Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors

Two sensor arrays, hull-mounted array, and towed array sensors are considered for bearings-only tracking. An algorithm is designed to combine the information obtained as bearing (angle) measurements from both sensor arrays to give a better solution. Using data from two different sensor arrays reduces the problem of observability and the observer need not follow the S-maneuver to attain observability of the process. The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor array are considered. Different filters are used for analyzing both algorithms. Monte Carlo runs need to be done to evaluate the performance of algorithms more accurately. Also, the performance of the fusion algorithm is evaluated in terms of solution convergence time.

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Utilizing Sensor Arrays to Attain High-Operating Bandwidth

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.1767848 ◽

2004 ◽

Vol 126 (2) ◽

pp. 294-302

Author(s):

Sugathevan Suranthiran ◽

Suhada Jayasuriya

Keyword(s):

Sensor Array ◽

Sensor Arrays ◽

Cost Effective ◽

Sensor Data ◽

Pass Band ◽

Multiple Sensors ◽

High Bandwidth ◽

Small Bandwidth ◽

Operating Bandwidth ◽

Single Sensor

Proposed in this paper is a methodology for the design of a sensor array of small bandwidth passband sensors (sensors with small bandwidth but different bandwidths) to attain a high operating bandwidth. In certain control applications, it is necessary that a high bandwidth sensor be used for feedback efficiency. The design of a single sensor with the desired high bandwidth may not be easy and economically feasible. A new approach, which recommends the use of an array of small bandwidth pass-band sensors in place of a single sensor of high bandwidth is proposed. It is shown that the idea of sensor arrays can be utilized to obtain a cost effective and efficient solution to the problem posed. The proposed sensor array that consists of multiple sensors with possible overlapping operating regions as defined by their pass-bands requires that an effective fusion technique be used to unite multi-sensor data. A multi-sensor data fusion scheme using Frequency Response Methods is developed to facilitate the possible implementation of proposed sensor arrays.

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Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

Sensors ◽

10.3390/s21020601 ◽

2021 ◽

Vol 21 (2) ◽

pp. 601 ◽

Cited By ~ 1

Author(s):

Marco Germanotta ◽

Ilaria Mileti ◽

Ilaria Conforti ◽

Zaccaria Del Prete ◽

Irene Aprile ◽

...

Keyword(s):

Inertial Sensors ◽

Center Of Mass ◽

Correlation Coefficients ◽

Biomechanical Model ◽

Lower Limbs ◽

Accuracy Evaluation ◽

Postural Tasks ◽

Single Sensor ◽

Using Data ◽

Displacement Based

The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

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Fundamental Sensor Development in Electrical Resistance Tomography

Jurnal Teknologi ◽

10.11113/jt.v73.4431 ◽

2015 ◽

Vol 73 (6) ◽

Cited By ~ 1

Author(s):

Ling En Hong ◽

Ruzairi Hj. Abdul Rahim ◽

Anita Ahmad ◽

Mohd Amri Md. Yunus ◽

Khairul Hamimah Aba ◽

...

Keyword(s):

Image Reconstruction ◽

Data Acquisition ◽

Electrical Resistance ◽

Sensor Array ◽

Sensor Arrays ◽

Region Of Interest ◽

Display System ◽

Electrical Resistance Tomography ◽

Fundamental Understanding ◽

Collection Strategies

This paper will provide a fundamental understanding of one of the most commonly used tomography, Electrical Resistance Tomography (ERT). Unlike the other tomography systems, ERT displayed conductivity distribution in the Region of Interest (ROI) and commonly associated to Sensitivity Theorem in their image reconstruction. The fundamental construction of ERT includes a sensor array spaced equally around the imaged object periphery, a Data Acquisition (DAQ), image reconstruction and display system. Four ERT data collection strategies that will be discussed are Adjacent Strategy, Opposite Strategy, Diagonal Strategy and Conducting Boundary Strategy. We will also explain briefly on some of the possible Data Acquisition System (DAQ), forward and inverse problems, different arrangements for conducting and non-conducting pipes and factors that influence sensor arrays selections.

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Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i04.8315 ◽

2018 ◽

Vol 14 (04) ◽

pp. 4

Author(s):

Xuemei Yao ◽

Shaobo Li ◽

Yong Yao ◽

Xiaoting Xie

Keyword(s):

Evidence Theory ◽

Sensor Data ◽

Uncertain Information ◽

Fusion Algorithm ◽

Monitoring And Diagnosis ◽

Multi Sensor Data Fusion ◽

Basic Probability ◽

Optimal Fusion ◽

Single Sensor ◽

Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

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Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

10.26434/chemrxiv.8179244.v2 ◽

2019 ◽

Author(s):

Arni Sturluson ◽

Rachel Sousa ◽

Yujing Zhang ◽

Melanie T. Huynh ◽

Caleb Laird ◽

...

Keyword(s):

Sensor Array ◽

Gas Sensing ◽

Metal Organic Frameworks ◽

Sensor Arrays ◽

Gas Composition ◽

Composition Space ◽

The Matrix ◽

Metal Organic ◽

Analyte Detection ◽

Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from gas composition space to sensor array response space defined by the matrix H of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of H is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO2 and SO2 in the gas phase.

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Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Chemistry Proceedings ◽

10.3390/csac2021-10447 ◽

2021 ◽

Vol 5 (1) ◽

pp. 21

Author(s):

Edgar G. Mendez-Lopez ◽

Jersson X. Leon-Medina ◽

Diego A. Tibaduiza

Keyword(s):

Machine Learning ◽

Dimensionality Reduction ◽

Performance Measures ◽

Sensor Array ◽

Three Dimensional ◽

Electronic Tongue ◽

Sensor Arrays ◽

Classification Performance ◽

Supervised Machine Learning ◽

Electrochemical Tests

Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

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Estimation of Arbitrary Resident Locations Using Data Obtained from an Infrared Sensor Array

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc.2018.00638 ◽

2018 ◽

Cited By ~ 1

Author(s):

Qiangfu Zhao ◽

Shoichi Ichimura ◽

Ryo Ota

Keyword(s):

Sensor Array ◽

Infrared Sensor ◽

Using Data

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Sensor arrays: an inspired idea or an objective measurement of environmental odours?

Water Science & Technology ◽

10.2166/wst.2001.0507 ◽

2001 ◽

Vol 44 (9) ◽

pp. 53-58 ◽

Cited By ~ 13

Author(s):

R.M. Stuetz ◽

J. Nicolas

Keyword(s):

Sensor Array ◽

Sensory System ◽

Sensor Arrays ◽

Objective Measurement ◽

Chemical Compositions ◽

Response Patterns ◽

Human Response ◽

Control And Prevention ◽

On Line ◽

The Right

The measure of annoyance odours from sewage tratment, landfill and agricultural practise has become highly significant in the control and prevention of dorous emissions from existing facilities and its crucial for new planning applications. Current methods (such as GC-MS analysis, H2S and NH3 measurements) provide an accurate description of chemical compositions or act as surrogates for odour strength, but tell us very little about the perceived effect, whereas olfactometry gives the right human response but is very subjective and expensive. The use of non-specific sensor arrays may offer an objective and on-line instrument for assessing olfactive annoyance. Results have shown that sensor array systems can discriminate between different odour sources (wastewater, livestock and landfill). The response patterns from these sources can be significantly different and that the intensity of sensor responses is proportional to the concentration of the volatiles. The correlation of the sensors responses against odour strengths have also shown that reasonable fits can be obtained for a range of odour concentrations (100-800,000 ou/m3). However, the influence of environmental fluctuations (humidity and temperature) on sensor baselines still remains an obstacle, as well as the need for periodic calibration of the sensory system and the choice of a suitable gas for different environmental odours.

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Spline approximation-based data compression for sensor arrays in the wireless hydrologic monitoring system

International Journal of Distributed Sensor Networks ◽

10.1177/1550147717692584 ◽

2017 ◽

Vol 13 (2) ◽

pp. 155014771769258

Author(s):

Danyang Li ◽

Wei Huangfu ◽

Keping Long

Keyword(s):

Data Compression ◽

Monitoring System ◽

Sensor Array ◽

Spline Approximation ◽

Sensor Arrays ◽

Huffman Coding ◽

Long Distance ◽

Data Set ◽

Array Data ◽

Hydrologic Monitoring

A sensor array produces lots of bits of data every sample period, which may cause a heavy burden on the long-distance wireless data transmission, especially in the scenarios of wireless sensor networks. A lossy but error-bounded sensor array data compression algorithm is proposed, in which the major part of the sensor array data are approximated by the Catmull-Rom spline curve and the residual errors are quantized and encoded with Huffman coding. The performance of this algorithm has been evaluated with a real data set from the wireless hydrologic monitoring system deployed in Qinhuangdao Port of China. The results show that the algorithm performs well for the hydrologic sensor array data.

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