In order to obtain the exact values of the turn-on times of the poles, which are the roundtrip time of the tags to the antenna, high resolution in the time domain is needed which deteriorates the resolution in the frequency domain. Using mathematical descriptions, a simple formulation is derived which exhibits a duality between late-time response in the time domain and early-time response in the frequency domain. Therefore, a space-frequency representation can be simply obtained from the application of the short-frequency matrix pencil method (SFMPM) to the frequency-domain response of the tags. By possessing the exact values of the turn-on times and pole/residues of the tags, their backscattered responses can be reconstructed and associated IDs can be retrieved. With the proposed method in this paper, some scenarios are studied and discussed. As an experimental result, three 3-bit tags with different IDs (ID1:101, ID2:111, and ID3:011) are fabricated and placed away from each other. Exploiting the presented technique, the IDs of the tags and their locations are successfully distinguished. The simulation results are validated by the measured ones. The ranging error obtained by the proposed method is less than 1 cm.

Complex-Natural-Resonance-Based Design of Chipless RFID Tag for High-Density Data

Each scatterer responds to the incident wave in a unique manner. The backscattered signal from the scatterer is affected by two distinct phenomena: early-time response which emanates from the scattering centers of the scatterer and late-time response which is the summation of the complex natural resonances (CNRs) with some weighting residues. Based on the singularity expansion method (SEM), CNRs are aspect-independent parameters which include some structural information of the scattering target. Based on this fact, the data can be embedded on the RFID tag as CNRs associated to the structural parameters. In this paper, a low-profile chipless RFID tag as a suitable device for high-density data is introduced. By incorporating several slots on the chipless RFID tag, the data can be encoded as CNRs on the structure. The designed RFID tag operates in the frequency band of 3.1-10.6 GHz. The data are decoded in the reader by applying short-time matrix pencil method (STMPM) to the transient backscattered response of the tag. In addition, the turn-on times, damping factors, and resonant frequencies of the poles are distinguished. After studying the effects of various parameters of the tag, a 24-bit tag is designed on the area of 24 × 24 mm2, simulated and measured. The measurement is performed in the frequency domain and, after applying inverse fast Fourier transform (IFFT), STMPM is employed to extract the signature of the tag. The tag is successfully read remotely via its scattered fields.

Short-Time Matrix Pencil Method for Chipless RFID Detection Applications

In this paper, the concept of short-time matrix pencil method (STMPM) is introduced and experimentally demonstrated as an efficient approach to extract a set of embedded poles in a chipless RFID. Here, by incorporating a few notches on the elliptical dipole antenna as a chipless RFID, the data is encoded as complex natural resonant frequencies of the structure. In this new detection approach, a sliding time window is introduced and the matrix pencil method (MPM) is applied to extract embedded poles and residues from the sliding time-window at each snapshot of time. The resulting complex poles and residues have a time index which allows us to process them as a set of time–frequency data. This time–frequency analysis enables us to discriminate late-time from early-time. Furthermore, averaging complex poles over the time increases the accuracy of the technique in a noisy environment. The measured data agrees well with our simulations and support the STMPM effectiveness in comparison to the MPM.