CN109255100A - A kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition - Google Patents
A kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition Download PDFInfo
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Abstract
The invention discloses a kind of Urban Rain inversion algorithms based on microwave attenuation characteristic response fingerprint recognition, and separation classification, the foundation of clutter characteristic response fingerprint base, the identification of clutter of acquisition and preliminary treatment, noise signal including microwave signal matches with removal, rainfall inverting, multi-source rainfall data scale, Model Parameter Optimization and inversion result real time correction.The present invention can be realized on the quick judgement and removal that different type clutter bring influences in microwave attenuation signal under urban environment, provide 100 meters -200 meters of spatial resolution, the multiple resolution ratio rainfall big data in -10 minutes 5 minutes cities of temporal resolution.
Description
Technical field
The present invention relates to meteorological factor monitoring technical fields, more particularly to a kind of microwave attenuation characteristic response fingerprint that is based on to know
Other Urban Rain inversion algorithm.
Background technique
The rainfall monitoring data of real-time high-precision is to realize bloods and droughts prevention and treatment, water resources development and utilization and comprehensive treatment
Premise.Real-time rainfall data monitoring mode relatively conventional at present mainly has precipitation station and two kinds of radar: in the ideal situation,
Ground precipitation station can provide the more accurate point rainfall data of 5min or more, but ground precipitation station is faced with construction cost
The outstanding problems such as height, management service are difficult, network density is low, data reliability is poor exist compared with city fine-grained management demand
Very big gap；Radar observation is also a kind of important monitoring means, can provide temporal resolution 5min, spatial resolution
The rainfall distribution of 100m × 100m, but radar has the influence covered vulnerable to high buildings and large mansions stop with foreign matter echo, short distance blind
Area, installation maintenance be at high cost, to far estimating the defects such as estimation precision is low.
Carrying out rainfall real-time monitoring using the decay of Wireless microwave signal of communication is a kind of novel rainfall monitoring technology, wirelessly
Microwave communication is communicated using the electromagnetic wave of wavelength 1mm-1m, and when electromagnetic wave passes through rainfall region, raindrop can produce electric wave
It is raw to absorb and scatter, so cause significantly to decay.International Telecommunication Union ITU is on the basis of summary microwave rain declines characteristic, publication
ITU-R rain declines formula.According to the formula, microwave link receive the pad value of transmitting end signal strength as inverting rainfall according to
According to the rainfall in tens meters of near surface can be obtained with inverting after going noise wave removing and influence of noise.
City is the key area of rainfall monitoring, and due to land surface condition complexity, rainfall special heterogeneity is high, needs high score
The rainfall monitoring technology distinguished.It can be not required to by existing wireless base station communication facility using the monitoring rainfall of Wireless microwave signal of communication
Infrastructure investment and construction are wanted, a large amount of disposable infrastructure investment, a large amount of operation and personnel's maintenance cost are saved,
It is to solve the rainfall of city high-spatial and temporal resolution with the significant advantages such as fast, investment is small, easy to maintain, encryption observation is flexible are built
The completely new approach of acquisition of information problem.
However, urban area environment is complicated, different types of noise and clutter high degree affect microwave signal pair
Therefore how the capturing ability of rainfall quickly identifies and extracts microwave decay to the sensitive big compression ring such as moisture content, atmospheric visibility
The characteristic response of border variable, and then ambient noise and clutter influence are removed, it is that the technology drops in real time applied to city high-resolution
Had in the building of rain data one of the critical issue considered and current research there is also major defect.On the other hand, by
It is influenced in unavoidably will receive the factors such as various clutters, superrefraction, plant growth during microwave signal transmission, error is not
It may completely eliminate.Meanwhile in different regional or different rainfalls, inverse model parameter value there is also bigger difference,
Cause rainfall estimated value and practical rainfall value deviation larger, for the city of limited rainfall earth station, Data fusion technique and school
Normal operation method is the important leverage for improving rainfall observation data precision and stability.
Summary of the invention
The purpose of the present invention is to provide a kind of, and the Urban Rain inverting based on microwave attenuation characteristic response fingerprint recognition is calculated
Method can be realized on the quick judgement and removal that different type clutter bring influences in microwave attenuation signal under urban environment,
100 meters -200 meters of spatial resolution, the multiple resolution ratio rainfall big data in -10 minutes 5 minutes cities of temporal resolution are provided.
In order to achieve the above-mentioned object of the invention, the invention proposes a kind of cities based on microwave attenuation characteristic response fingerprint recognition
City's rainfall inversion algorithm, which is characterized in that comprise the following steps that
The first step obtains the microwave attenuation signal strength data of microwave signal receiving end, and carries out to signal strength data
Preliminary treatment；
Second step carries out separation, identification and the removal of noise signal to the signal strength data after preliminary treatment；
Third step decays response relation according to the microwave space-time under different atmospheric conditions, establishes rain-and decline inverse model, benefit
With treated in second step, microwave signal intensity inverting obtains path rainfall；
4th step, the matching of multi-source rainfall data scale and Model Parameter Optimization；
5th step carries out rainfall inverting using the model after parameter optimization, obtains the real-time rainfall monitoring in urban area
Inverse modeling value；
6th step is established Remanent Model using the inverse modeling value and measured value residual sequence, is carried out to inversion result
Real time correction.
Preferably, further comprise the step of preliminary treatment in the first step: interpolation being carried out to individual data of losing, is removed bright
The aobvious interference data more than response lag.
Preferably, second step further comprises following step:
Using the signal strength data after preliminary treatment as sample data, learnt using the ICA algorithm in blind source separating
To one group of independent base vector；The good ICA algorithm of application training isolates the microwave signal of different distributions, counts each separation signal
Feature, including intensity and frequency spectrum, the signal isolated is divided into different types of by the feature of each separation signal according to statistics
Clutter attenuation signal and the deamplification as caused by rainfall establish big data characteristic response fingerprint base；By clutter attenuation signal from
It is eliminated in signal after preliminary treatment, the microwave signal intensity that obtains that treated.
Preferably, the step of noise signal separates further comprises following step:
Whitening matrix is passed through for the signal S (t) after preliminary treatmentIt carries out albefaction and obtains the signal after albefactionSignal separator is carried out using fixed point iteration algorithm:
(1) i=1 is enabled；
(2) choosing norm is 1 initial random matrix U (0), enables k=1；
(3) u is enabledi(k)=E [(xi(k-1)Txi)3]-3ui(k-1)；
(4) it enables
(5) if | ui(k)Tui(k-1) | 1 is converged on, then stops iteration, exports ui(k), it otherwise enables k=k+1, returns to the
(3) step continues iteration；
(6) if i is less than source signal number, (2) step is returned to, until institute active signal O (t) is separated.
Preferably, the step of noise signal identifies further comprises following step:
For the source signal O (t) isolated, the feature of each signal, including amplitude distribution characteristic, spectral property, correlation are counted
Property, it is non-stationary, classify to signal with different type according to feature, and it is special to establish the clutter big data under the influence of varying environment
Sign response fingerprint base.
Preferably, the step of noise signal removes further comprises following step: Signal separator and feature extraction are utilized,
And be compared with clutter characteristic fingerprint in big data characteristic response fingerprint base, it quickly identifies clutter type, and be removed, obtains
To treated signal y (t).
Preferably, the 4th step further comprise the following steps that using scale matching algorithm by microwave rainfall data and packet
The multi-source rainfall data for including precipitation station, radar and satellite carry out scale matching；Parameters sensitivity analysis is carried out to inverse model, really
Determine sensitive parameter；Model sensitive parameter is optimized using SCE-UA optimization algorithm, reduces model parameter uncertainty.
Preferably, parameters sensitivity analysis is carried out to inverse model, determines sensitive parameter, which further comprises as follows
The step of:
Sensitivity analysis is carried out to inverse model parameter using modified Morris screening method, by independent variable with fixed step size
Variation, using the mean value of calculated Morris coefficient after multiple disturbance as the sensitivity factor of parameter.
Preferably, model sensitive parameter is optimized using SCE-UA optimization algorithm, reduces model parameter uncertainty,
The step further comprises following step: determining after obtaining responsive parameter, utilizes chromatographic technique combination Geographical Weighted Regression
Method handles link raininess with earth station data, obtains the gridding information of same scale；Earth station's data, radar will be merged
Data and the Rainfall Products of satellite data production carry out model responsive parameter using SCE-UA algorithm excellent as measured value
Change.
Preferably, the 6th step further comprise the following steps that using autogression of error correcting algorithm to inversion result into
Row real time correction.
The beneficial effects of the present invention are: (1) it in the present invention by separation and tagsort to microwave signal is established big
Data characteristics responds fingerprint base, can in the complex environment of quick separating city separate sources noise signal, be not only able to satisfy city
The demand of the real-time rainfall monitoring in city, and more standby scientific and theoretical foundation；(2) this method is separated according to characteristic fingerprint identification
Noise wave removing, obtained decay intensity signal can more really react the response relation between rainfall and microwave, facilitate
More accurate response model parameter out；(3) the model sensitive parameter Optimization Steps in the present invention improve the accurate of inverse model
Degree and stability, being optimized using rapid optimizing algorithm to model parameter is to guarantee real-time rainfall monitoring under the complex environment of city
The important leverage that technology is realized；(4) real time correction is carried out to inversion result using Remanent Model combination multi-source data in the present invention,
It is to guarantee that inversion result stablizes one of accurate committed step, is applied to that there is high-resolution to by Microwave Net monitoring rainfall technology
The Urban Rain monitoring of rate demand is of great significance.(5) present invention can construct 100 meters -200 meters of spatial resolution, time
The multiple resolution ratio rainfall big data in -10 minutes 5 minutes cities of resolution ratio can better adapt to urban area high-resolution drop
The demand of rain observation.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention according to the embodiment.
Specific embodiment
In order to keep the objectives, technical solutions, and advantages of the present invention more explicit, following embodiment further illustrates this
The content of invention, but should not be construed as limiting the invention.Without departing from the spirit and substance of the case in the present invention, to this hair
Modification and replacement made by bright method, step or condition, all belong to the scope of the present invention.Unless otherwise specified, institute in embodiment
The conventional means that technological means is well known to those skilled in the art.
The invention proposes a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition, such as Fig. 1
It is shown.
The signal strength data R (t) that Microwave Net is collected carries out preliminary processing, and the reasons such as signal collision are generated
Individual time point loss of data carry out interpolation, rejected to instrument failure and more than the numerical value of zone of reasonableness, obtain just
Step treated signal S (t).
Whitening matrix is passed through for the signal S (t) after preliminary treatmentIt carries out albefaction and obtains the signal after albefactionSignal separator is carried out using fixed point iteration algorithm:
(1) i=1 is enabled；
(2) choosing norm is 1 initial random matrix U (0), enables k=1；
(3) u is enabledi(k)=E [(xi(k-1)Txi)3]-3ui(k-1)；
(4) it enables
(5) if | ui(k)Tui(k-1) | 1 is converged on, then stops iteration, exports ui(k), it otherwise enables k=k+1, returns to the
(3) step continues iteration；
(6) if i is less than source signal number, (2) step is returned to, until institute active signal O (t) is separated.
The feature of each signal, including amplitude distribution characteristic, spectral property, correlation are counted for the source signal O (t) isolated
Property, it is non-stationary, classify to signal with different type according to feature, and it is special to establish the clutter big data under the influence of varying environment
Sign response fingerprint base.
Compared using Signal separator and feature extraction, and with clutter characteristic fingerprint in big data characteristic response fingerprint base
It is right, quickly identify clutter type, and be removed, the signal y (t) that obtains that treated.
Using removing the data y (t) after noise wave removing, the road of each link in Microwave Net is finally inversed by according to the rain-response relation that declines
Diameter rainfall P (t)=f (y (t)), that is, establish inverse model.
The multi-sources rainfall data such as microwave rainfall data and precipitation station, radar, satellite are subjected to ruler using scale matching algorithm
Degree matching.
Sensitivity analysis is carried out to inverse model parameter using modified Morris screening method, by independent variable with fixed step size
Variation, using the mean value of calculated Morris coefficient after multiple disturbance as the sensitivity factor of parameter.Calculation formula are as follows:
In formula: Se is sensitivity Assessing parameters；yiFor the result of model i-th operation；yi+1For model i+1 time operation
Result；y0For calculated result initial value after parameter calibration；PiIt is worth variation relative to calibration for i-th model calculation parameter value
Percentage；Pi+1It is worth percentage change relative to calibration for i+1 time model calculation parameter value；N is model calculation number.
It determines after obtaining responsive parameter, using chromatographic technique combination Geographical Weighted Regression method by link raininess and earth station
Data is handled, and the gridding information of same scale is obtained.It will fusion earth station's data, radar data and satellite data production
Rainfall Products as measured value, model responsive parameter is optimized using SCE-UA algorithm.
Specific steps are as follows:
(1) it initializing, selects p >=1, m >=n+1, wherein p is the number of complex shape, and m is the points of each complex shape,
Calculating sample number is s=p × m；
(2) sample is generated.Generate s sample point x at random in feasible zone1, x2..., xs, calculate separately every xiLetter
Numerical value fi=f (xi), i=1,2 ..., s；
(3) sample point sorts, s sample point (xi, f (xi)) arranged by functional value ascending order, (x is still denoted as after sequencei, f
(xi)), i=1,2 ..., s, wherein f1≤f2≤…≤fs, remember D={ (xi, fi), i=1,2 ..., s }；
(4) compound group is divided, D is divided into p compound A1, A2..., Ap, it is each compound containing m point, make
?
(5) compound evolution is evolved each compound respectively according to CCE；
(6) whole compound each of after evolution vertex is combined into new point set, then presses functional value f by compound mixingi
Ascending order arrangement, is still denoted as D, is arranged according to the ascending order of objective function D after sequence；
(7) convergence diagnoses, and stops if meeting the condition of convergence, otherwise returns to (4).
The inverting of real-time rainfall data, obtained inversion result are carried out using the model after parameter optimizationPass through precipitation station
Real-time measuring data carries out real time correction.Using autogression of error correcting algorithm, the specific steps are as follows:
(1) by inversion resultIt is ranked up with the residual sequence e of measured value P；
(2) standardized auto-correlation coefficient formula is utilizedCalculate each rank auto-correlation coefficient r (1), r
(2),…,r(n)；
(3) autoregression model is found out by solving You Er-Wall gram equation groupCoefficient；
(4) error e at autogression of error model extrapolation t+1 moment is utilizedt+1, then t+1 moment inverting rainfall correction value
For
The invention discloses a kind of Urban Rain inversion algorithms based on microwave attenuation characteristic response fingerprint recognition, including letter
Number collection and preliminary treatment, separation classification, the foundation of clutter characteristic response big data fingerprint base, the knowledge of clutter of noise signal
It is not matched with removal, rainfall inverting, multi-source rainfall data scale, Model Parameter Optimization and inversion result real time correction.With it is existing
There is technology to compare, the invention has the following advantages that establishing in (1) present invention by the separation and tagsort to microwave signal
Characteristic response fingerprint base, can in the complex environment of quick separating city separate sources noise signal, be not only able to satisfy city reality
When rainfall monitoring demand, and more standby scientific and theoretical foundation；(2) this method separates removal of impurities according to characteristic fingerprint identification
Wave, obtained decay intensity signal can more really react the response relation between rainfall and microwave, help to obtain more
Accurate response model parameter；(3) present invention in model sensitive parameter Optimization Steps improve inverse model accuracy and
Stability, being optimized using rapid optimizing algorithm to model parameter is to guarantee real-time rainfall monitoring technology under the complex environment of city
The important leverage of realization；(4) real time correction is carried out to inversion result using Remanent Model combination multi-source data in the present invention, is to protect
Card inversion result stablizes one of accurate committed step, is applied to that there is high-resolution to need to by Microwave Net monitoring rainfall technology
The Urban Rain monitoring asked is of great significance；(5) present invention can construct 100 meters -200 meters of spatial resolution, time resolution
The multiple resolution ratio rainfall big data in -10 minutes 5 minutes cities of rate can better adapt to urban area high-resolution rainfall sight
The demand of survey.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify to technical solution documented by previous embodiment, or some or all of the technical features are equal
Replacement；And these are modified or replaceed, technical solution of the embodiment of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition, which is characterized in that including as follows
The step of:
The first step obtains the microwave attenuation signal strength data of microwave signal receiving end, and carries out to signal strength data preliminary
Processing；
Second step carries out separation, identification and the removal of noise signal to the signal strength data after preliminary treatment；
Third step decays response relation according to the microwave space-time under different atmospheric conditions, establishes rain-and decline inverse model, utilizes the
Treated in two steps, and microwave signal intensity inverting obtains path rainfall；
4th step, the matching of multi-source rainfall data scale and Model Parameter Optimization；
5th step carries out rainfall inverting using the model after parameter optimization, obtains the inverting of the real-time rainfall monitoring in urban area
The analogue value；
6th step establishes Remanent Model using the inverse modeling value and measured value residual sequence, carries out to inversion result real-time
Correction.
2. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 1,
It is characterized in that, further comprising the step of preliminary treatment in the first step: carrying out interpolation to individual data of losing, remove obvious super
Cross the interference data of response lag.
3. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 1,
It is characterized in that, second step further comprises following step:
Using the signal strength data after preliminary treatment as sample data, learn to obtain one using the ICA algorithm in blind source separating
The independent base vector of group；The good ICA algorithm of application training isolates the microwave signal of different distributions, counts the spy of each separation signal
The signal isolated is divided into different types of clutter by the feature of sign, including intensity and frequency spectrum, each separation signal according to statistics
Deamplification and the deamplification as caused by rainfall establish big data characteristic response fingerprint base；By clutter attenuation signal from preliminary
It is eliminated in treated signal, the microwave signal intensity that obtains that treated.
4. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 3,
It is characterized in that, noise signal further comprises following step the step of separation:
Whitening matrix is passed through for the signal S (t) after preliminary treatmentIt carries out albefaction and obtains the signal after albefactionSignal separator is carried out using fixed point iteration algorithm:
(1) i=1 is enabled；
(2) choosing norm is 1 initial random matrix U (0), enables k=1；
(3) u is enabledi(k)=E [(xi(k-1)Txi)3]-3ui(k-1)；
(4) it enables
(5) if | ui(k)Tui(k-1) | 1 is converged on, then stops iteration, exports ui(k), k=k+1 is otherwise enabled, (3) step is returned,
Continue iteration；
(6) if i is less than source signal number, (2) step is returned to, until institute active signal O (t) is separated.
5. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 3,
It is characterized in that, noise signal further comprises following step the step of identification:
For the source signal O (t) isolated, the feature of each signal is counted, including amplitude distribution characteristic, spectral property, correlation, non-
Stationarity classifies to signal with different type according to feature, and establishes the clutter big data feature sound under the influence of varying environment
Answer fingerprint base.
6. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 3,
It is characterized in that, noise signal further comprises following step the step of removal: using Signal separator and feature extraction, and with
Clutter characteristic fingerprint is compared in big data characteristic response fingerprint base, quickly identifies clutter type, and be removed, obtains everywhere
Signal y (t) after reason.
7. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 1,
It is characterized in that, the 4th step is further comprised the following steps that using scale matching algorithm by microwave rainfall data and including rain
The multi-source rainfall data at amount station, radar and satellite carry out scale matching；Parameters sensitivity analysis is carried out to inverse model, is determined quick
Feel parameter；Model sensitive parameter is optimized using SCE-UA optimization algorithm, reduces model parameter uncertainty.
8. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 7,
It is characterized in that, carrying out parameters sensitivity analysis to inverse model, sensitive parameter is determined, which further comprises following step
It is rapid:
Sensitivity analysis is carried out to inverse model parameter using modified Morris screening method, independent variable is become with fixed step size
Change, using the mean value of calculated Morris coefficient after multiple disturbance as the sensitivity factor of parameter.
9. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 7,
It is characterized in that, optimizing using SCE-UA optimization algorithm to model sensitive parameter, model parameter uncertainty, the step are reduced
Suddenly further comprise following step: determining after obtaining responsive parameter, it will using chromatographic technique combination Geographical Weighted Regression method
Link raininess is handled with earth station's data, obtains the gridding information of same scale；Earth station's data, radar data will be merged
And the Rainfall Products of satellite data production optimize model responsive parameter using SCE-UA algorithm as measured value.
10. a kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition according to claim 1,
Inversion result is carried out in fact using autogression of error correcting algorithm it is characterized in that, the 6th step is further comprised the following steps that
Shi Jiaozheng.
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| CN111337396A (en) * | 2020-02-17 | 2020-06-26 | 河海大学 | Microwave attenuation atmospheric PM2.5 monitoring method based on field self-adaption |
| WO2021164238A1 (en) * | 2020-02-18 | 2021-08-26 | 河海大学 | Adjustable link length microwave rain attenuation pattern artificial rainfall experimentation method |
| GB2600190A (en) * | 2020-02-18 | 2022-04-27 | Hohe Univ | Adjustable link length microwave rain attenuation pattern artificial rainfall experimentation method |
| GB2600190B (en) * | 2020-02-18 | 2023-09-27 | Hohe Univ | Improved method of measurement of the disruption of microwave communication by rain |
| CN112131989A (en) * | 2020-09-15 | 2020-12-25 | 河海大学 | Millimeter wave rain measurement model parameter obtaining method based on space rainfall data |
| CN112131990A (en) * | 2020-09-15 | 2020-12-25 | 河海大学 | Millimeter wave network rainfall inversion model construction method suitable for complex scene |
| CN112731567A (en) * | 2021-01-11 | 2021-04-30 | 河海大学 | Time-space collaborative dry-wet enhancement discrimination method for ultrahigh frequency microwave |
| CN112731567B (en) * | 2021-01-11 | 2021-09-21 | 河海大学 | Time-space collaborative dry-wet enhancement discrimination method for ultrahigh frequency microwave |
| CN114170427A (en) * | 2021-11-12 | 2022-03-11 | 河海大学 | Wireless microwave rain attenuation model SSIM image similarity evaluation method based on rain cells |
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