esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 2 Measurement Algorithms
### Space Vector Taylor-Fourier Algorithm
According to the three-phase definition of frequency deviation and ROCOF, they can be obtained, with the formulas reported in [13], from the estimated derivatives of the positive sequence synchrophasor at \(t_{r}\), that is as:  
\[\widehat{Af}(t_{r}) =\frac{1}{2\pi}\frac{\Im\Big{[}\hat{\mathbb{X}}_{+}^{(1)}(t_{r}) \cdot\hat{\mathbb{X}}_{+}^{*}(t_{r})\Big{]}}{\Big{|}\hat{\mathbb{X}}_{+}(t_{r })\Big{|}^{2}} \tag{36}\] \[\widehat{\text{ROCOF}}(t_{r}) =\frac{1}{\pi}\Bigg{(}\frac{\Im\Big{[}\hat{\mathbb{X}}_{+}^{(2)}( t_{r})\hat{\mathbb{X}}_{+}^{*}(t_{r})\Big{]}}{2\Big{|}\hat{\mathbb{X}}_{+}(t_{r })\Big{|}^{2}}-\frac{\Re\Big{[}\hat{\mathbb{X}}_{+}^{(1)}(t_{r})\hat{\mathbb{ X}}_{+}^{*}(t_{r})\Big{]}\Im\Big{[}\hat{\mathbb{X}}_{+}^{(1)}(t_{r})\hat{ \mathbb{X}}_{+}^{*}(t_{r})\Big{]}}{\Big{|}\hat{\mathbb{X}}_{+}(t_{r})\Big{|}^ {4}}\Bigg{)} \tag{37}\]  
From the above equations, it is clear that an order \(K_{+}\geq 2\) is needed to estimate frequency and ROCOF. The latency of the algorithm depends only on the length of the window and thus, in this case, is \(L=\frac{N_{\text{F}}-1}{2}T_{s}\).
## 2 Computational Model and Basic Assumptions  
Figure 1 shows a new mechanical model for the longitudinal vibration of an interaction system including soil and a solid pile. \(H\), \(r_{1}\), \(\rho^{\mathrm{P}}\), \(E^{\mathrm{P}}\) and \(\
u^{\mathrm{P}}\) represent the length, diameter, density, elastic modulus and Poisson's ratio, respectively, of a floating solid pile. The surrounding soil is composed of two parts, i.e., two zones: an inner disturbed annular zone and a semi-infinite outer undisturbed zone of surrounding soil. The inner zone of surrounding soil can be further divided into \(m\) annular sub-layers. The radial thickness and radius of the inner disturbed zone are \(b\) and \(r_{m+1}\), respectively. \(r_{j+1}\) is the outer radius of the \(j\)th annular sub-layer. \(\rho_{j}^{\mathrm{s}}\), \(\lambda_{j}^{\mathrm{s}}\), \(G_{j}^{\mathrm{s}}\), \(E_{j}^{\mathrm{s}}\) and \(c_{j}^{\mathrm{s}}\) denote the density, Lame constant, shear modulus, elastic modulus and viscous coefficient, respectively, of the \(j\)th disturbed sub-layer. The mechanical constants of the viscoelastic supports beneath the pile toe and surrounding soil are \(k^{\mathrm{P}}\), \(\delta^{\mathrm{P}}\) and \(k^{\mathrm{s}}\), \(\delta^{\mathrm{s}}\), respectively. The uniformly distributed excitation pressure is \(p(t)\). Besides this, the following assumptions are specified in the proposed mechanical model:  
1. The large-diameter pile is considered to be a Rayleigh-Love rod with linear elasticity and a uniform cross section, while the soil is an isotropic viscoelastic continuum with frequency-dependent viscous-type damping [38].
2. Within the inner zone of surrounding soil, two neighboring annular sub-layers are completely coupled at the interface.
3. The deformation of the simplified mechanical system is small. There is no interface sliding between the pile and soil.
4. Within the inner zone of surrounding soil, \(G_{j}^{\mathrm{s}}\) and \(c_{j}^{\mathrm{s}}\) are determined in terms of the following expressions: \[G_{j}^{\mathrm{s}}(r)=\left\{\begin{array}{cc}G_{1}^{\mathrm{s}}&r=r_{1}\\ G_{m+1}^{\mathrm{s}}\times f(r)&r_{1}<r<r_{m+1}\\ G_{m+1}^{\mathrm{s}}&r\geq r_{m+1}\end{array}\right.\] (1a) \[c_{j}^{\mathrm{s}}(r)=\left\{\begin{array}{cc}c_{1}^{\mathrm{s}}&r=r_{1}\\ c_{m+1}^{\mathrm{s}}\times f(r)&r_{1}<r<r_{m+1}\\ c_{m+1}^{\mathrm{s}}&r\geq r_{m+1}\end{array}\right.\] (1b) where \(f(r)\) is a parabolic function that describes the variance of construction disturbance for the \(j\)th disturbed sub-layer [22].  
The mathematical implementation for the derivation procedure of pile-soil dynamic vibration is shown in Figure 2.  
Figure 1: The simplified mechanical model.
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 3 Fundamental Equations for Measurement and Motion
### Equations for Motion
The integral coefficient denotes that:  
\[\mathbf{G}^{i}_{k}=\mathbf{G}\!\int_{t_{k-1}}^{t_{k}}\mathbf{\Phi}(t_{k},\tau)\mathrm{d}\tau \tag{23}\]  
For simplicity, the white noise will be denoted as \(\mathbf{w}^{i}_{k-1}\) in the following. The discretized state equation derived from Equation (22) then is:\[\delta\mathbf{x}_{k}^{i}=\mathbf{\Phi}_{k-1}^{i}\delta\mathbf{x}_{k-1}^{i}+\mathbf{G}_{k}^{i}\mathbf{w }_{k-1}^{i} \tag{24}\]  
where \(\mathbf{\Phi}_{k-1}^{i}\) denotes the state transition matrix from \(t_{k-1}\) to \(t_{k}\). According to Equation (24), the predicted state covariance matrix is:  
\[\overline{\mathbf{P}}_{k}^{i}=\mathbf{\Phi}_{k-1}^{i}\hat{\mathbf{p}}_{k-1}^{i}\mathbf{\Phi}_{k -1}^{\text{T}}+\mathbf{G}_{k-1}^{i}\mathbf{Q}_{k-1}^{i}\mathbf{G}_{k-1}^{\text{T}} \tag{25}\]
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
## 4 Mode Excitation Simulation and Verification  
Mode excitation is simulated with ANSYS to verify the above proposed method. The excitation signal is a sine wave modulated by the Hanning window with a frequency of 35 kHz as shown in Figure 7.  
In the simulation process, the excitation signal is applied on the rail at 4 m from the right end of the rail as shown in Figure 8 in order to avoid the influence of the rail end echo on the simulation results. Between 0.8 and 2.7 m from the excitation node, a set of data acquisition array is set at intervals of 5 mm. There are 380 data acquisition nodes.  
The two-dimensional Fast Fourier Transform(2D-FFT) is used on the acquired data to identify the phase velocity of the mode. The simulation results are compared with the frequency wavenumber dispersion curves calculated by SAFE method as shown in Figure 9.  
Figure 8: _Cont._  
Figure 7: Excitation signal.  
Figure 8: Node excitation simulation diagram. (**a**) Node excitation simulation diagram of mode 7; (**b**) node excitation simulation diagram of mode 3; (**c**) node excitation simulation diagram of mode 1.  
Figure 9: _Cont._  
In Figure 9, the result of 2D-FFT coincides well with the frequency wavenumber curve of the corresponding mode. The ordinate value of the wavenumber \(\xi\) can be acquired from Figure 9 when the abscissa \(f\) is 35 kHz. The relationship between wavenumber \(\xi\) and frequency \(f\) is shown in Equation (10):  
\[C_{p}=\frac{2\pi f}{\xi} \tag{10}\]  
Then the frequency \(f\) and wavenumber \(\xi\) are substituted into the Equation (10) to calculate the simulation phase velocity of each mode. The calculation results are shown in Table 2.  
It is found from Table 2 that the phase velocity errors of mode 7, mode 3 and mode 1 are 2.49%, 0.16%, 0.07% respectively. It is proved that the method of node excitation is effective, but there are still some interference modes. In order to excite a relatively single mode, it is necessary to study the method of mode enhancement and interference mode suppression.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Mode**} & **Simulated Phase** & **Theoretical Phase** & **Phase Velocity Error** \\  & **Velocity Value (m/s)** & **Velocity Value (m/s)** & **Phase Velocity Error** \\ \hline
7 & 2669.4 & 2737.6 & 2.49\% \\
3 & 2282.5 & 2286.1 & 0.16\% \\
1 & 1982.5 & 1983.8 & 0.07\% \\ \hline \end{tabular}
\end{table}
Table 2: Phase velocity of modes.  
Figure 9: Frequency wavenumber curves. (**a**) 2D-FFT result of mode 7; (**b**) 2D-FFT result of mode 3; (**c**) 2D-FFT result of mode 1.
## 3 Explicit Finite Element Method for the _u-p_ Formulation
### Explicit Integration Method in Time Domain  
To increase the computational efficiency, the finite element Equations (8) and (9) are computed using the explicit integration method [40] in time domain. The equations of the local node system of node \(k\) at the \(t+1\) time step can be expressed as:  
\[\mathbf{u}_{k}^{t+1}=2\mathbf{u}_{k}^{t}-\mathbf{u}_{k}^{t-1}+\Delta t^{2}\frac{1}{M_{dk} }\sum_{L=1}^{n}\mathbf{f}_{k,u}^{t,t}+\Delta t^{2}\frac{1}{M_{dk}}\sum_{L=1}^{n} \sum_{j=1}^{4}\mathbf{Q}_{kj}^{L}p_{j}^{L,t}-\Delta t^{2}\frac{1}{M_{dk}}\sum_{L=1 }^{n}\sum_{j=1}^{4}\mathbf{K}_{kj}^{L}\mathbf{u}_{j}^{L,t} \tag{11}\]  
\[p_{k}^{t+1}=p_{k}^{t}+\Delta t\frac{1}{S_{dk}}\sum_{L=1}^{n}f_{k,q}^{L,t}-\frac {1}{S_{dk}}\sum_{L=1}^{n}\sum_{j=1}^{4}\left(\mathbf{Q}_{kj}^{L}\right)^{T}\left( \mathbf{u}_{j}^{t}-\mathbf{u}_{j}^{t-1}\right)-\Delta t\frac{1}{S_{dk}}\sum_{L=1}^{n} \sum_{j=1}^{4}H_{kj}^{L}p_{j}^{L,t} \tag{12}\]  
where the lumped mass matrix \(\mathbf{M}_{dk}^{e}\) and the lumped fluid compressibility matrix \(\mathbf{S}_{dk}^{e}\) of element \(e\) are processed by the same diagonalization method [40], as shown in Equations (13) and (14).  
\[\left(\mathbf{M}_{dk}^{e}\right)_{ij}=\left\{\begin{array}{cc}a\left(\mathbf{M}_{k} ^{e}\right)_{ii}=a\int\limits_{\forall e}\rho^{e}\mathbf{N}_{i}^{T}\mathbf{N}_{i}dV^{e }&(j=i)\\ 0&(j\
eq i)\end{array}\right.,\qquad\sum_{i=1}^{n_{e}}\left(\mathbf{M}_{dk}^{e} \right)_{ii}=a\sum_{i=1}^{n_{e}}\left(\mathbf{M}_{k}^{e}\right)_{ii}=W^{e}\mathbf{I}_ {d}=\rho^{e}A^{e}\mathbf{I}_{d} \tag{13}\]  
\[\left(\mathbf{S}_{dk}^{e}\right)_{ij}=\left\{\begin{array}{cc}b\left(\mathbf{S}_{k} ^{e}\right)_{ii}=b\int\limits_{\forall e}\frac{1}{\mathbf{Q}_{k}^{e}}\mathbf{N}_{i}^{ T}\mathbf{N}_{i}dV^{e}&(j=i)\\ 0&(j\
eq i)\end{array}\right.,\qquad\sum_{i=1}^{n_{e}}\left(\mathbf{S}_{dk}^{e} \right)_{ii}=b\sum_{i=1}^{n_{e}}\left(\mathbf{S}_{k}^{e}\right)_{ii}=V^{e}\mathbf{I}_ {d}=\frac{1}{Q_{b}}A^{e}\mathbf{I}_{d} \tag{14}\]  
where \(\mathbf{M}_{k}^{e}\) and \(\mathbf{S}_{k}^{e}\) are the consistent mass matrix and the consistent fluid compressibility matrix of element \(e\), \(a\) and \(b\) are corresponding scaling coefficients, \(W^{e}\) and \(V^{e}\) are the mass and compressive deformation of element \(e\), respectively, \(A^{e}\) is the area of element \(e\), and \(I_{d}\) is unit matrix.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Finance
#### 6.2.2 Example 2: Enhancing Stock Trading Strategies  
XAI's influence extends into the realm of trading and investment. [PERSON] et al. [76] present a compelling perspective by proposing an explainable reinforcement learning approach designed for financial stock trading. This approach integrates SHAP, offering transparency and interpretability to decision-making processes. It unveils the intricate factors underpinning trading choices and portfolio management strategies. By doing so, it equips traders with a deeper understanding of their strategies, which can lead to more effective investment decisions.
## 2 Materials and Methods
### Experimental Setup
#### 2.1.2 Beam Oscillation Due to Highly Dynamic Scanner  
A highly dynamic scanner was integrated into the collimated laser beam path. The used scanner achieved oscillation frequencies up to 4 kHz. The maximum obtainable oscillation amplitude depended on the frequency and varied between 0 \(\upmu\)m (static beam shaping) and 600 \(\upmu\)m.  
Longitudinal, linear beam oscillation was used due to the significant process improvements achieved in previous studies [24]. Figure 2 compares the movement of the laser beam for static beam shaping and longitudinal, linear beam oscillation. For static beam shaping as seen in Figure 2a, the relative motion between the laser beam and material is usually generated by moving the cutting head. In case of longitudinal, linear beam oscillation, the feed was superimposed by a periodical forward and backward movement along the cutting direction presented in Figure 2b. In the following, it will only be referred to as beam oscillation.  
In the preliminary stages of the current investigation, extensive parameter studies were carried out in order to optimize the process results. Static beam shaping and three different sets of the beam oscillation according to Table 1 were chosen for in-depth analysis. The starting point for the beam oscillation is dataset #2 with 300 \(\upmu\)m amplitude and 500 Hz frequency, which obtained the highest improvements in the current investigation. Based on this, on the one hand, the frequency increased to 2000 Hz, and on the other, hand the amplitude increased to 550 \(\upmu\)m to represent general tendencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Set \#1** & **Set \#2** & **Set \# 3** & **Set \#4** \\ \hline beam shaping & static & & longitudinal, linear & \\ amplitude [\(\upmu\)m] & 0 & 300 & 550 & 300 \\ frequency [Hz] & 0 & 500 & 500 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the analyzed sets of static beam shaping and longitudinal, linear beam oscillation.  
Figure 1: Schematic design of the experimental setup consisting of a standard cutting head with an integrated highly dynamic scanner to initiate beam oscillation and a camera unit for the high spatial and high frequency temporal resolution temperature measurement.  
Figure 2: Schematic beam movement of (**a**) Static beam shaping; (**b**) Longitudinal, linear beam oscillation.
## 5 Discussion and Future Opportunities
### The Construction of Evaluation Index Systems  
The construction of evaluation index systems for landslide susceptibility assessment is directly related to the accurate evaluation and effective management of landslide risk. The evaluation index system mainly includes the selection of mapping units, the selection of evaluation factors, and the selection of positive and negative samples. Regarding the selection of mapping units, raster units and slope units are currently widely studied, each with its own advantages and disadvantages [131]. The choice of suitable mapping units depends on the specific requirements and practical circumstances of the research. Raster units are suitable for situations requiring high spatial resolution and the use of GIS data for model establishment, while slope units are more suitable for capturing terrain continuity features and terrain interpretation. Landslide susceptibility is influenced by various factors, including terrain, geology, climate, and land use. Selecting key factors from numerous factors and making reasonable trade-offs is a challenge. The future selection of evaluation factors needs to overcome the complexity, data availability, dynamics, and uncertainty of multiple factors [14]. The selection of positive and negative samples is the foundation of model training and validation. Positive samples come from historical landslide locations, but there is currently a lack of standards for selecting negative samples. In Section 4.1 of the preceding text, we discussed using the frequency ratio method and information gain method as prior models for landslide susceptibility assessment in the study area. Subsequently, we selected non-landslide points from low-susceptibility areas at random to serve as negative samples for the machine learning models. This approach significantly improved the model's evaluation accuracy. Future research needs to establish a set of standards for selecting negative samples to adapt to different geographical environments. Additionally, the spatial distribution of positive and negative samples may be uneven, exhibiting clustering phenomena. How to select samples reasonably to cover different regions and terrain features is a challenge.
## 5 Results and Discussion
### \(\mathcal{F}_{intra}\) Intra-Lingual Experiments
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Experiment Label**} & \multicolumn{6}{c}{**Average EER per Signature Dataset**} \\ \cline{2-7}  & **CEDAR** & **MCYT** & **GPDS300** & **BANGLA** & **HINDI** \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(A,0,SV)\) & 0.206 & **1.198** & 0.736 & 0.353 & 0.440 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,0,SV)\) & 0.295 & 2.267 & 1.251 & 0.838 & **0.436** \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,0,DS)\) & **0.244** & 2.052 & 0.929 & 1.041 & 0.779 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,100,SV)\) & 2.229 & 2.220 & 4.698 & 0.602 & 1.371 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,100,DS)\) & 1.376 & 2.461 & 2.379 & 0.676 & 1.291 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,0,SV)\) & 0.466 & **1.044** & 1.224 & 0.602 & 0.691 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,0,DS)\) & 0.635 & 2.022 & 1.591 & 0.720 & 1.278 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,100,SV)\) & 0.618 & 1.227 & 1.679 & **0.447** & 0.960 \\ \hline \(\frac{\Psi_{XY}}{LFW2}\mathcal{E}(S,100,DS)\) & 1.393 & 1.988 & 2.458 & 0.642 & 1.837 \\ \hline \(\frac{\Psi_{XY}}{LFW1}\mathcal{E}(S,0,SV)\) & 0.432 & 1.368 & **0.894** & 0.458 & 1.047 \\ \hline \(\frac{\Psi_{XY}}{LFW1}\mathcal{E}(S,0,DS)\) & 0.614 & 1.504 & 1.096 & 0.607 & 1.752 \\ \hline \(\frac{\Psi_{XY}}{LFW1}\mathcal{E}(S,100,SV)\) & 0.551 & 1.368 & 1.196 & 0.379 & 1.127 \\ \hline \(\frac{\Psi_{XY}}{LFW1}\mathcal{E}(S,100,DS)\) & 0.786 & 1.326 & 1.709 & 0.546 & 2.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(\mathcal{F}_{intra}\) and LFW2/TFW2 \(EER_{SF}^{user}\) (%) for the intra-lingual scheme and the [PERSON] measure. Minimum values are displayed in bold. A comprehensive examination of Tables 4-6 reveals that the use of the AIRM measure, in conjunction with a binary SVM classifier, robustly yields low verification error rates when benchmarked against [PERSON], [PERSON]'s measure, and the DSC-BFS classifier.
# Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
## 3 SEDGE Proposed Methodology
### Application Layer
#### 3.6.3 Error Analysis  
In this sub-section, a review of the long short-term memory networks (LSTM) and the autoencoder network are presented. Additionally, the proposed algorithm developed for anomaly detection is illustrated.
## 3 Suppression Methods for Low-Frequency Pressure Fluctuation  
Suppression measures for low-frequency pressure fluctuations can be categorized into passive and active control. Active control methods require changing the flow state by injecting mass or energy according to the specific flow situation, while passive control methods do not require mass or energy injection.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Direction** & **Max. SPL (dB) in** & **Frequency (Hz) at** & **Wind Speed (m/s) at** & \multirow{2}{*}{\(\Delta\)(\(L_{i}\)/\(L_{j}\))1**} \\  & **Plenum Corner** & **max. SPL** & & **max. SPL** \\ \hline Length (X) & 130 & 18 & 44.4 & 5.6\% \\ Width (Y) & 134 & 22 & 55.5 & 1.7\% \\ Height (Z) & 118 & 39 & 77.8 & 14.2\% \\ \hline \hline \end{tabular}  
* \({}^{1}\Delta\)(\(L_{i}\)/\(L_{j}\)) represents the discrepancy between the actual values of \(L_{i}\)/\(L_{j}\) and their predicted equivalents calculated by using Equation (5) (\(n=2\)). Here, \(L_{i}\) denotes the values of \(L_{x}\), \(L_{y}\), and \(L_{z}\) in the X, Y, and \(Z\) directions, respectively. According to the literature [19], the jet length \(L_{j}=2.585\) m, plenum dimensions \(L_{x}=8.75\) m, \(L_{y}=6.85\) m, \(L_{z}=4.37\) m, and speed of sound \(a=340\) m/s.  
\end{table}
Table 2: The conditions of standing waves resonating with edgetone feedback in IVK-MWT.  
Figure 5: Variation of the edgetone frequency with wind speed in DNW-LLF open test section.  
Commonly used passive control measures include (i) the incorporation of a vortex generator at the jet nozzle [2; 3; 4; 8; 9; 16; 23; 51; 52; 53; 54; 55], (ii) optimization of the configuration of the collector, which includes shape optimization [4; 6; 17; 21; 23; 31; 49; 56; 57] and the addition of a breathing gap [4; 6; 7; 14; 15; 58; 59], (iii) the introduction of resonant chambers (Helmholtz resonators) connected to the plenum or the wind tunnel circuits [31; 55; 60], and (iv) modifying the wind tunnel circuits [9; 11; 19].  
The active control techniques commonly employed include the utilization of loudspeakers and nozzle-activated flaps [19; 26; 27; 32; 50].  
The commonly used low-frequency pressure suppression methods described in the literature are summarized in Table 3. Each method is assessed based on its effectiveness, side effects, cost, and application range. Their performance is rated with \(-\), 0, \(+\), and \(++\) to indicate poor, fair, good, and better results, respectively.  
It can be observed that both the collector breathing gap and the nozzle vortex generator exhibit favorable characteristics across multiple aspects and are widely employed as control strategies in practical wind tunnel applications (refer to Table 1).
## 1 Introduction
Through fundamental research, we verified that the scattering phenomenon in the audible frequency can be used to detect an object [27] or a person hiding behind an obstacle (the related results are described in Appendix B). We expect that the combination of ultrasound with its straightness and audible sound with strong scattering can detect a person better. Thus, to create a function as a sensor using a loudspeaker and a microphone array, we studied which room conditions result in the reflection generated by an intruder being considered as a new sound source. We introduce two main topics to implement the proposed idea. The first aspect is signal processing to estimate the position using reflection, and the second involves the simulation and analysis method of the sound field to estimate the position through the reflected sound in the reverberation space. Thus, analysis equations using acoustic parameters are proposed. When estimating the position of a person using an active acoustic-based method, the analysis of the sound field to determine the position of the intruder has the following implications. In a reverberant environment, the proposed method is not aimed at estimating the position by increasing the number of microphones. In other words, this does not mean that many microphones are distributed in the control space or that the microphone arrays are arranged at each corner of the control space. By using limited hardware, one loudspeaker and one microphone array, the method of estimating a person's position using the reflected sound is possible through sound field analysis. Therefore, the active localization method proposed in this paper was verified by estimating the position of a polyvinyl chloride (PVC) pipe and a person in a classroom using signal processing and sound field analysis. The remainder of this paper is organized as follows. In Section 2, the signal model for position estimation using the reflection sound is presented; subsequently, the algorithm is proposed. The feasibility results of the proposed method are presented through the testing of an anechoic chamber. In Section 3, the simulation results for a reverberant environment are described, and the operating conditions in the reverberant space are proposed based on acoustic parameters. In Section 4, the examination of the proposed method using a PVC pipe and a person in a classroom is described. Finally, the conclusions are presented in Section 5.