Multi-Sensor Information Fusion and Application

Gong, Ting; Yan, Hui

doi:10.4028/www.scientific.net/amm.602-605.2623

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…For different types of sensor data, there are also corresponding fusion frameworks (Analog to Digital Converter/AD Converter) that can be used to transform and synthesize digital signals [16]. Decision layer fusion refers to the eventual association of each sensor, where the features extracted from the feature layer are compared and fused by multiple algorithms, which can effectively and quickly integrate non-uniform information [17]. Typical decision layer fusion algorithms include weighted average algorithms, logical inference, D-S fusion algorithms and Bayesian algorithms for different sensor models as well as in real-world scenarios.…”

Section: Process Of Fusion Of Multi-sensor For Locationmentioning

confidence: 99%

Application of Multi-Type Sensor in Target Location: Principle, Method and Application

2024

HSET

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With the continuous development of modern society and the continuous improvement of science and technology, precision science and technology put forward more and more high requirements for sensors, in the field of distance sensors, such as industrial manufacturing and aerospace, improve the accuracy of sensors and sensing efficiency has become the direction of scientific and technological development. The purpose of this paper is to emphasize the importance of distance sensors in social applications, while introducing the basic principles of mainstream sensors such as infrared sensors, laser sensors and ultrasonic sensors. The application of multiple laser distance sensors in real 3D space and the physical models of multi-sensor data fusion, such as triangulated laser method and WSN, are described, and the mathematical fusion models are categorized and synthesized. Finally, the fusion experiment of Kalman mathematical model in real multi-sensor distance measurement is presented to verify the fusion effect of Kalman filter. to achieve a deeper and better understanding of distance sensors and sensing disciplines.

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Section: Process Of Fusion Of Multi-sensor For Locationmentioning

confidence: 99%

Application of Multi-Type Sensor in Target Location: Principle, Method and Application

2024

HSET

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“…On the basis of estimation theory and information fusion theory, parameter fusion estimation studies how to fully mine and utilize the effective information in different data sets. At present, the research and application of parameter fusion estimation are very extensive in the fields of target tracking, situation estimation and risk decision [15,16]. Parameter fusion estimation algorithm is based on fusion structure.…”

Section: Introductionmentioning

confidence: 99%

“…In distributed fusion, each data set is preprocessed by different local fusion centers, and then the feature level data is sent to the top-level nodes for centralized fusion processing. There is no local processor in the centralized fusion structure, only a fusion center for analyzing the original test data [17,18]. There are some problems in the information fusion application progress, such as information conflict management, information weight distribution and so on.…”

Section: Introductionmentioning

confidence: 99%

Radar seeker performance evaluation based on information fusion method

et al. 2020

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Radar seeker is responsible for transmitting the dynamic position information of the target back to the weapon control center in the guidance phase. It is of great engineering significance to evaluate the anti-jamming ability of radar seeker in the anti-jamming environment Aimed at obtaining more reasonable evaluation result, an evaluation method based on information fusion is proposed against the background of angle measurement performance in this paper. Radar seeker performance test have many stages with different characteristics. Information fusion has advantages in radar performance evaluation which takes advantage of measured data obtained in different stages rather than process them respectively. Furthermore, we studied two specific issues in the information-fusion stage. The first problem is about consistency testing between different information using K-L information distance measurement, which is the premise of information fusion. The second problem is about the information weights calculation by PLT criterion, which can take sample size into account. Information fusion can make full use of the multi-source test information to obtain more reliable evaluation conclusion. Experimental results prove the effectiveness of the proposed method.

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“…Multi‐source information fusion is used in the military field of early warning threats, situation assessment, and intelligence synthesis [14, 15]. With the continuous improvement in fusion structure and algorithm, information fusion is widely used in comprehensive processing of multi‐sensor signal, such as target recognition, intelligent robot, fault diagnosis, and image processing [16, 17]. Considering that the performance evaluation of a radar seeker involves many experimental stages, we propose a performance evaluation fusion scheme for test information obtained in multiple test stages, which is similar with classical fusion process of information obtained by different test device.…”

Section: Introductionmentioning

confidence: 99%