Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

Jia, Yong; Guo, Yong; Yan, Chao; Sheng, Haoxuan; Cui, Guolong; Zhong, Xiaoling

doi:10.3390/rs11121428

Cited by 22 publications

(10 citation statements)

References 24 publications

(37 reference statements)

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“…For the analysis, the theory of covariance functions was used [16][17][18][19][20][21][22]. Using this theory, it is possible to determine the strength of the dependence between all the environmental pollution and O 2 parameters considered.…”

Section: Methods: a Covariance Model Of The Intensity Of Environmental Pollution Parameter Vectorsmentioning

confidence: 99%

“…Estimates of the covariance function of two numerical parametric vectors of the environmental pollution parameters or the auto-covariance function of a single parametric vector are calculated by spreading digital data vectors in the form of random functions. Discrete transformation is used to process digital signals [20,23].…”

Section: Methods: a Covariance Model Of The Intensity Of Environmental Pollution Parameter Vectorsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Air Pollution Parameters Using Covariance Function Theory

Daugėla

Visockienė

Skeivalas

2020

Ecological Chemistry and Engineering S

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The paper analyses the intensity changes of three pollution parameter vectors in space and time. The RGB raster pollution data of the Lithuanian territory used for the research were prepared according to the digital images of the Sentinel-2 Earth satellites. The numerical vectors of environmental pollution parameters CH4 (methane), NO2 (nitrogen dioxide) and for direct comparison O2 (oxygen gas) were used for the calculations. The covariance function theory was used to perform the analysis of intensity changes in digital vectors. Estimates of the covariance functions of the numerical vectors of pollution parameters and O2 or the auto-covariance functions of single vectors are calculated from random functions consisting of arrays of measurement parameters of all parameters vectors. Correlation between parameters vectors depends on the density of parameters and their structure. Estimates of covariance functions were calculated by changing the quantization interval on a time scale and using a compiled computer program using the Matlab procedure package. The probability dependence between the environmental pollution parameter vectors and trace gas of the territory in Lithuania and their change in time scale was determined.

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Section: Methods: a Covariance Model Of The Intensity Of Environmental Pollution Parameter Vectorsmentioning

confidence: 99%

Section: Methods: a Covariance Model Of The Intensity Of Environmental Pollution Parameter Vectorsmentioning

confidence: 99%

Analysis of Air Pollution Parameters Using Covariance Function Theory

Daugėla

Visockienė

Skeivalas

2020

Ecological Chemistry and Engineering S

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“…Table 1 lists the main parameters and characteristics of radars published in commercial and academic papers. The novel [24] UESTC * (China) 5 SFCW * radar, dual-station "RF-Capture" [22] MIT * (USA) 3 FMCW radar, MIMO, 5.46~7.24 GHz Xia [16] IECAS * (China) 12.5 Pseudo-random radar, portable…”

Section: Introductionmentioning

confidence: 99%

Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar

Yan

Fang

2021

Applied Sciences

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In practical situations such as hostage rescue, earthquake and other similar events, the ultra-wideband (UWB) life-detection radar echo response from the respiration motion of the trapped person is always quasi-/non-periodic in respiration frequency or very weak in respiration amplitude, which can be called quasi-static vital sign. Although it is an extremely difficult task, considering the economic cost, the detection ability of the traditional UWB life-detection radars with only a pair of transceiver antennas is desired to be enhanced for locating the quasi-static trapped human being. This article proposes two different detection methods for quasi-static trapped human beings through the single/multiple observation points, which corresponds to the single-/multi-station radar operating mode, respectively. Proof-of-principle experiments were carried out by our designed radar prototypes, validating the effectiveness of the proposed methods.

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“…The technique is an old one, but improvements are still emerging [ 2 ]. In the area of earth science, image correlation can be used to analyse and monitor any Earth’s surface displacements in the geosciences [ 3 , 4 , 5 ]. The method was developed and first applied to imaging processes in the early 1990s, and relies on a discrete Fourier transform [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants

et al. 2021

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This article describes an agricultural application of remote sensing methods. The idea is to aid in eradicating an invasive plant called Sosnowskyi borscht (H. sosnowskyi). These plants contain strong allergens and can induce burning skin pain, and may displace native plant species by overshadowing them, meaning that even solitary individuals must be controlled or destroyed in order to prevent damage to unused rural land and other neighbouring land of various types (mostly violated forest or housing areas). We describe several methods for detecting H. sosnowskyi plants from Sentinel-2A images, and verify our results. The workflow is based on recently improved technologies, which are used to pinpoint exact locations (small areas) of plants, allowing them to be found more efficiently than by visual inspection on foot or by car. The results are in the form of images that can be classified by several methods, and estimates of the cross-covariance or single-vector auto-covariance functions of the contaminant parameters are calculated from random functions composed of plant pixel vector data arrays. The correlation of the pixel vectors for H. sosnowskyi images depends on the density of the chlorophyll content in the plants. Estimates of the covariance functions were computed by varying the quantisation interval on a certain time scale and using a computer programme based on MATLAB. The correlation between the pixels of the H. sosnowskyi plants and other plants was found, possibly because their structures have sufficiently unique spectral signatures (pixel values) in raster images. H. sosnowskyi can be identified and confirmed using a combination of two classification methods (using supervised and unsupervised approaches). The reliability of this combined method was verified by applying the theory of covariance function, and the results showed that H. sosnowskyi plants had a higher correlation coefficient. This can be used to improve the results in order to get rid of plants in particular areas. Further experiments will be carried out to confirm these results based on in situ fieldwork, and to calculate the efficiency of our method.

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Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

Cited by 22 publications

References 24 publications

Analysis of Air Pollution Parameters Using Covariance Function Theory

Analysis of Air Pollution Parameters Using Covariance Function Theory

Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar

Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants

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