CN109255100B - Urban rainfall inversion method based on microwave attenuation characteristic response fingerprint identification - Google Patents
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Abstract
The invention discloses an urban rainfall inversion algorithm based on microwave attenuation characteristic response fingerprint identification, which comprises the steps of microwave signal acquisition and preliminary processing, clutter signal separation and classification, clutter characteristic response fingerprint library establishment, clutter identification and removal, rainfall inversion, multi-source rainfall data scale matching, model parameter optimization and inversion result real-time correction. The invention can realize the quick judgment and removal of the influence caused by different types of clutter in microwave attenuation signals in urban environment, and provides urban multi-resolution rainfall big data with the spatial resolution of 100-200 m and the time resolution of 5-10 min.
Description
Technical Field
The invention relates to the technical field of meteorological factor monitoring, in particular to a city rainfall inversion method based on microwave attenuation characteristic response fingerprint identification.
Background
The existing common real-time rainfall data monitoring modes mainly comprise a rainfall station and a radar, wherein in an ideal state, the ground rainfall station can provide more accurate point rainfall data for 5min or more, but the ground rainfall station has the outstanding problems of high construction cost, difficult management and maintenance, low station network density, poor data reliability and the like, compared with the requirement of urban fine management, the ground rainfall station has a large difference, radar observation is also an important monitoring means, rainfall distribution with the time resolution of 5min and the spatial resolution of 100m × 100m can be provided at most, but the radar has the defects of easy influence of shielding and blocking of tall buildings and foreign body echoes, short-distance blind areas, high installation and maintenance cost, low estimation accuracy of far-distance points and the like.
The rainfall real-time monitoring by utilizing wireless microwave communication signal decay is a novel rainfall monitoring technology, the wireless microwave communication adopts electromagnetic waves with the wavelength of 1mm-1m for communication, and when the electromagnetic waves pass through a rainfall area, raindrops can absorb and scatter the electromagnetic waves, so that obvious attenuation is caused. The ITU-R rain attenuation formula is published by the International telecommunication Union ITU on the basis of summarizing microwave rain attenuation characteristics. According to the formula, the attenuation value of the signal intensity of the receiving and transmitting end of the microwave link is used as the basis for inverting the rainfall, and the rainfall in tens of meters close to the surface of the earth can be obtained by inversion after clutter and noise influence is removed.
The city is the key area of rainfall monitoring, and because the underlying surface condition is complicated, the rainfall spatial heterogeneity is high, and a high-resolution rainfall monitoring technology is urgently needed. The rainfall monitoring by using the wireless microwave communication signals can be realized by means of the existing wireless base station communication facilities, the infrastructure investment and construction are not needed, a large amount of disposable infrastructure investment and a large amount of operation and personnel maintenance cost are saved, the method has the remarkable advantages of high construction speed, small investment, convenience in maintenance, flexibility in encryption observation and the like, and is a brand new way for solving the problem of acquiring urban high-space-time resolution rainfall information.
However, urban area environments are complex, different types of noise and clutter greatly affect the capacity of capturing rainfall by microwave signals, and therefore, how to quickly identify and extract the characteristic response of microwave decay to sensitive atmospheric environment variables such as water vapor content and atmospheric visibility to further remove the influence of environmental noise and clutter is one of the key problems that the technology needs to be considered when being applied to urban high-resolution real-time rainfall data construction, and is also a main defect of current research. On the other hand, the error cannot be completely eliminated because the microwave signal is inevitably influenced by various factors such as clutter, super refraction, plant growth and the like in the transmission process. Meanwhile, in different areas or different rainfall processes, the inversion model parameter values have large differences, so that the deviation between the rainfall estimated value and the actual rainfall value is large, and for cities with limited rainfall ground stations, the data fusion technology and the correction method are important guarantees for improving the precision and the stability of rainfall observation data.
Disclosure of Invention
The invention aims to provide a microwave attenuation characteristic response fingerprint identification-based urban rainfall inversion method, which can realize the quick judgment and removal of the influence caused by different types of clutter in microwave attenuation signals in an urban environment and provide urban multi-resolution rainfall big data with the spatial resolution of 100-200 m and the time resolution of 5-10 min.
In order to achieve the purpose, the invention provides a microwave attenuation characteristic response fingerprint identification-based urban rainfall inversion method, which is characterized by comprising the following steps of:
the method comprises the steps of firstly, obtaining microwave attenuation signal intensity data of a microwave signal receiving end, and carrying out primary processing on the signal intensity data;
secondly, separating, identifying and removing clutter signals from the signal intensity data after the preliminary processing;
thirdly, establishing a rain-attenuation inversion model according to the microwave time-space decay response relation under different atmospheric conditions, and performing inversion by using the microwave signal strength processed in the second step to obtain the path rainfall;
fourthly, multi-source rainfall data scale matching and model parameter optimization;
fifthly, performing rainfall inversion by using the model after parameter optimization to obtain an inversion simulation value of real-time rainfall monitoring of the urban area;
and sixthly, establishing a residual error model by using the inversion simulation value and the measured value residual error sequence, and correcting the inversion result in real time.
Preferably, the step of preliminary processing in the first step further comprises: and (4) interpolating individual lost data to remove interference data obviously exceeding a response threshold.
Preferably, the second step further comprises the steps of:
using the signal intensity data after the primary processing as sample data, and learning by utilizing an ICA (independent component analysis) method in blind source separation to obtain a group of independent basis vectors; separating microwave signals with different distributions by applying a trained ICA method, counting the characteristics of each separated signal, including amplitude distribution characteristics, spectral characteristics, correlation and non-stationarity, dividing the separated signals into clutter attenuation signals with different types and attenuation signals caused by rainfall according to the counted characteristics of each separated signal, and establishing a big data characteristic response fingerprint library; and eliminating the clutter attenuation signal from the signal after the preliminary processing to obtain the processed microwave signal intensity.
Preferably, the step of separating the clutter signals further comprises the steps of:
for the signal S (t) after the preliminary processing through a whitening matrix
Whitening to obtain whitened signal
And (3) separating signals by using a fixed point iteration method:
(1) let i equal to 1;
(2) selecting an initial random matrix u (0) with norm 1, and making k equal to 1;
(3) let ui(k)＝E[(xi(k-1)Txi)3]-3ui(k-1)；
(4) Order to
(5) If ui(k)TuiIf (k-1) | converges to 1, the iteration is stopped and u is outputi(k) Otherwise, making k equal to k +1, returning to the step (3), and continuing iteration;
(6) if i is less than the number of the source signals, returning to the step (2) until all the source signals O (t) are separated.
Preferably, the step of clutter signal identification further comprises the steps of:
for the separated source signals O (t), the characteristics of each signal, including amplitude distribution characteristics, spectral characteristics, correlation and non-stationarity, are counted, different types of signals are classified according to the characteristics, and a clutter big data characteristic response fingerprint library under different environmental influences is established.
Preferably, the step of removing the clutter signal further comprises the steps of: and (3) performing signal separation and feature extraction, comparing the signal with clutter feature fingerprints in a big data feature response fingerprint library, quickly identifying the clutter type, and removing to obtain a processed signal y (t).
Preferably, the fourth step further comprises the steps of: carrying out scale matching on the microwave rainfall data and multi-source rainfall data comprising a rainfall station, a radar and a satellite by using a scale matching method; performing parameter sensitivity analysis on the inversion model to determine sensitive parameters; and optimizing the sensitive parameters of the model by using an SCE-UA optimization method, and reducing the uncertainty of the parameters of the model.
Preferably, the parameter sensitivity analysis is performed on the inversion model to determine the sensitivity parameters, and the step further includes the following steps:
and performing sensitivity analysis on the inversion model parameters by using a corrected Morris screening method, changing the independent variable by a fixed step length, and taking the mean value of the Morris coefficient calculated after multiple disturbances as the sensitivity factor of the parameters.
Preferably, the SCE-UA optimization method is used to optimize the model sensitive parameters and reduce the uncertainty of the model parameters, and the method further comprises the following steps: after the sensitivity parameters are determined, processing the link rain intensity and the ground station data by utilizing a chromatography technology in combination with a geographical weighted regression method to obtain grid information with the same scale; and (3) optimizing the model sensitivity parameters by using an SCE-UA method by taking rainfall products produced by fusing ground station data, radar data and satellite data as measured values.
Preferably, the sixth step further comprises the steps of: and correcting the inversion result in real time by adopting an error autoregressive correction method.
The invention has the beneficial effects that: (1) according to the method, the large data characteristic response fingerprint database is established by separating and classifying the characteristics of the microwave signals, clutter signals from different sources in the urban complex environment can be quickly separated, the requirement of urban real-time rainfall monitoring can be met, and scientific and theoretical basis is provided; (2) the method separates and removes the clutter according to the characteristic fingerprint identification, and the obtained attenuation intensity signal can reflect the response relation between the rainfall and the microwave more truly, thereby being beneficial to obtaining more accurate response model parameters; (3) the model sensitive parameter optimization step in the invention improves the accuracy and stability of the inversion model, and the optimization of the model parameters by using a rapid optimization method is an important guarantee for ensuring the realization of the real-time rainfall monitoring technology in the urban complex environment; (4) the method utilizes the residual error model and combines multi-source data to correct the inversion result in real time, is one of key steps for ensuring the stability and the accuracy of the inversion result, and has important significance for applying the microwave network rainfall monitoring technology to urban rainfall monitoring with high resolution requirements. (5) The method can construct urban multi-resolution rainfall big data with the spatial resolution of 100-200 m and the time resolution of 5-10 min, and can better meet the requirement of high-resolution rainfall observation in urban areas.
Drawings
Fig. 1 is a flow diagram of the method of the present invention according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the following examples further illustrate the contents of the present invention, but should not be construed as limiting the present invention. Modifications and substitutions to methods, procedures, or conditions of the invention may be made without departing from the spirit and substance of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
The invention provides an urban rainfall inversion method based on microwave attenuation characteristic response fingerprint identification, which is shown in figure 1.
And (3) carrying out preliminary processing on the signal intensity data R (t) collected by the microwave network, interpolating the data loss of individual time points generated by signal collision and other reasons, and eliminating numerical values of instrument failure and exceeding a reasonable range to obtain a signal S (t) after the preliminary processing.
For the signal S (t) after the preliminary processing through a whitening matrix
Whitening to obtain whitened signal
And (3) separating signals by using a fixed point iteration method:
(1) let i equal to 1;
(2) selecting an initial random matrix u (0) with norm 1, and making k equal to 1;
(3) let ui(k)＝E[(xi(k-1)Txi)3]-3ui(k-1)；
(4) Order to
(5) If ui(k)TuiIf (k-1) | converges to 1, the iteration is stopped and u is outputi(k) Otherwise, making k equal to k +1, returning to the step (3), and continuing iteration;
(6) if i is less than the number of the source signals, returning to the step (2) until all the source signals O (t) are separated.
And for the separated source signals O (t), counting the characteristics of each signal, including amplitude distribution characteristics, spectral characteristics, correlation and non-stationarity, classifying different types of signals according to the characteristics, and establishing a clutter big data characteristic response fingerprint database under different environmental influences.
And (3) performing signal separation and feature extraction, comparing the signal with clutter feature fingerprints in a big data feature response fingerprint library, quickly identifying the clutter type, and removing to obtain a processed signal y (t).
And (f) (y (t)) according to the rainfall-attenuation response relation, inverting the path rainfall P (t) of each link in the microwave network by using the data y (t) after the removal of the impurity, and establishing an inversion model.
And (3) carrying out scale matching on the microwave rainfall data and the multi-source rainfall data such as a rainfall station, a radar, a satellite and the like by using a scale matching method.
And performing sensitivity analysis on the inversion model parameters by using a corrected Morris screening method, changing the independent variable by a fixed step length, and taking the mean value of the Morris coefficient calculated after multiple disturbances as the sensitivity factor of the parameters. The calculation formula is as follows:
in the formula: se is a sensitivity discrimination factor; y isiIs the result of the model's i-th run; y isi+1Is the result of the (i + 1) th run of the model; y is0Calculating knots after parameter calibrationAn initial value of a fruit; piThe change percentage of the parameter value of the ith model operation relative to the calibration value is calculated; pi+1The 1+1 st model operation parameter value is the percentage of change relative to the calibration value; n is the number of model operations.
And after determining the sensitivity parameters, processing the link rain intensity and the ground station data by utilizing a chromatography technology in combination with a geographical weighted regression method to obtain grid information with the same scale. And (3) optimizing the model sensitivity parameters by using an SCE-UA method by taking rainfall products produced by fusing ground station data, radar data and satellite data as measured values.
The method comprises the following specific steps:
(1) initializing, selecting p to be more than or equal to 1, and m to be more than or equal to n +1, wherein p is the number of complex shapes, m is the number of points of each complex shape, and calculating the number of samples to be s-p × m;
(2) a sample is generated. Randomly generating s sample points x in feasible domain1，x2，…，xsSeparately calculate each point xiFunction value f ofi＝f(xi),i＝1,2,…,s；
(3) Sorting the sample points by sorting s sample points (x)i，f(xi) Arranged in ascending order of function value, and still marked as (x) after sortingi，f(xi) I ═ 1,2, …, s, where f1≤f2≤…≤fsLet D { (x)i,fi),i＝1,2,…,s}；
(4) Dividing the compound type group into p compound type A1,A2,…,ApEach composite type contains m points, so that
(5) Compound evolution, namely respectively evolving each compound according to CCE;
(6) compound mixing, combining all the vertexes of each compound into a new point set, and then according to the function value fiArranging in an ascending order, marking the D after the ordering, and arranging the D according to the ascending order of the target function;
(7) and (4) stopping the convergence diagnosis if the convergence condition is met, and returning to the step (4) if the convergence condition is not met.
Performing inversion of real-time rainfall data by using the model after parameter optimization to obtain an inversion result
And carrying out real-time correction through real-time measurement data of the rainfall station. An error autoregressive correction method is adopted, and the method comprises the following specific steps:
(1) the inversion result is obtained
Sequencing the residual error sequence e with the measured value P;
(2) using normalized autocorrelation coefficient formula
Calculating autocorrelation coefficients r (1), r (2), … and r (n) of each order;
(3) solving an autoregressive model by solving a system of equations
The coefficient of (a);
(4) extrapolation of the error e at time t +1 using an error autoregressive modelt+1If the rainfall correction value is inverted at the time t +1
Is composed of
The invention discloses a microwave attenuation characteristic response fingerprint identification-based urban rainfall inversion method which comprises the steps of signal collection and preliminary processing, clutter signal separation and classification, clutter characteristic response big data fingerprint library establishment, clutter identification and removal, rainfall inversion, multi-source rainfall data scale matching, model parameter optimization and inversion result real-time correction. Compared with the prior art, the invention has the following advantages: (1) according to the method, the microwave signals are separated and classified according to characteristics, a characteristic response fingerprint database is established, clutter signals from different sources in the urban complex environment can be rapidly separated, the requirement of urban real-time rainfall monitoring can be met, and scientific and theoretical bases are provided; (2) the method separates and removes the clutter according to the characteristic fingerprint identification, and the obtained attenuation intensity signal can reflect the response relation between the rainfall and the microwave more truly, thereby being beneficial to obtaining more accurate response model parameters; (3) the model sensitive parameter optimization step in the invention improves the accuracy and stability of the inversion model, and the optimization of the model parameters by using a rapid optimization method is an important guarantee for ensuring the realization of the real-time rainfall monitoring technology in the urban complex environment; (4) the method utilizes the residual error model and combines multi-source data to correct the inversion result in real time, is one of key steps for ensuring the stability and the accuracy of the inversion result, and has important significance for applying the microwave network rainfall monitoring technology to urban rainfall monitoring with high resolution requirement; (5) the method can construct urban multi-resolution rainfall big data with the spatial resolution of 100-200 m and the time resolution of 5-10 min, and can better meet the requirement of high-resolution rainfall observation in urban areas.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (6)
1. A microwave attenuation characteristic response fingerprint identification-based urban rainfall inversion method is characterized by comprising the following steps:
the method comprises the steps of firstly, obtaining microwave attenuation signal intensity data of a microwave signal receiving end, and carrying out primary processing on the signal intensity data;
secondly, separating, identifying and removing clutter signals from the signal intensity data after the preliminary processing; the second step further comprises the steps of:
using the signal intensity data after the primary processing as sample data, and learning by utilizing an ICA (independent component analysis) method in blind source separation to obtain a group of independent basis vectors; separating microwave signals with different distributions by applying a trained ICA method, counting the characteristics of each separated signal, including amplitude distribution characteristics, spectral characteristics, correlation and non-stationarity, dividing the separated signals into clutter attenuation signals with different types and attenuation signals caused by rainfall according to the counted characteristics of each separated signal, and establishing a big data characteristic response fingerprint library; eliminating the clutter attenuation signal from the signal after the preliminary processing to obtain the intensity of the processed microwave signal;
the step of separating the clutter signals further comprises the steps of:
for the signal S (t) after the preliminary processing through a whitening matrix
Whitening to obtain whitened signal
And (3) separating signals by using a fixed point iteration method:
(1) let i equal to 1;
(2) selecting an initial random matrix u (0) with norm 1, and making k equal to 1;
(3) let ui(k)＝E[(xi(k-1)Txi)3]-3ui(k-1)；
(4) Order to
(5) If ui(k)TuiIf (k-1) | converges to 1, the iteration is stopped and u is outputi(k) Otherwise, making k equal to k +1, returning to the step (3), and continuing iteration;
(6) if i is less than the number of the source signals, returning to the step (2) until all the source signals O (t) are separated;
the step of clutter signal identification further comprises the steps of:
for the separated source signals O (t), counting the characteristics of each signal, including amplitude distribution characteristics, spectral characteristics, correlation and non-stationarity, classifying different types of signals according to the characteristics, and establishing a clutter big data characteristic response fingerprint database under different environmental influences;
the step of removing the clutter signal further comprises the steps of: signal separation and characteristic extraction are utilized, and the clutter characteristic fingerprints in the big data characteristic response fingerprint library are compared, the clutter type is rapidly identified and removed, and a processed signal y (t) is obtained;
thirdly, establishing a rain-attenuation inversion model according to the microwave time-space decay response relation under different atmospheric conditions, and performing inversion by using the microwave signal strength processed in the second step to obtain the path rainfall;
fourthly, multi-source rainfall data scale matching and model parameter optimization;
fifthly, performing rainfall inversion by using the model after parameter optimization to obtain an inversion simulation value of real-time rainfall monitoring of the urban area;
and sixthly, establishing a residual error model by using the inversion simulation value and the measured value residual error sequence, and correcting the inversion result in real time.
2. The method for urban rainfall inversion based on microwave attenuation characteristic response fingerprint identification according to claim 1, wherein the preliminary processing step in the first step further comprises: and (4) interpolating individual lost data to remove interference data obviously exceeding a response threshold.
3. The method of claim 1, wherein the fourth step comprises the steps of: carrying out scale matching on the microwave rainfall data and multi-source rainfall data comprising a rainfall station, a radar and a satellite by using a scale matching method; performing parameter sensitivity analysis on the inversion model to determine sensitive parameters; and optimizing the sensitive parameters of the model by using an SCE-UA optimization method, and reducing the uncertainty of the parameters of the model.
4. The method of claim 3, wherein the inversion model is subjected to parameter sensitivity analysis to determine sensitivity parameters, and the method further comprises the following steps:
and performing sensitivity analysis on the inversion model parameters by using a corrected Morris screening method, changing the independent variable by a fixed step length, and taking the mean value of the Morris coefficient calculated after multiple disturbances as the sensitivity factor of the parameters.
5. The method of claim 3, wherein the SCE-UA optimization method is used to optimize model sensitive parameters to reduce uncertainty of model parameters, the method further comprising the steps of: after the sensitivity parameters are determined, processing the link rain intensity and the ground station data by utilizing a chromatography technology in combination with a geographical weighted regression method to obtain grid information with the same scale; and (3) optimizing the model sensitivity parameters by using an SCE-UA method by taking rainfall products produced by fusing ground station data, radar data and satellite data as measured values.
6. The method of claim 1, wherein the sixth step further comprises the steps of: and correcting the inversion result in real time by adopting an error autoregressive correction method.
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