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Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 14032

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Special Issue Editor

Prof. Dr. Ryszard Sroka

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Department of Measurement and Electronics, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: measurements of physical quantities; phase angle measurements; WIM systems and measurement of road traffic parameters; modeling and simulations of measurement systems; signal processing and data fusion in measurement systems
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Special Issue Information

Dear Colleagues,

In any technical system, the cooperation of sensors and measuring systems is essential element enabling the collection of information about the current state of the tested object. The credibility and accuracy of these data are crucial for its further processing and use. Increasingly, in the case of important phenomena and objects, we simultaneously use many different sensors and other types of knowledge resources. Therefore, it has become extremely important to use all available data wisely. Since the early 2000s, methods of data fusion at various levels of the data acquisition and processing process have been intensively developed worldwide.

In the case of measurement systems, the basic aim of data fusion is to reduce the uncertainty of the measurement results or to increase the effectiveness of the classification, detection, or location of the object. This idea involved the joint use of signals and measurement data from many sensors and information derived from other sources (e.g., apriori knowledge). The method used to combine this information depends on the specifics of the object being measured and on the used measurement tools. This method can take diverse forms, starting with the simple averaging of results and continuing on to the use of models based on, for e.g., Bayes theory or Kalman’s filtration theory.

In measurement systems, the fusion process can be located at different levels, from the hardware to advanced methods of signal processing. The methods used for the fusion of data can be divided into three groups:

Competitive fusion, where different types of sensors are used to measure the same physical quantity. This may lead to information redundancy.
Complementary fusion, where each sensor is used to measure a different property of the studied object.
Cooperative fusion, where the correct operation of a single sensor is dependent on the results of some other sensor. Without cooperation, the operation of the first sensor would be impossible or undesirable.

Therefore, it is extremely important from the point of view of measurement systems to review possible data fusion methods, hardware solutions enabling the desired fusion, as well as examples of specific applications of such solutions.

This Special Issue invites papers presenting innovative works on all aspects related to fusion in measurement systems, especially in the following areas:

Sensor level fusion
Data fusion methods
Application of fusion in measurement systems
Data fusion in WIM systems
Multi-sensor data fusion
Signal processing in measurement system

Prof. Dr. Ryszard Sroka
Guest Editor

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Keywords

data fusion
information fusion
data fusion in WIM
sensor fusion
signal processing
measurement systems
fusion methods
data fusion applications
multi-sensor fusion
sensor networks
vehicle sensor fusion
robot sensor fusion
sensor fusion algorithms
smart sensors
applications of sensor fusion
multimodal sensor fusion

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Published Papers (10 papers)

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Research

18 pages, 15556 KiB

Open AccessArticle

Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells

by Qing Wang, Guodong Ji, Jianhua Guo, Ke Wu, Chao Mei, Long Zeng and Qilong Xue

Electronics 2024, 13(16), 3231; https://doi.org/10.3390/electronics13163231 - 15 Aug 2024

Viewed by 708

Abstract

Conventional Measurement-While-Drilling (MWD) technology is unable to function statically at the predicted temperatures of deep formations exceeding 200 °C in wells reaching depths of 10,000 m. It is limited to measuring downhole engineering parameters through purely mechanical means, such as inclination. However, the accurate long-distance transmission of drilling fluid pulse signals poses a significant bottleneck, restricting the application of these mechanical measurement methods. To address these issues, this paper develops and designs an algorithm to identify and analyze the amplitude characteristics of deep well mud signals. By employing a signal coding algorithm, a signal processing analysis method, and a signal feature recognition algorithm based on grey correlation degree, we construct a signal recognition method capable of decoding mud amplitude encoded signals. Key techniques such as filtering, smoothing, and feature extraction are utilized in the signal processing, and the proposed method’s effectiveness is verified through the analysis of collected signals. Furthermore, long-distance simulation analysis software is developed to evaluate waveform distortion during extended transmission, confirming the feasibility of the recognition algorithm. Laboratory experiments demonstrate that this algorithm can accurately recognize and demodulate signals generated by mechanical inclinometer structures, providing a novel decoding method for signal transmission in deep and ultra-deep wells. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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16 pages, 6177 KiB

Open AccessArticle

Magnetoresistive Shunt as an Alternative to Wheatstone Bridge Sensors in Electrical Current Sensing

by Diego Ramírez-Muñoz, Rafael García-Gil, Sandra Soriano-Díaz, Susana Cardoso and Paulo P. Freitas

Electronics 2024, 13(15), 2991; https://doi.org/10.3390/electronics13152991 - 29 Jul 2024

Viewed by 917

Abstract

The main objective of the work is to investigate the capacity of a single magnetoresistance (MR) element to measure AC electrical currents. An instrumentation system is presented to characterize individually the four active elements of an MR bridge current sensor preserving their internal connections. The system suggests the possibility to sense electrical currents using only one element of the bridge opening the way to design new MR sensors based on this concept. Sensitivity, offset and non-linearity deviation were obtained using bridges of tunnel (TMR)- and giant (GMR)-based MR technologies. The single element embedded in a Wheatstone bridge configuration is used for practical current measurements in a 50 Hz line. An electronic circuitry is proposed to measure alternating (AC) currents with a single MR element, including a lock-in amplifier and an interface to properly convert the signal to its root mean square (rms) value with a resolution of 250 mA peak in the 125 A range. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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16 pages, 7973 KiB

Open AccessArticle

Kalman Filter with Adaptive Covariance Estimation for Carrier Tracking under Weak Signals and Dynamic Conditions

by Yan Cheng, Shengkang Zhang, Xueyun Wang, Haifeng Wang and Huijun Yang

Electronics 2024, 13(7), 1288; https://doi.org/10.3390/electronics13071288 - 30 Mar 2024

Cited by 1 | Viewed by 1376

Abstract

Kalman filtering (KF)-based tracking has been commonly employed in global navigation satellite system (GNSS) receivers to achieve robust tracking. However, under more serious conditions, such as severe strength attenuation and abrupt dynamic coexisting environments, it is difficult for KF-based tracking to keep tracking well due to the fixed noise statistics. To further enhance the carrier tracking performance, this paper proposes an adaptive KF carrier tracking method for resisting signal strength fading and high dynamic environments. The proposed method introduces the adaptive factor to adjust the process noise covariance to accommodate the noise statistics in actual variable situations. Moreover, we apply the chi-square hypothesis test to detect system stability. The adaptive factor is only applied when the system is not stable, which can enhance computational efficiency. The proposed method is conducted in the GPS L1 software receivers. According to the results, the proposed algorithm can improve the robustness in tracking performance compared with other tracking methods under signal serious fading and high dynamic conditions. Using the proposed method, GNSS receivers’ navigation performance can be improved under complex conditions. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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17 pages, 5814 KiB

Open AccessArticle

Robust State Estimation Using the Maximum Correntropy Cubature Kalman Filter with Adaptive Cauchy-Kernel Size

by Xiangzhou Ye, Siyu Lu, Jian Wang, Dongjie Wu and Yong Zhang

Electronics 2024, 13(1), 114; https://doi.org/10.3390/electronics13010114 - 27 Dec 2023

Viewed by 1074

Abstract

The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters. However, its performance largely depends on the proper setting of the kernel bandwidth, and currently, there is no efficient adaptive kernel adjustment mechanism. To deal with this issue, a new adaptive Cauchy-kernel maximum correntropy cubature Kalman filter (ACKMC-CKF) is proposed. This algorithm constructs adaptive factors for each dimension of the measurement system and establishes an entropy matrix with adaptive kernel sizes, enabling targeted handling of specific anomalies. Through simulation experiments in target tracking, the performance of the proposed algorithm was comprehensively validated. The results show that the ACKMC-CKF, through its flexible kernel adaptive mechanism, can effectively handle various types of anomalies. Not only does the algorithm demonstrate excellent reliability, but it also has low sensitivity to parameter settings, making it more broadly applicable in a variety of practical application scenarios. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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20 pages, 504 KiB

Open AccessArticle

One-Dimensional Quaternion Discrete Fourier Transform and an Approach to Its Fast Computation

by Dorota Majorkowska-Mech and Aleksandr Cariow

Electronics 2023, 12(24), 4974; https://doi.org/10.3390/electronics12244974 - 12 Dec 2023

Cited by 3 | Viewed by 1473

Abstract

This paper proposes a new method for calculating the quaternion discrete Fourier transform for one-dimensional data. Although the computational complexity of the proposed method still belongs to the

O (N {log}_{2} N)

class, it allows us to reduce the total [...] Read more.

This paper proposes a new method for calculating the quaternion discrete Fourier transform for one-dimensional data. Although the computational complexity of the proposed method still belongs to the

O (N {log}_{2} N)

class, it allows us to reduce the total number of arithmetic operations required to perform it compared to other known methods for computing this transform. Moreover, compared to the method using symplectic decomposition, the presented method does not require changing the basis in the subspace of pure quaternions and, consequently, calculating the new basis vectors and change-of-basis matrix. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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31 pages, 34550 KiB

Open AccessArticle

Relative Jitter Measurement Methodology and Comparison of Clocking Resources Jitter in Artix 7 FPGA

by Andrzej A. Wojciechowski, Krzysztof Marcinek and Witold A. Pleskacz

Electronics 2023, 12(20), 4297; https://doi.org/10.3390/electronics12204297 - 17 Oct 2023

Cited by 2 | Viewed by 1430

Abstract

Phase jitter is one of the crucial factors in modern digital electronics, determining the reliability of a design. This paper presents a novel approach to designing a jitter comparison system and methodology for FPGA chips using a Tapped Delay Line (TDL)—commonly used to implement a Time-to-Digital Converter (TDC). The design and its revision utilizing latches replacing some of the flip-flops are presented and discussed, with potential further improvements. A minimal temperature influence is verified and presented. The methodology of automated relative jitter measurements is discussed. Multiple different FPGA clock signal path configurations are measured, and the results are presented. The influence of clock routing is identified as critical when MMCM or PLL modules are omitted. It is demonstrated that with careful resource and routing allocation, the clock signal’s jitter performance does not have to be deteriorated by the absence of jitter filtering blocks. The proposed technique was implemented and verified and relative jitter performance was measured in the AMD/Xilinx Artix 7 35T FPGA platform. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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15 pages, 9028 KiB

Open AccessArticle

Self-Tuning Process Noise in Variational Bayesian Adaptive Kalman Filter for Target Tracking

by Yan Cheng, Shengkang Zhang, Xueyun Wang and Haifeng Wang

Electronics 2023, 12(18), 3887; https://doi.org/10.3390/electronics12183887 - 14 Sep 2023

Cited by 1 | Viewed by 1201

Abstract

Many practical systems, such as target tracking, navigation systems, autonomous vehicles, and other applications, are usually applied in dynamic conditions. Thus, the actual noise statistics characteristics of these systems are generally time varying and unknown, which will deteriorate the state estimation accuracy of the Kalman filter (KF) and even cause filter diverging. To address this issue, this paper proposes an adaptive process noise covariance (Q_k)-based variational Bayesian adaptive Kalman filter (AQ-VBAKF) algorithm. Firstly, the adaptive factor is introduced to self-tune the process noise covariance; the adaptive factor is obtained based on the innovation sequences, which can adapt to the input measurement values. Then, the VB solution is applied to approximate the time variant and unknown measurement noise covariance. Therefore, this proposed algorithm can adjust the process noise covariance and the measurement noise covariance simultaneously based on the variable input signals, which can improve the self-adaptive ability of the state estimation filter in dynamic conditions. According to the dynamic target tracking test results, the proposed AQ-VBAKF outperforms several other existing filtering methods in estimation accuracy, robustness, and computational efficiency. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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19 pages, 4391 KiB

Open AccessArticle

Spatial Modeling of Air Pollution Using Data Fusion

by Adrian Dudek and Jerzy Baranowski

Electronics 2023, 12(15), 3353; https://doi.org/10.3390/electronics12153353 - 5 Aug 2023

Cited by 1 | Viewed by 1696

Abstract

Air pollution is a widespread issue. One approach to predicting air pollution levels in specific locations is through the development of mathematical models. Spatial models are one such category, and they can be optimized using calculation methods like the INLA (integrated nested Laplace approximation) package. It streamlines the complex computational process by combining the Laplace approximation and numerical integration to approximate the model and provides a computationally efficient alternative to traditional MCMC (Markov chain Monte Carlo) methods for Bayesian inference in complex hierarchical models. Another crucial aspect is obtaining data for this type of problem. Relying only on official or professional monitoring stations can pose challenges, so it is advisable to employ data fusion techniques and integrate data from various sensors, including amateur ones. Moreover, when modeling spatial air pollution, careful consideration should be given to factors such as the range of impact and potential obstacles that may affect a pollutant’s dispersion. This study showcases the utilization of INLA spatial modeling and data fusion to address multiple problems, such as pollution in industrial facilities and urban areas. The results show promise for resolving such problems with the proposed algorithms. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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18 pages, 5022 KiB

Open AccessArticle

Wavelet Threshold Ultrasound Echo Signal Denoising Algorithm Based on CEEMDAN

by Zhiwei Li, Huyue Xu, Bibo Jiang and Fangfang Han

Electronics 2023, 12(14), 3026; https://doi.org/10.3390/electronics12143026 - 10 Jul 2023

Cited by 3 | Viewed by 1721

Abstract

In this study, an algorithm for denoising ultrasound echo signals in industrial settings is proposed to address the problem of high noise and low signal-to-noise ratio. The algorithm combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information entropy (MIE), and wavelet threshold denoising to ensure effectiveness given the unique structure of ultrasound echo signals. Initially, CEEMDAN is used to decompose the signal into intrinsic mode function (IMFs) and residual signals. The MIE is then used to determine the correlation of neighboring IMF signals, which are then divided into a noise- and a signal-dominated part. Finally, using wavelet thresholding, noise is suppressed in the signal-dominant part, and the resulting denoised signal is reconstructed using the residual signal. The performance of the algorithm is verified through simulations and physical experiments, and the results show that it is superior to traditional signal denoising methods. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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14 pages, 5008 KiB

Open AccessArticle

Accuracy Maps of Weigh-In-Motion Systems for Direct Enforcement

by Janusz Gajda, Piotr Burnos, Ryszard Sroka and Mateusz Daniol

Electronics 2023, 12(7), 1621; https://doi.org/10.3390/electronics12071621 - 30 Mar 2023

Cited by 3 | Viewed by 1532

Abstract

The need to protect road infrastructure and the environment, as well as to increase the safety of road users and to ensure fair conditions of competition in road transport, requires an increase in the efficiency of the elimination of overloaded vehicles from road traffic. The replacement of “manual” vehicle control (carried out by inspectors of the relevant services) by automatic control can ensure that these are highly effective. Such control can be implemented directly on the basis of weighing results obtained from weigh-in-motion (WIM) systems. The high sensitivity of WIM systems to various interfering factors is an obstacle to the full implementation of this goal. This paper presents a concept for accuracy maps determined for direct enforcement WIM systems. The use of such maps allows for the minimization of the probability of an error consisting in classifying a normative vehicle as an overloaded one. Full article

(This article belongs to the Special Issue Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems)

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