Massive MIMO Radar based Burden Surface Imaging: How mm-Wave Integrated Circuits Enable Optimization of Blast Furnace Charging

Schuster, Stefan; Zankl, Dominik; Scheiblhofer, Stefan; Feilmayr, Christoph; Reisinger, Johann; Feger, Reinhard; Stelzer, Andreas; Schmid, Christian M.

doi:10.1109/icmim48759.2020.9299056

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…The red circles and black squares represent transmitter and receiver positioning at each transducer, respectively. Refer to [13], [35], [36] for complete application details. then asymptotically reaches the MAE T of the TLLS method.…”

Section: B Tomographic Reconstruction Of the Temperature Inside A Bla...mentioning

confidence: 99%

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

bhuvana¹,

Och²,

Schuster³

2022

Preprint

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<p>A typical time-of-flight (ToF) tomography method requires accurate information about the system’s calibration parameters in order to reconstruct the features of interest within a region of interest (RoI). This paper proposes a constrained joint calibration and tomography (CJCT) method based on a separable least squares (SLS) approach for ToF tomographic systems. Contrary to the existing offline and online calibration methods, the proposed method utilizes prior information about the calibration parameters in the form of constraints. The imposed constraints allow for an efficient search for the minimum of the inherent SLS cost function by reducing the system and measurement ambiguities. Thus, the proposed CJCT method can find the optimum values for both tomography and calibration parameters more efficiently compared to unconstrained methods. The general applicability of the proposed method is demonstrated by evaluating it on two applications: tomographic reconstruction of the permittivity distribution in a RoI using ToF measurements from a high resolution millimeter-wave radar system, and the temperature distribution inside an industrial blast furnace using measured and simulated acoustic ToF values. The simulation and experimental results show that the proposed method performs significantly better in terms of tomographic reconstruction accuracy compared to unconstrained methods.</p>

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Section: B Tomographic Reconstruction Of the Temperature Inside A Bla...mentioning

confidence: 99%

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

bhuvana¹,

Och²,

Schuster³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…where x i is the average vector of the ith class sample data, the vector The input sample of the algorithm consists of three parts, which are the highdimensional spatial features of each cycle, the periodic features, and the radar detection data processed by sliding window in this cycle, as shown in Equation (10).…”

Section: Eigen Qr Decomposition Direct Clustering Algorithm (Eqrdd)mentioning

confidence: 99%

“…Step 2: When the first sliding window radar data of the first material charging cycle are sampled, the first input sample x 1 (1) is constructed according to Equation (10), a first class is formed, and the number of classes K = 1, the clustering centers C = {c 1 } and c 1 = x 1 (1), and the first hidden node h 1 of the hidden layer of the network are generated;…”

Section: Selection and Comparison Of Maximum Likelihood Radial Basis ...mentioning

confidence: 99%

“…This method is widely used due to its advantages of non-contact, high penetration, and real-time performance [9]. However, because the smelting environment in the blast furnace is extremely harsh [10], the high-speed airflow and high concentration of dust in the furnace often interfere with the radar directional echo signal, which makes the measurement accuracy fluctuate, and have poor stability and low reliability [11]. Therefore, overcoming the defects of the two methods for stockline measurement and using the advantages of the two methods, so as to obtain the stockline detection data continuously and with high precision in real-time, is a scientific problem that requires solving urgently.…”

Section: Introductionmentioning

confidence: 99%

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High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Liu

Chen

Gui

et al. 2022

Sensors

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The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.

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“…Although this research has improved the accuracy of measuring the burden level with a mechanical probe, it still cannot overcome the problem of discontinuity in the measuring process. For the research of measuring burden level with a radar probe, in order to solve the problem of low accuracy and reliability of a radar probe, S. Schuster et al used an integrated millimeter-wave design circuit and advanced signal processing technology to obtain the average burden level information of a blast furnace in real-time [ 17 ]. Dominik Zankl et al proposed a BLASTDAR radar sensing system for real-time radar imaging of blast furnace burden surface [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

Yan-li

Chen

Gui

et al. 2022

Sensors

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Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

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Massive MIMO Radar based Burden Surface Imaging: How mm-Wave Integrated Circuits Enable Optimization of Blast Furnace Charging

Cited by 3 publications

References 11 publications

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

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