CN106442635A - Method for recognizing structure layer inside tree on basis of radar waves - Google Patents
Method for recognizing structure layer inside tree on basis of radar waves Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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Abstract
The invention relates to a continuous and nondestructive method for recognizing a layer structure in a tree, and can detect and image structures and abnormity in trees. Echo data are obtained at first by using radar waves to detect trees. When the radar waves are transmitted in wood, different dielectric constants in the wood affect radar wave transmitting speed, amplitude intensity, reflection time and the like. Dielectric constants of different dielectric layers (a bark, a splint wood and a heartwood) inside the tree are different, echo signal amplitude and reflection time of the radar waves are different. The dielectric constants of the various layers are estimated by a layer-by-layer inversion method, and electromagnetic wave transmitting speed is determined; and echo time delay of the various layers is obtained by a Hilbert detection method. Thicknesses of the various layers are determined on the basis of the echo time delay and the transmitting speed. According to the characteristics that a reflection wave amplitude value and value of difference between dielectric constants of adjacent interfaces are directly proportional, and reflection wave time delay depends on the property and the position of dielectric, the position and the shape of a rotten part in the tree can be determined by analyzing echo signal time delay and amplitude.
Description
Technical field
The invention belongs to trees torso interior structure Detection and assessment field and in particular to a kind of based in the trees of radar wave
Method for distinguishing is known in portion structure sheaf position
Background technology
Under natural environment, the trees of growth are easily affected Health of Tree, firm, destruction life by environmental degradation and extraneous infringement
State environment, remedies the precautionary measures and easily causes economic loss not in time；Forest zone, the forest internal flaw in forest farm, pest and disease damage impact life
Long quality；Urban tree is fractureed in inclement weather by infringement and topples over, and jeopardizes personnel's property and life security；Ancient and well-known trees
Brought irremediable loss by fungi, infringement of damaging by worms to state utility function legacy.The infringement that trees are subject to is mainly shown as trees
Internal cracking, scab, rotten and hollow.By infringement early stage live standing tree mostly cannot using nondestructive detection method from
Outside is accurately observed to internal physical structure, but the change of trees internal physical institutional framework influences whether the electricity of wooden body
Learn characteristic (dielectric constant, electrical conductivity and magnetic conductivity etc.), physical characteristic (component content, density and moisture content etc.) and mechanical force
Learn the changes in distribution of performance.Change using these performances adopts accurately and effectively Dynamic Non-Destruction Measurement, can be wooden internal
Portion occurs rotten early stage to carry out Identification to the infringement being subject to of trees and trees damage location, thus stoping trees rotten
Propagation and improve stand quality, the health of the structure that protects trees to greatest extent and firm.
Radar wave detection technology fast and efficiently can carry out entire scan (laterally, longitudinal direction and spiral to surface of trees
Mobile), collection points and collecting efficiency are significantly increased, simultaneously easy to operate, it is difficult by external interference, be capable of real non-
Insertion type, lossless, on-line continuous detection trees internal structure.In order to adopt nondestructive detection side to by infringement early stage trees
Method is accurately observed to internal physical structure from outside, it is possible to use radar wave simultaneously gathers the echo of the internal different structure of trees
Response, thus realize the lossless detection to trees internal structure and exception；It is tested right to be determined by the cross-sectional image detecting
Location and shape size as internal abnormality.
Content of the invention
Method for distinguishing is known, according to radar reflection wave amplitude and adjacent two in a kind of trees internal structure layer position based on radar wave
Dielectric constant difference between interface is directly proportional, the time delay of radar return depends on the electrical property of medium and the feature of position,
Determine the location and shape of internal abnormality by the time delay and amplitude analyzing echo-signal.
A kind of trees internal structure layer position based on radar wave is known method for distinguishing and is included：Pre- place including radar wave data
Reason, layer analysis method, the contrast with different tree species database.
In order to realize above-mentioned technical purpose, the technical scheme is that：
Step 1：The radar wave echo data of described acquisition is initialized and is pre-processed；
Step 2：The radar wave signal of preprocessed mistake, is obtained using Dividing Characteristics identification location technology Hilbert integration method
Take the Delay of wooden body internal abnormality, and then determine the time that radar wave is propagated in dielectric layer；
Step 3：Estimate the dielectric constant of each layer of position using the method for Layer by layer inversion, and then obtain the thunder in current media
Reach ripple transmission velocity of wave, the echo time delay obtaining according to step 2 and propagation time, determine the thickness of layer position；
The information of calculated wooden internal abnormal layer position thickness is compared by step 4 from the database of different trees
Right, improve accuracy of detection；
In step 1 the radar wave echo data receiving is initialized and pre-processed, including herein below：In radar
During ripple detection, the echo-signal receiving includes direct wave between dual-mode antenna, target back wave, external disturbance ripple etc., thunder
Reach data and between low frequency wonder, horizontal road interference, noise signal interference occur, need to filter or suppress interference signal to have extracted
With signal, radar scanning data is removed with direct wave, background removal, moving average filter etc. to remove noise wave removing and noise,
Improve signal to noise ratio, to improve the precision of follow-up internal abnormality analysis.
Further, radar wave receiving terminal adopts linear gain processing data, the loss to echo-signal and the benefit of decay
Repay.Need to use gain during radar wave receiving terminal data processing, the loss for echo-signal and the compensation of decay, carry out signal
In gain process requirement environment, noise is less or first carries out filtering process, and otherwise carrying out may be by these when signal gain is processed
Noise amplifies.
The radar wave signal of preprocessed mistake in step 2, amasss inspection using Dividing Characteristics identification location technology Hilbert
Survey method obtains wooden body internal abnormality time delay, comprises the following steps：
(1) echo-signal is done with EMD decompose, obtain N number of IMF component c accordingly1(t)～cN(t)；
(2) calculate the dot product of all IMF component absolute values, be designated as P (t)：
(3) because echo-signal comprises posivtive spike and negative peak so that receipt signal is made up of multimodal pulse, easily cause false inspection
Survey it is therefore desirable to signal is carried out with the smoothing processing of window function.With P (t) and window function WtCarry out convolution to complete smoothing processing：
T (t)=W (t) * P (t) (2)
(4) T (t) and threshold value V are judgedtSize：If being more than threshold value in T (t) signal, it is judged to reflecting layer is detected,
And calculate corresponding time delay；Otherwise then it is not detected by reflecting layer.
Method for distinguishing is known in a kind of trees internal structure layer position based on radar wave, according to radar wave trees different medium with
And the amplitude of abnormal interface back wave and time delay, it is possible to achieve the Hierarchical Location of trees internal structure and its exception, Jin Erzhan
The visual image of existing trees internal flaw.
Brief description
Fig. 1 is flow chart of the present invention.
Fig. 2 amasss detection method generation layer position time delay figure for Hilbert.
Fig. 3 is specifically layered signal Fig. 1 for the present invention.
Fig. 4 is specifically layered signal Fig. 2 for the present invention.
Description of reference numerals：1. trees A split surface bark 2. trees A wood split surface sapwood 3. trees A split surface heartwood
4. trees A bark 5. trees A sapwood 6. trees A heartwood 7. trees B split surface bark 8. trees B split surface sapwood 9. tree
Abnormal 11. trees B-tree skin, 12. trees B sapwood 13. trees B heartwood 14. in wooden B split surface heartwood 10. trees B split surface
Abnormal area in trees B
Specific embodiment
With reference to concrete drawings and embodiments example, the invention will be further described.
The present invention is that method for distinguishing is known in a kind of trees internal structure layer position based on radar wave.According to flow process Fig. 1, the present invention
Step is as follows：
Step 1:With radar wave, wooden body inside is detected, and the radar wave echo data of described acquisition is carried out just
Beginningization and pretreatment；
Step 2：The radar wave signal of preprocessed mistake, is obtained using Dividing Characteristics identification location technology Hilbert integration method
Take the Delay of wooden body internal abnormality, and then determine the time that radar wave is propagated in dielectric layer；Hilbert area method is led to
Cross empirical mode decomposition EMD and signal adaptive is resolved into limited multiple inherence mold component IMF and a sign signal trend change
The residue signal changed, and with Hilbert transform, time frequency analysis are carried out to each IMF obtaining.
Hilbert area method is the detection algorithm using IMF component construction, for the analysis ratio of non-stationary, nonlinear properties
Relatively directly perceived, and self adaptation is strong.Fig. 2 is the temporal information of each layer of position being analyzed using Xi Er baud detection method, Jin Erji
Calculate Delay.
Hilbert area method comprises the following specific steps that：
(1) echo-signal is done with EMD decompose, obtain N number of IMF component c accordingly1(t)～cN(t)；
(2) calculate the dot product of all IMF component absolute values, be designated as P (t)：
(3) because echo-signal comprises posivtive spike and negative peak so that receipt signal is made up of multimodal pulse, easily cause false inspection
Survey it is therefore desirable to signal is carried out with the smoothing processing of window function.With P (t) and window function WtCarry out convolution to complete smoothing processing：
T (t)=W (t) * P (t) (2)
(4) T (t) and threshold value V are judgedtSize：If being more than threshold value in T (t) signal, it is judged to reflecting layer is detected,
And calculate corresponding time delay；Otherwise then it is not detected by reflecting layer.
Wherein, δ is noise bias, PfFor false alarm rate.The single track ripple that the method for the invention uses is 512 sampled points.
(5) envelope peak is compared with threshold point：If peak value is more than threshold point, it is judged to reflecting layer is detected, and counts
Corresponding time delay；Otherwise then it is not detected by reflecting layer.
Step 3:Estimate the dielectric constant of each layer of position using the method for Layer by layer inversion, and then obtain the thunder in current media
Reach ripple transmission velocity of wave. for impulse electromagnetic wave, it is each bed boundary back wave that the echo signal model receiving can be approximately considered
Superposition：
Wherein, x (t) be incoming electromagnetic wave impulse, N be bed boundary number, AiIt is that reflex amplitude, the n (t) of every bed boundary is
Added white Gaussian noise, τiIt is i-th layer of round trip echo time.
Reflex amplitude AiThe method that amplitude ratio can be used solves dielectric constant；Estimate medium interlayer dielectric constant, the
The estimation formulas of one layer of dielectric permittivity are：
The dielectric constant estimation formulas of the second layer are：
Wherein, A1For the reflection wave amplitude between ground floor and the second bed boundary, A2For anti-between the second layer and third layer interface
Ejected wave amplitude, AmEcho amplitude for the total reflection to metallic plate for the radar wave.
Step 4：Echo time delay can be used for determining the time that radar wave is propagated in dielectric layer；And then can be according to these ginsengs
Number, estimates every layer of thickness.
Electromagnetic wave spread speed V in media as wellp：
Depth Z residing for detected target：
Wherein, ε ' is relative dielectric constant, and t is the time of electromagnetic wave round trip.
Step 5：The radar wave signal of preprocessed mistake, is obtained using Dividing Characteristics identification location technology Hilbert integration method
Take the Delay of wooden body internal abnormality, and then determine the time that radar wave is propagated in dielectric layer.According to radar return
Dielectric constant difference between amplitude and adjacent two interfaces is directly proportional, the time delay of radar return depend on medium electrical property and
The feature of position, by analyzing the time delay of echo-signal and the dielectric constant of layer medium, is calculated a layer dielectric thickness.To count
The information of the wooden internal abnormal layer position thickness obtaining is compared from the database of different trees, improves accuracy of detection；
As Fig. 3 (b) show the hierarchy schematic diagram of trees, Fig. 3 (a) show the layer position that trees split surface shows
Figure.
As Fig. 4 (b) show the internal trees hierarchy schematic diagram containing exception, Fig. 4 (a) show trees split surface
The layer bitmap of display.
Above example is only the illustration to the technology of the present invention thought, does not constitute to protection scope of the present invention
Restriction, every on the premise of the design without departing from the present invention and principle, same or analogous design belongs to the present invention
Within protection scope of the present invention.
Claims (4)
1. a kind of layer analysis identification trees internalist methodology based on radar wave is it is characterised in that include：Including radar wave data
Pretreatment, layer analysis method, the contrast with different tree species database；
Step 1：The radar wave echo data of described acquisition is initialized and is pre-processed；
Step 2：The radar wave signal of preprocessed mistake, obtains wood using Dividing Characteristics identification location technology Hilbert integration method
The Delay of plastid internal abnormality, and then determine the time that radar wave is propagated in dielectric layer；
Step 3：Estimate the dielectric constant of each layer of position using the method for Layer by layer inversion, and then obtain the radar wave in current media
Transmission velocity of wave, the echo time delay obtaining according to step 2 and propagation time, determine the thickness of layer position；
The information of calculated wooden internal abnormal layer position thickness is compared by step 4 from the database of different trees, carries
High measurement accuracy.
2. method according to claim 1 is it is characterised in that carry out to the radar wave echo data receiving in step 1 just
Beginningization and pretreatment, including herein below：During radar wave detection, the echo-signal receiving is included between dual-mode antenna directly
Reach ripple, target back wave, external disturbance ripple etc., radar data occurs that interference between low frequency wonder, horizontal road, noise signal are done
Disturb, need to filter or suppress interference signal to extract useful signal, direct wave is removed to radar scanning data, background is gone
Remove, moving average filter etc. to remove noise wave removing and noise, improve signal to noise ratio, to improve the precision of follow-up internal abnormality analysis；
Further, radar wave echo-signal adopts linear gain processing data, the loss to echo-signal and the compensation of decay；
Need to use gain during radar wave receiving terminal data processing, the loss for echo-signal and the compensation of decay, carry out signal increasing
In beneficial processing requirement environment, noise is less or first carries out filtering process, otherwise carries out these may being made an uproar when signal gain is processed
Sound amplifies.
3. method according to claim 1, it is characterised in that the radar wave signal of preprocessed mistake in step 2, adopts
Dividing Characteristics identification location technology Hilbert amasss detection method and obtains wooden body internal abnormality time delay, comprises the following steps：
（1）Echo-signal is done with EMD decompose, obtain N number of IMF component accordinglyc 1 (t)~c N (t)；
（2）Calculate the dot product of all IMF component absolute values, be designated asP(t)：
（1）
（3）Because echo-signal comprises posivtive spike and negative peak so that receipt signal is made up of multimodal pulse, easily cause false detection,
It is thus desirable to signal is carried out with the smoothing processing of window function；WithP(t)And window functionWtCarry out convolution to complete smoothing processing：
T(t)=W(t)*P(t) （2）
（4）JudgeT(t)With threshold valueVtSize：IfT(t)Be more than in signal threshold value be then judged to reflecting layer is detected, and count
Corresponding time delay；Otherwise then it is not detected by reflecting layer.
4. method according to claim 1 it is characterised in that in described step 4 will be calculated wooden different in vivo
Often the information of layer position thickness is compared from the database of different trees, improves accuracy of detection.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110133643A (en) * | 2019-05-22 | 2019-08-16 | 北京林业大学 | Root system of plant detection method and device |
| CN110595341A (en) * | 2019-09-16 | 2019-12-20 | 浙江水利水电学院 | Resistivity method-based method for obtaining width of sapwood |
| CN112285790A (en) * | 2020-03-30 | 2021-01-29 | 中国科学院地质与地球物理研究所 | Method and device for determining attenuation coefficient of electromagnetic wave field and medium |
| WO2021072801A1 (en) * | 2019-10-16 | 2021-04-22 | 中国科学院遥感与数字地球研究所 | Method and apparatus for measuring wood density of live timber |
| CN113419227A (en) * | 2021-05-07 | 2021-09-21 | 北京林业大学 | Dielectric characteristic analysis system and method for radial layered structure of tree branches |
| CN116125380A (en) * | 2023-04-19 | 2023-05-16 | 齐鲁工业大学(山东省科学院) | Mobile scene super-resolution positioning method based on Kalman filter |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103808624A (en) * | 2014-02-21 | 2014-05-21 | 北京林业大学 | Wood moisture content detection method based on radar waves |
| CN103913733A (en) * | 2014-04-14 | 2014-07-09 | 中国科学院电子学研究所 | Detection method for thickness of polar glacier |
| CN107121705A (en) * | 2017-04-28 | 2017-09-01 | 中南大学 | A kind of ground penetrating radar echo signals Denoising Algorithm compared based on automatic anti-phase correction and kurtosis value |
-
2016
- 2016-09-22 CN CN201610839001.4A patent/CN106442635A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103808624A (en) * | 2014-02-21 | 2014-05-21 | 北京林业大学 | Wood moisture content detection method based on radar waves |
| CN103913733A (en) * | 2014-04-14 | 2014-07-09 | 中国科学院电子学研究所 | Detection method for thickness of polar glacier |
| CN103913733B (en) * | 2014-04-14 | 2016-06-15 | 中国科学院电子学研究所 | Glacier, polar region detecting thickness method |
| CN107121705A (en) * | 2017-04-28 | 2017-09-01 | 中南大学 | A kind of ground penetrating radar echo signals Denoising Algorithm compared based on automatic anti-phase correction and kurtosis value |
Non-Patent Citations (3)
| Title |
|---|
| 吕静霞: "基于雷达波的木材内部缺陷检测方法研究", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
| 张翔，等: "基于测井资料的经验模态分解法的沉积旋回界面划分", 《石油天然气学报（江汉石油学院学报）》 * |
| 张蓓: "路面结构层材料介电特性及其厚度反演分析的系统识别方法-路面雷达关键技术研究", 《万方数据》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110133643A (en) * | 2019-05-22 | 2019-08-16 | 北京林业大学 | Root system of plant detection method and device |
| CN110133643B (en) * | 2019-05-22 | 2021-08-20 | 北京林业大学 | Plant root system detection method and device |
| CN110595341A (en) * | 2019-09-16 | 2019-12-20 | 浙江水利水电学院 | Resistivity method-based method for obtaining width of sapwood |
| WO2021072801A1 (en) * | 2019-10-16 | 2021-04-22 | 中国科学院遥感与数字地球研究所 | Method and apparatus for measuring wood density of live timber |
| CN112285790A (en) * | 2020-03-30 | 2021-01-29 | 中国科学院地质与地球物理研究所 | Method and device for determining attenuation coefficient of electromagnetic wave field and medium |
| CN113419227A (en) * | 2021-05-07 | 2021-09-21 | 北京林业大学 | Dielectric characteristic analysis system and method for radial layered structure of tree branches |
| CN116125380A (en) * | 2023-04-19 | 2023-05-16 | 齐鲁工业大学(山东省科学院) | Mobile scene super-resolution positioning method based on Kalman filter |
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