CN111337549A - GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy - Google Patents
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Abstract
The invention discloses a GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy, which belongs to the technical field of soil humidity measurement, namely, the GNSS-IR technology is utilized to extract the oscillation amplitude of signal-to-noise ratios of three frequency bands of all satellites from a GPS receiver, the optimal multi-satellite three-frequency oscillation amplitude fusion value is obtained by fusion through the fuzzy entropy method, and an inversion model of soil humidity is established by utilizing the fusion value. The method effectively utilizes the difference and complementarity of the GPS multi-satellite three-frequency data, and the inversion effect is obviously improved compared with a single-satellite single-frequency method; the problems in the prior art are solved.
Description
Technical Field
The invention relates to a GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy, and belongs to the technical field of soil humidity measurement.
Background
Soil humidity is an important link of global carbohydrate cycle and is a key parameter for quantifying land and atmospheric energy exchange. The method can timely and accurately acquire the farmland soil humidity data, and is very important for reasonable irrigation in agricultural production, reduction of water resource waste, reduction of production cost and improvement of crop yield. Compared with the traditional methods for acquiring soil humidity by using contact methods such as a drying and weighing method, a Time Domain Reflectometry (TDR) method, a Frequency Domain Reflectometry (FDR) method and the like, the method for detecting soil humidity by using the GNSS-R (Global navigation Satellite System Reflectometry) technology is a new technical means, has the advantages of non-contact, large area, real-time performance and continuity, and receives more and more attention in recent years.
In a method for measuring soil humidity by using a GNSS as a signal source, a technology for measuring soil humidity by using a GNSS interference signal has appeared in recent years, and the technology theoretically requires only one antenna to complete soil humidity measurement. The signal-to-noise ratio data recorded by the GNSS single-antenna receiver shows damped oscillation characteristics under the influence of multipath signals, the characteristics are the expression of interference phenomenon, the oscillation amplitude and the phase of the signal-to-noise ratio data are related to soil humidity, and an empirical model of the amplitude or the phase can be respectively established by utilizing the correlation to carry out soil humidity inversion. However, the method only utilizes the signal-to-noise ratio data of a single frequency band of a single GNSS satellite, does not consider the difference and complementarity of satellite data of different orbits, frequencies and powers, and is also greatly limited in data source.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy, which utilizes data of satellites with different orbits and frequencies to carry out weighted fusion, establishes an inversion model based on the fuzzy entropy to improve the precision of inversion soil humidity and solves the problems in the prior art.
The invention discloses a GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy, which comprises the following steps:
step 1: data acquisition:
erecting a surveying and mapping GPS receiver on an experimental site to record data of a plurality of frequency bands of all satellites, and simultaneously erecting meteorological station equipment to acquire soil humidity data of the same ratio;
step 2: SNR data processing:
selecting SNR data of a plurality of frequency bands of each satellite of a low elevation angle GPS with obvious oscillation phenomenon from a GPS receiver recording file according to the elevation angle;
and step 3: removing a direct component:
removing the direct component of the receiver from the SNR data obtained in the step 2 by utilizing polynomial fitting, and only keeping the multipath component related to the soil humidity;
and 4, step 4: obtaining an oscillation amplitude observed quantity by spectrum analysis:
performing spectrum analysis by using a signal spectrum analysis method to obtain oscillation frequency, and performing least square fitting on multipath components to obtain amplitude observed quantity;
and 5: data fusion:
representing the fuzzy degree of the current satellite data by using fuzzy entropy based on the thought of the fuzzy entropy of Zadeh, and performing a multi-satellite weighted fusion algorithm based on the fuzzy entropy on the amplitude observed quantity data obtained in the step 4 to obtain a fusion observed quantity;
step 6: establishing a relation between the fusion observed quantity and the soil humidity and inverting the soil humidity:
and (5) establishing a regression model by using the fusion observed quantity obtained in the step (5) and the actually measured soil humidity, inputting the data on the test set into the regression model, and outputting to obtain a soil humidity inversion value.
Further, the data of the multiple frequency bands in step 1 includes: data of three frequency bands of all satellites L1, L2 and L5.
Further, the elevation angle in step 2 is in the range of 2 ° to 30 °.
Further, the removing of the direct component in step 3 specifically includes the following steps:
the SNR direct and reflected signals may be expressed as:
in formula (1): a. thed、AmIndicating the amplitude of the direct and reflected signals, respectively, and psi indicating the phase difference between the direct and reflected signals;
then there are:
in formula (2):
for the phase difference caused by the direct reflection path difference,
representing an initial phase of interference, H representing an equivalent height of the receiver antenna, which varies with a penetration depth of the electromagnetic wave and a change in dielectric characteristics of the reflecting surface; λ represents the wavelength of the satellite signal, and θ represents the altitude of the satellite;
eliminating direct signal, only retaining multipath signal related to reflecting surface parameter, SNR of said multipath signalmRepresented by the formula:
in formula (3): a. themRepresenting the amplitude of the reflected signal, H representing the equivalent altitude of the receiver antenna, theta representing the altitude of the satellite,
representing the initial phase of the multipath signal.
Further, the step 4 of obtaining the oscillation amplitude observed quantity by spectrum analysis includes the following steps: performing spectrum analysis by Lomb-Scargle transformation to obtain a frequency spectrum, further obtaining a spectrum of the equivalent antenna height, selecting the equivalent antenna height value with the maximum spectrum value as an estimated value of the equivalent antenna height, and then obtaining an amplitude value by least square fitting.
Further, the multi-star weighted fusion algorithm based on the fuzzy entropy in the step 5 specifically includes the following steps:
the shannon entropy is a probability weighted average value of information quantity, and represents the uncertainty degree of the information source, if the probability space of the information source is:
its information entropy is then:
fuzzy entropy mixing sample xiFuzzy membership function mu for fuzzy event AA(xi) As a weight of shannon entropy, the fuzzy degree of the sample set with respect to the fuzzy set of the fuzzy event a is described, and is defined as:
and performing weighted fusion on the data of each satellite according to the fuzzy entropy value to obtain a fusion observed quantity which is closer to the true value of the soil humidity.
Further, the calculation of the fusion observed quantity specifically includes the following steps:
let the m-th star's effective measurement set at a certain day be
The membership degree of the ith valid measurement from the target is expressed by the formula (6), so that the ambiguity E of the current day measurement set of the satellite is obtainedmSince the larger the fuzzy entropy is, the higher the fuzzy degree of the set is, i.e. the lower the reliability is, so take
Then
The larger, the higher the reliability of the star,
then the weight of the star is:
the equivalent measurement of the m star obtained by adopting the PDA algorithm is
Then the fusion observed quantity after the weighted fusion of the multiple stars is:
further, the amplitude observed quantity obtained in the step 5 is divided into a training set and a test set according to the ratio of 2:1, and the fuzzy entropy-based multi-satellite weighted fusion algorithm is performed on the training set data to obtain a fusion observed quantity on the training set.
Compared with the prior art, the invention has the following beneficial effects:
the GPS multi-satellite fusion soil humidity monitoring method based on the fuzzy entropy extracts the oscillation amplitudes of signal-to-noise ratios of three frequency bands of all satellites from a GPS receiver by utilizing a GNSS-IR technology, performs fusion by utilizing the fuzzy entropy method to obtain an optimal multi-satellite three-frequency oscillation amplitude fusion value, and establishes an inversion model with soil humidity by utilizing the fusion value.
The data of satellites with different orbits and frequencies are fully utilized for weighted fusion, the fuzzy degree of the current satellite data is represented by the fuzzy entropy based on the thought of the fuzzy entropy of Zadeh, a multi-satellite weighted fusion algorithm based on the fuzzy entropy is provided, and a multi-satellite three-frequency inversion model based on the fuzzy entropy is established to improve the accuracy of soil humidity inversion. Compared with the traditional Larson method, the method has the advantages that the correlation coefficient is improved by 24.69%, the correlation coefficient is improved by 26.77% compared with the mean value fusion method, the root mean square error RMSE is reduced by 22.28% compared with the Larson method and is reduced by 23.26% compared with the mean value fusion method, the difference and complementarity of the GPS multi-satellite three-frequency data are effectively utilized, and the inversion effect is obviously improved compared with a single-satellite single-frequency method; the problems in the prior art are solved.
Drawings
FIG. 1 is a diagram illustrating an embodiment of a GNSS SNR interference signal;
FIG. 2 is a flow chart of data processing according to an embodiment of the present invention;
FIG. 3 is a graph of a rise period SNR data analysis according to an embodiment of the present invention;
FIG. 4 is a diagram of a GPS multi-satellite fusion model and inversion results in an embodiment of the invention;
FIG. 5 is a comparison graph of results of different inversion models in an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples:
example 1:
as shown in FIG. 2, the GPS multi-satellite fusion soil humidity monitoring method based on the fuzzy entropy comprises the following steps:
step 1: data acquisition
A GPS receiver and an antenna are erected in an experimental site, meanwhile, automatic weather station equipment is erected to collect soil humidity measured values of the same proportion, and a RINEX format is stored.
Step 2: data screening
And extracting SNR, elevation angle, azimuth angle and time of L1, L2 and L5 from the acquired data according to PRN, preliminarily screening the extracted data, and removing the data with unobvious SNR oscillation.
And step 3: data pre-processing
As shown in fig. 1, the SNR direct and reflected signals can be expressed as:
in the formula, Ad、AmIndicating the amplitude of the direct and reflected signals, respectively, and psi the phase difference between the direct and reflected signals.
Then there are:
in the formula (I), the compound is shown in the specification,
for the phase difference caused by the direct reflection path difference,
denotes an interference initial phase, and H denotes an equivalent height of the receiver antenna, which varies with a penetration depth of the electromagnetic wave and a dielectric characteristic of the reflection surface. λ represents the wavelength of the GNSS satellite signals and θ represents the altitude of the satellite. Eliminating the direct signals, only retaining the multipath signals related to the parameters of the reflecting surface,
as shown in FIG. 3, the SNR of the multipath signalmCan be represented by the following formula:
and 4, step 4: analyzing frequency spectrum and obtaining amplitude value
And performing spectrum analysis by using Lomb-Scargle transformation to obtain a frequency spectrum, further obtaining a spectrum of the equivalent antenna height, and selecting the equivalent antenna height value with the maximum spectrum value as an estimated value of the equivalent antenna height. The amplitude values are then found by least squares fitting, as shown in fig. 3. Fig. 3a shows the original SNR waveform, and it can be seen that at a low elevation angle of 30 degrees, the ringing phenomenon is significant. FIG. 3b shows the SNR of the multipath signal after the direct signal is removedmThe oscillation waveform of (1). FIG. 3c shows SNR for multipath signalsmPower spectrum waveform of the spectral analysis.
And 5: establishing multi-star fusion model based on fuzzy entropy
Based on the thought of the Zadeh fuzzy entropy, representing the fuzzy degree of the current satellite data by the fuzzy entropy, dividing the amplitude observed quantity into a training set and a test set according to the proportion of 2:1 by using a multi-satellite weighted fusion algorithm based on the fuzzy entropy, and performing the multi-satellite weighted fusion algorithm based on the fuzzy entropy on the training set data to obtain a fused observed quantity on the training set;
the Shannon entropy in the information theory is defined as a probability weighted average value of information quantity, and represents the uncertainty degree of the information source. If the source probability space is:
its information entropy is then:
fuzzy entropy mixing sample xiFuzzy membership function mu for fuzzy event AA(xi) As a weight of shannon entropy, the fuzzy degree of the sample set with respect to the fuzzy set of the fuzzy event a is described, and is defined as:
for a fusion system composed of N satellites, since the altitude angle and the azimuth angle of each satellite are different, the ambiguity degrees of effective measurement data sets obtained by each satellite are different, and the size of the ambiguity degree directly affects the confidence of the satellite in data fusion, thereby affecting the weight of the satellite in the whole fusion system. Therefore, the data of each satellite can be weighted and fused according to the fuzzy entropy value, and a fusion value which is closer to the true value of the soil humidity is obtained.
Let the m-th star's effective measurement set at a certain day be
The membership degree of the ith valid measurement from the target is expressed by the formula (6), so that the ambiguity E of the current day measurement set of the satellite is obtainedm. Since the larger the fuzzy entropy, the higher the set fuzzy degree, i.e. the lower the reliability, so take
Then
The larger the satellite is, the higher the reliability of the satellite is, the weight of the satellite is:
the equivalent measurement of the m star obtained by adopting the PDA algorithm is
Then the fusion observed quantity after the weighted fusion of the multiple stars is:
after the fusion observed quantity of the training set is obtained, a unitary linear regression model for fusing the observed quantity and the soil humidity on the training set is established, namely y (%) < 0.99x (V.V.)-1)+10.74. 0.99 is the slope of the regression model and 10.74 is the intercept. And then inputting the fusion observed quantity on the test set as an independent variable x into the model to obtain a soil humidity inversion value y. The inverse model and the results are shown in fig. 4.
The correlation between the test set inversion value obtained by the method and the soil humidity true value is good, and FIG. 4a is a fusion method training set inversion model; FIG. 4b is the inversion result of the fusion test set, and it can be seen from the graph that the correlation coefficient reaches 0.8059, the RMSE is 2.088%, and the continuous monitoring of the soil humidity in the fixed area can be better realized.
The comparison result with other inversion methods is shown in fig. 5, fig. 5a is a comparison of results of different inversion model test sets, and fig. 5b is a comparison of correlation of different inversion model test sets; compared with the traditional Larson method, the method has the advantages that the correlation coefficient is improved by 24.69%, the correlation coefficient is improved by 26.77% compared with the mean value fusion method, the root mean square error RMSE is reduced by 22.28% compared with the Larson method, the root mean square error RMSE is reduced by 23.26% compared with the mean value fusion method, and the effectiveness of the soil humidity inversion model is further verified.
By adopting the GPS multi-satellite fusion soil humidity monitoring method based on the fuzzy entropy in the embodiment of the invention described in the attached drawings, the data of satellites with different orbits and frequencies are used for weighted fusion, the inversion model based on the fuzzy entropy is established to improve the accuracy of inverting the soil humidity, and the problems in the prior art are solved. The present invention is not limited to the embodiments described, but rather, variations, modifications, substitutions and alterations are possible without departing from the spirit and scope of the present invention.
Claims (8)
1. A GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy is characterized in that: the method comprises the following steps:
step 1: data acquisition:
erecting a surveying and mapping GPS receiver on an experimental site to record data of a plurality of frequency bands of all satellites, and simultaneously erecting meteorological station equipment to acquire soil humidity data of the same ratio;
step 2: SNR data processing:
selecting SNR data of a plurality of frequency bands of each satellite of a low elevation angle GPS with obvious oscillation phenomenon from a GPS receiver recording file according to the elevation angle;
and step 3: removing a direct component:
removing the direct component of the receiver from the SNR data obtained in the step 2 by utilizing polynomial fitting, and only keeping the multipath component related to the soil humidity;
and 4, step 4: obtaining an oscillation amplitude observed quantity by spectrum analysis:
performing spectrum analysis by using a signal spectrum analysis method to obtain oscillation frequency, and performing least square fitting on multipath components to obtain amplitude observed quantity;
and 5: data fusion:
representing the fuzzy degree of the current satellite data by using fuzzy entropy based on the thought of the fuzzy entropy of Zadeh, and performing a multi-satellite weighted fusion algorithm based on the fuzzy entropy on the amplitude observed quantity data obtained in the step 4 to obtain a fusion observed quantity;
step 6: establishing a relation between the fusion observed quantity and the soil humidity and inverting the soil humidity:
and (5) establishing a regression model by using the fusion observed quantity obtained in the step (5) and the actually measured soil humidity, inputting the data on the test set into the regression model, and outputting to obtain a soil humidity inversion value.
2. The GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: the data of the plurality of frequency bands in step 1 includes: data of three frequency bands of all satellites L1, L2 and L5.
3. The GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: the low elevation angle in the step 2 ranges from 2 degrees to 30 degrees.
4. The GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: the step 3 of removing the direct component specifically includes the following steps:
the SNR direct and reflected signals may be expressed as:
in formula (1): a. thed、AmIndicating the amplitude of the direct and reflected signals, respectively, and psi indicating the phase difference between the direct and reflected signals;
then there are:
in formula (2):
for the phase difference caused by the direct reflection path difference,
representing an initial phase of interference, H representing an equivalent height of the receiver antenna, which varies with a penetration depth of the electromagnetic wave and a change in dielectric characteristics of the reflecting surface; λ represents the wavelength of the satellite signal, and θ represents the altitude of the satellite;
direct incident signalEliminating, only retaining multipath signals related to reflecting surface parameters, SNR of the multipath signalsmRepresented by the formula:
in formula (3): a. themRepresenting the amplitude of the reflected signal, H representing the equivalent altitude of the receiver antenna, theta representing the altitude of the satellite,
representing the initial phase of the multipath signal.
5. The GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: the step 4 of obtaining the oscillation amplitude observed quantity by frequency spectrum analysis comprises the following steps: performing spectrum analysis by Lomb-Scargle transformation to obtain a frequency spectrum, further obtaining a spectrum of the equivalent antenna height, selecting the equivalent antenna height value with the maximum spectrum value as an estimated value of the equivalent antenna height, and then obtaining an amplitude value by least square fitting.
6. The GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: the multi-star weighted fusion algorithm based on the fuzzy entropy in the step 5 specifically comprises the following steps:
the shannon entropy is a probability weighted average value of information quantity, and represents the uncertainty degree of the information source, if the probability space of the information source is:
its information entropy is then:
fuzzy entropy mixing sample xiFuzzy membership to fuzzy event AFunction muA(xi) As a weight of shannon entropy, the fuzzy degree of the sample set with respect to the fuzzy set of the fuzzy event a is described, and is defined as:
and performing weighted fusion on the data of each satellite according to the fuzzy entropy value to obtain a fusion observed quantity which is closer to the true value of the soil humidity.
7. The GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: the specific calculation process of the fusion observed quantity comprises the following steps:
let the m-th star's effective measurement set at a certain day be
The membership degree of the ith valid measurement from the target is expressed by the formula (6), so that the ambiguity E of the current day measurement set of the satellite is obtainedmSince the larger the fuzzy entropy is, the higher the fuzzy degree of the set is, i.e. the lower the reliability is, so take
Then
The larger, the higher the reliability of the star,
then the weight of the star is:
the equivalent measurement of the m star obtained by adopting the PDA algorithm is
Then the fusion observed quantity after the weighted fusion of the multiple stars is:
8. the GPS multi-satellite fusion soil humidity monitoring method based on fuzzy entropy of claim 1, characterized in that: and 5, dividing the amplitude observed quantity obtained in the step 5 into a training set and a test set according to the ratio of 2:1, and performing a fuzzy entropy-based multi-satellite weighted fusion algorithm on the training set data to obtain a fusion observed quantity on the training set.
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| CN112685694A (en) * | 2020-12-30 | 2021-04-20 | 西安邮电大学 | Information entropy-based estimation method for error distribution of reflecting surface of mesh antenna |
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