NL2026449A - Abnormal grain condition detection method based on radio tomographic imaging - Google Patents
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Abstract
An abnormal grain condition detection method based on radio tomographic imaging includes the following sequential steps: (1) arranging sensors: arranging a plurality of sensor nodes around a grain depot; (2) establishing a non-abnormal grain condition 5 database: transmitting radio signals, and summarizing radio signals received by the sensors to establish the non-abnormal grain condition database; (3) collecting experimental data of an abnormal grain condition: transmitting radio signals, and summarizing radio signals received by the sensors to collect the experimental data of the abnormal grain condition; (4) preprocessing the collected experimental data; (5) 10 establishing a linear model of communication link attenuation on the pre-processed data; (6) solving a vector matrix; (7) solving the linear model of communication link attenuation to obtain an image attenuation value; and (8) performing image reconstruction to find out a position of the abnormal grain condition. The method of the present invention improves the accuracy of the abnormal grain condition detection; 15 which is simple and easy to implement.
Description
-1-
TECHNICAL FIELD The present invention belongs to the technical field of abnormal grain condition detection, and more particularly, relates to an abnormal grain condition detection method based on radio tomographic imaging.
BACKGROUND Grain security is always a serious problem worldwide. Grain condition detection can find the abnormal grain condition in the stored grain heap, so as to make early warning and policy regulation for grain security ahead of time. At present, the abnormal grain condition detection means mainly adopts manual detection to the grain depot, depot penetrating radar and wired sensor network detection. Manual detection to the grain depot requires lots of manpower, material resources and financial resources, which is low in the speed and accuracy, and it is difficult to find the abnormal grain condition in the grain heap. With respect to wired sensor network detection, various sensors are embedded into and equipped into the grain heap to collect various grain condition monitoring data in the coverage by the sensor nodes in the grain depot in real time.
However, existing wired sensor network detection has problems such as difficult layout, single monitoring parameter, small coverage, difficult upgrading and others. Conventional ground penetrating radar is called as “depot penetrating radar” when used for the abnormal grain condition detection. When detecting the grain depot, the receiving and transmitting antenna is arranged on the grain surface, the abnormal condition is estimated by microwave reflection theory and electromagnetic wave reflection imaging caused by dielectric characteristic change interface of the grain heap. Depot penetrating radar based on reflection theory faces two problems in practical applications. The first problem is the contradiction between the detection depth and the resolution of detection target. Specifically, the imaging resolution of detection target is inversely proportional to the detection frequency, and the low frequency of the radar seriously affects the accuracy of anomaly target recognition. The second problem is the imaging difficulty caused by small difference of relative dielectric constants between the grain and the abnormal grain condition. Since the magnitude of the detected reflection wave of the
-2- anomaly target is mainly determined by difference of relative dielectric constants between the grain and the abnormal grain condition at the interface, when the difference between the dielectric constant of media of the grain and the dielectric constant of media of the abnormal grain condition is not large enough, the reflection wave is weak, while the transmission wave is strong, so that it is difficult to obtain the return wave signal to cause the difficulty of imaging. Therefore, from the aspect of detection resolution, the microwave detection based on transmission theory presents obvious advantages. Recently, a radio tomographic imaging technique based on transmission theory has emerged. The radio tomographic imaging uses distributed wireless sensor nodes to measure power attenuation of the link and obtain the signal attenuation in region by inversion. Radio tomographic imaging has low hardware requirements, and the radio signals have good penetration. Radio tomographic imaging has been widely used in target positioning and tracking, vehicle recognition, through-the-wall detection, medical sanitation and other fields. Compared with conventional ground penetrating radar, the radio tomographic imaging has great advantages. Firstly, radio tomographic imaging can work at a relative high frequency band to obtain a better spatial resolution. Secondly, radio tomographic imaging only requires to measure power of radio signals, and has a low requirement for nodes, which can reduce cost of the system and facilitate miniaturization of the system.
SUMMARY An objective of the present invention is to provide an abnormal grain condition detection method based on radio tomographic imaging with a high detection accuracy.
In order to solve the technical problems mentioned above, the present invention provides the following technical solution. An abnormal grain condition detection method based on radio tomographic imaging includes the following sequential steps: (1) arranging sensors: arranging a plurality of sensor nodes around a grain depot; (2) establishing a non-abnormal grain condition database: transmitting radio signals, and summarizing radio signals received by the sensors to establish the non-abnormal grain condition database;
-3-
(3) collecting experimental data of an abnormal grain condition: transmitting radio signals, and summarizing radio signals received by the sensors to collect the experimental data of the abnormal grain condition;
(4) preprocessing the collected experimental data according to formula (1);
ite NN f(jjx h4 => SA, (1), {0 where, f(i) =exp((i-r) /(2*6°)/N2*S° is a Gaussian mask; Af, An, Ar are signal strength values received by the sensors on an /™ link at (1-0)! moment, 2 moment, (7+ )'® moment, respectively; and @ is a filtering window;
(5) establishing a linear model of communication link attenuation on the pre-
processed data to obtain formula (2);
Ar, = WAx, +n, (2),
where, Ar, represents a measurement matrix consisting of received signals of all communication links at moment; Ax, represents a shadow fading vector of a received signal strength of all grids at f moment; n, represents a noise vector, and W is a vector matrix of a weight of each grid on each link; (6) solving a vector matrix by formula (3); ch í e” d()+d,(2)<d, +4 0 otherwise ’
where, d, is alength of the / link; d, (1) and d,(2) are distances from aj" grid to two sensors, respectively; A is a parameter adjusting an range of an ellipse; and 4 is a distance from a grid inside the elliptic to a current link;
(7) solving the linear model of communication link attenuation by formula (4) to obtain an image attenuation value;
Ak, = (WIW +al' Ty W'Ar, (4),
where, Ax, is a derivative of Ax,, and WTisa transpose of W; and
(8) performing image reconstruction to find out a position of the abnormal grain condition;
(1) comparing each image attenuation value to obtain a grid coordinate Jmax of the maximum image attenuation value;
-4- (11) obtaining the position of the abnormal grain condition by formula (5); P= (5), where, P is the position of the abnormal grain condition. In step (4), the filtering window takes a value of 5. In step (5), a method for establishing the linear model of communication link attenuation on the pre-processed data is as follows: the received signal strength measurement #, of the / link at t moment is expressed as follows: hy = P, 4 Ss, -h, 4%, (6), where, ©, represents a transmitting power of a sensor node, £; represents a path loss of the /? link, S,, represents a shadow fading caused by shielding the link through the abnormal grain condition, /;, represents a multi-path fading noise, and 7%, represents a measured noise of a monitoring area; a received signal strength change caused by the abnormal grain condition is follows: A, =h-h = Si +, +n, (7), the monitoring area of the grain depot is divided evenly into N grids; the shadow fading of the ? link caused by the abnormal grain condition is expressed by a weighted sum of all grid values; and the shadow fading of the link is expressed by using a spatial integral as follows:
N S= D Wij A, (8), j=1 where, Ax ; represents a signal strength change of the monitoring area of the grain depot at the /!! grid, and wy, represents a weight of the / grid on the / link; since a medium of the monitoring area of the grain depot is single, is similar to an outdoor environment and has a small multi-path fading, +, 30, the received signal strength change of the /! link in the monitoring area is as follows:
N Ar, = Dw, Ax a HR, (9), J=1 formula (9) only considers the received signal strength change of the / link, and when all links are considered, formula (2) is obtained as follows:
-5- Ar =WAx +n, (2), where, Ar, represents the measurement matrix consisting of the received signals of all communication links at ‘moment; Ax, represents the shadow fading vector of the received signal strength of all grids at # moment; n, represents the noise vector; and W is the vector matrix of the weight of each grid on each link.
In step (6), a method for obtaining the formula (3) is as follows: modeling an area influenced by the abnormal grain condition as the ellipse, wherein a focus of the ellipse is a receiving and transmitting node; since grids inside the ellipse are considered to cause influence on a result, giving a weight to the grids inside the ellipse; while since grids outside the ellipse are considered to cause no influence on the result, giving a weight of 0 to the grids outside the ellipse.
In step (7), a method for obtaining the formula (4) is as follows: firstly, minimizing an objective function as follows: min Ar, - WA, | + a UAx | (10), 4x, where, T is a Tikhonov matrix and represents a prior information of a model solution; |TAx,| represents a penalty term for Tikhonov regularization; and @ is an adjustable regularization parameter, a value of & determines whether a final solution is biased to measured data or biased to the prior information; then, an approximate estimation of AX, is obtained by taking a derivative of formula (9) to obtain formula (4) as follows: Ak, = (WW +al"T)" WT Ar, (4) By adopting the above technical solution, the present invention has the following advantages. Compared with traditional methods, simulation experiment shows that the method of the present invention has more advantages. In order to solve those difficult problems such as large workload of grain detection, small difference of dielectric constants of abnormal grain condition and low accuracy, a new detection method is proposed by the present invention to provide new technical solution and research direction for the abnormal grain condition detection. This new method can effectively find out the position of the abnormal grain condition, which has a high accuracy and a fast detection speed.
-6-
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart of the present invention; FIG. 2 shows an experimental result when water anomaly exists in the grain condition in case of four sensor nodes; FIG. 3 shows an experimental result when water anomaly exists in the grain condition in case of six sensor nodes; FIG. 4 shows an experimental result when water anomaly exists in the grain condition in case of eight sensor nodes; FIG. 5 is a comparative chart of median errors when air anomaly, metal anomaly and plastic anomaly exist in the grain condition; FIG. 6 shows an experimental result when air anomaly exists in the grain condition; FIG. 7 shows an experimental result when metal anomaly exists in the grain condition; FIG. 8 shows an experimental result when plastic anomaly exists in the grain condition; FIG. 9 is a schematic diagram showing positions of the abnormal grain condition; FIG. 10 shows median errors of different positions; and FIG. 11 is a comparative chart of median errors of the abnormal grain condition with image preprocessing and without image preprocessing.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS As shown in FIG. 1, an abnormal grain condition detection method based on radio tomographic imaging includes the following sequential steps. (1) Sensors are arranged. A plurality of sensor nodes are arranged around a grain depot. In the present embodiment, a resin-based transparent plastic box with a size of
-7- 200 mm * 300 m * 500 mm is adopted to simulate the grain depot. four sensor nodes, six sensor nodes, and eight sensor nodes are successively arranged to perform grouped experiments, and the sensor nodes are required to be located at a same horizontal plane.
(2) A non-abnormal grain condition database is established. Radio signals are transmitted, and radio signals received by the sensors are summarized to establish the non-abnormal grain condition database. The non-abnormal grain is poured into the grain depot. Then, a sensor network transmits and receives the radio signals automatically for ten minutes, summarizes the received signal strength data, extracts the received signal strength feature of each sensor node, and establishes the non-abnormal grain condition database.
(3) Experimental data of an abnormal grain condition are collected. Radio signals are transmitted, and radio signals received by the sensors are summarized to collect the experimental data of the abnormal grain condition. The abnormal grain is poured into the grain depot. Then, the sensor network continuously receives and transmits the radio frequency signals, and extracts the received signal strength of each sensor node periodically as the experimental data of the abnormal grain condition.
(4) Data collected in step (3) are processed. Grain belongs to granular body. When the radio signal is transmitted in the grain depot through transmission, the grain surface easily reflects the radio signal. Since the slight change of the received signals caused by the grain reflection belongs to fast fading, it is usually modeled as the noise. When the system processes the signals in time domain, a low-pass filter is used to reduce the influence of the fast fading. The low-pass filter employs a one-dimensional Gaussian filter. Assuming that the size of the Gaussian filtering window is © and the standard deviation of the Gaussian function is & , the output signal can be expressed as follows: +o ne=2 SOA, to where, f(D) =exp((i-r) /(Q*6°)/ 2s? ‚which is a Gaussian mask; Af, Ar, Ar. are the signal strength values received by the sensors on the /" link at (t-0)% moment, i moment, (7+ )™ moment, respectively; and © is the filtering window. In the present embodiment, © takes a value of 5. Since the excessive large value of the filtering window © increases the time delay and the excessive small value of the filtering window © causes the poor denoising effect, after weighing the time
-8- delay and the validity, the present invention takes a value of 5 as the size of the filtering window.
In this step, the data is pre-processed to improve the accuracy of the detection result.
From the above, grain is granular body.
If the data is not pre-processed, the existing excessive large noise decreases the accuracy greatly, while after the data being pre- processed, a large part of noise is removed, and the accuracy is improved significantly and is far higher than the accuracy obtained in case that the data is not pre-processed.
As shown in FIG. 11, after pre-processing, the simulation experiment is closer to the actual location and has a relatively small median error. (5) A linear model of communication link attenuation is established on the pre- processed data.
When there is no abnormal grain condition in the grain depot, the received signal strength measurement of the /" link is recorded as # . When an abnormal grain condition exists in the grain depot, the abnormal grain condition reflects and shields the radio signals to cause shadow fading, so that the received signal strength measurement of the link changes.
The received signal strength measurement 4%, of the / link at t moment is expressed as follows: he = P -f Ss, Pi, TM, (6), where, P, represents a transmitting power of a sensor node, I} represents a path loss of the MM link, 5;, represents the shadow fading caused by shielding the link through the abnormal grain condition, +, represents the multi-path fading noise, and #;, represents the measured noise of the monitoring area.
The received signal strength change caused by the abnormal grain condition is follows: Ar, hhh; Tr 8 +k, +n, (7). The monitoring area of the grain depot is divided evenly into N grids.
The shadow fading of the /* link caused by the abnormal grain condition can be expressed by a weighted sum of all grid values . The shadow fading of the /M link is expressed by using a spatial integral as follows:
-9-
N Si = 2 wax, (8),
JT where, AX, represents the signal strength change of the monitoring area of the grain depot at aj" grid, and wy, represents a weight of the / grid on the / link. Since the medium of the monitoring area of the grain depot is single, is similar to the outdoor environment and has a small multi-path fading, assuming that £,, 0 the received signal strength change of the / link in the monitoring area is as follows:
N Ar, = 2 yA, +1, (9). I= The above formula (9) only considers the received signal strength (RSS) change of the /* link, and the monitoring area of the grain depot includes L links totally, so that a system of linear equations containing L RSS changes is obtained and expressed in a matrix form as follows: Ar, = WAx, +n, (2), where, Ar, represents a measurement matrix consisting of the received signals of all communication links at # moment; Ax, represents a shadow fading vector of the received signal strength of all grids at f moment; n, represents a noise vector; and W 1s a vector matrix of a weight of each grid on each link.
(6) The vector matrix is solved by formula (3) as follows: Je” d,()+dy(2)<d, +4 hi | 0 otherwise (3), where, d, is a length of the / link; d,(1) and d,(2) are distances from the j* grid to two sensors, respectively; A is a parameter adjusting an range of an ellipse; and / is a distance from a grid inside the elliptic to a current link.
Formula (3) is established depending on the following basis: the area influenced by the abnormal grain condition is modeled as the ellipse whose focus is the receiving and transmitting node. Grids inside the ellipse are considered to cause influence on the result and thus are given a weight, while grids outside the ellipse are considered to cause no influence on the result and thus have a weight of 0.
-10- (7) The linear model of communication link attenuation is solved by formula (4) to obtain an image attenuation value as follows: Ak, = (WIW +a") WTAr, (4), where, AX, is a derivative of Ax,, and WT is a transpose of W.
After the weight matrix W of the ellipse is determined, the formula (2) is solved to obtain Ax, so that image reconstruction is performed to find out the position of the abnormal grain condition.
When solving Ax,, since the number of unknowns in the system of equations is larger than the number of equations, regularization method is required. Tikhonov regularization is an effective means to solve the ill-conditioned equations. Firstly, the objective function is minimized as follows: min Jar, - Wax, [+ ocfrax (10), where, F is a Tikhonov matrix, which represents the prior information of the model solution; [Ax | represents a penalty term for Tikhonov regularization; and « is an adjustable regularization parameter whose value determines whether the final solution is biased to the measured data or biased to the prior information. Then, an approximate estimation of AX, can be obtained by taking a derivative of the minimized objective function as follows: Ak, = (WIW +a Ty WTAr, (4) where, AX, is derivative of Ax;, and WT is the transpose of W. (8) Image reconstruction is performed to find out the position of the abnormal grain condition. (1) Each image attenuation value is compared to obtain the grid coordinate Jmax of the maximum image attenuation value. (11) The position of the abnormal grain condition is obtained by formula (5) as follows: P=Limax (5), where, P is the position of the abnormal grain condition. Experimental analysis:
-11- Simulation analysis is performed on the method of the present invention. Firstly, water anomaly experiments are successively performed on four nodes, six nodes and eight nodes, and the experimental results are shown in FIGS. 2-4. The experimental results show that the experimental median error of four nodes 1s 0.0162m, the experimental median error of six nodes is 0.0135m, and the experimental median error of eight nodes is 0.0128m. Compared with four nodes, the size of imaging area of foreign body of six nodes and eight nodes is more condensed and has a smaller range. Meanwhile, considering the complexity of the equipment, data processing operation and other factors, it is considered that six nodes are suitable for this experiment.
Then, experiments are performed to explore the influence of different grain conditions on experimental results. Experiments are successively performed on void anomaly, metal anomaly and plastic anomaly, and the experimental results are shown in FIGS. 6-8. The experimental results show that when the abnormal grain condition is the metal, in the radio tomographic imaging results, the surface is relatively smooth, and the position of the foreign body even has a basic shape only because the large dielectric constant of the metal absorbs the radio frequency signals more obviously, and the changes in communication link attenuation becomes more obvious.
Finally, experiments are performed to explore the radio tomographic imaging effects of the metal anomaly at different positions in case of six nodes to further research the influence of the position on the imaging results. The positions of the metal during experiment are shown in FIG. 9, and the experimental median errors are shown in FIG.
10. The experimental results show that the median error of the position 3 is minimal while the median error of other positions is larger than that of the position 3 because the position 3 has a larger number of links than other positions and has a more accurate imaging result.
The method of the present invention can detect the abnormal grain condition, which has a high-accuracy detection result and a fast detection speed. Compared with traditional detection methods, the method of the present invention is easier to achieve online measurement, and can fast find the position of the abnormal grain condition without moving grain.
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