A sea surface height measurement method based on Android smartphone

Abstract

The present invention relates to a sea level height measurement method based on an Android smartphone, and relates to the field of remote sensing, including: obtaining a navigation signal of an Android smartphone; performing satellite screening according to the navigation signal to obtain a screened satellite signal; performing signal-to-noise ratio sequence extraction on the screened satellite signal to obtain a signal-to-noise ratio sequence; performing empirical mode decomposition on the signal-to-noise ratio sequence to obtain multiple modal components; performing effective spectrum estimation on each modal component to obtain the main oscillation frequency of each modal component; performing height inversion using the main oscillation frequency of each modal component to obtain the sea level. The present invention can reduce the interference of satellite navigation signals reflected in non-detection areas around smartphones on sea level measurement, and improve the accuracy of sea level measurement.

Description

Technical Field

[0001] The invention relates to the field of remote sensing, and in particular to a sea surface height measurement method based on an Android smart phone. Background Art

[0002] Marine environmental changes and disasters such as sea level rise, tidal changes, and typhoons have a huge impact on the world, especially on the human and economic development of coastal areas. With the continuous increase in coastal population and economic development, monitoring of sea level changes in coastal waters is of great significance to marine engineering, disaster warning, production safety, and transportation.

[0003] Using GNSS (Global Navigation Satellites System) reflected signals to invert the physical parameters of the Earth's sea surface is one of the new technologies in the field of remote sensing, with the advantages of wide signal sources, portable equipment, and little influence from the atmosphere. Using GNSS reflected signals to measure sea surface height can effectively reduce costs, increase measurement coverage, convert traditional single-point measurements to surface measurements, reduce the required measurement equipment, and make the measurement data more effectively used in related fields such as sea surface monitoring.

[0004] With the popularity of smart phones, mobile phones have become an indispensable part of people's lives and can provide a lot of convenient services for people's lives. Most mobile phones on the market are now equipped with navigation chips that can receive raw data from satellites. Linking this function with sea level measurement can help users better measure the sea level nearby and monitor changes in sea level, which has a considerable positive impact on users' lives.

[0005] In addition to the direct signal of line-of-sight propagation, the navigation signal received by the mobile phone navigation chip also includes the signal after being reflected through multiple paths. The multipath reflection signal is usually suppressed as interference, but in fact the reflection signal is correlated with the physical characteristics of the reflecting surface, such as the sea level. The GNSS signal received by a single antenna includes the direct component and the reflected multipath component. Since the frequencies of the direct and reflected signals are approximately equal in the shore-based case, but the propagation path lengths are different, these two components will interfere with each other more stably at the receiver antenna to form an interference signal. At low altitude angles, the interference oscillation phenomenon is very obvious. The receiver will record and store this interference signal in the device record in the form of a signal-to-noise ratio (SNR). The change of the interference phenomenon is determined by the reflection characteristics of the ocean surface. Therefore, this interference phenomenon can be used to measure some important physical properties such as sea level. The mathematical model of SNR can be expressed as follows:

[0006]

[0007] Among them, A d represents the direct signal amplitude value, θ represents the GNSS satellite altitude angle, is the seawater reflection coefficient, λ is the signal wavelength, h r is the height of the mobile phone from the sea surface.

[0008] Only the characteristics of the reflected signal are related to the physical characteristics of the reflecting surface, so it is necessary to remove the direct signal in the interference signal and the multipath signal reflected by other objects to obtain the SNR of the reflected signal. Traditional methods do not consider the impact of multipath signals reflected by other objects and often output multiple false peaks. Summary of the invention

[0009] The invention aims to provide a sea level height measurement method based on an Android smart phone, reduce the interference of satellite navigation signals reflected in a non-detection area around the smart phone on the sea level height measurement, and improve the accuracy of the sea level height measurement.

[0010] To achieve the above object, the present invention provides the following solutions:

[0011] A sea surface height measurement method based on an Android smartphone, comprising:

[0012] Get navigation signals from Android smartphones;

[0013] Perform satellite screening according to the navigation signal to obtain screened satellite signals;

[0014] Extracting a signal-to-noise ratio sequence from the screened satellite signals to obtain a signal-to-noise ratio sequence;

[0015] Performing empirical mode decomposition on the signal-to-noise ratio sequence to obtain multiple modal components;

[0016] Perform effective spectrum estimation on each modal component to obtain the main oscillation frequency of each modal component;

[0017] The main oscillation frequencies of the modal components are used to perform height inversion to obtain the sea surface height.

[0018] Optionally, the performing satellite screening according to the navigation signal to obtain a screened satellite signal specifically includes:

[0019] Get GPS standard ephemeris file;

[0020] The navigation signal is compared with the ephemeris file to obtain a filtered satellite signal.

[0021] Optionally, extracting a signal-to-noise ratio sequence from the screened satellite signals to obtain a signal-to-noise ratio sequence specifically includes:

[0022] Determine the interference signal mean value according to the screened satellite signals;

[0023] Determine whether the signal-to-noise ratio sequence value of the screened satellite signal at the current moment is greater than the mean value of the interference signal, and obtain a first determination result;

[0024] If the first judgment result is yes, the signal-to-noise ratio sequence value at the next moment is used as the signal-to-noise ratio sequence value at the current moment, and the process returns to the step of "judging whether the signal-to-noise ratio sequence value of the filtered satellite signal at the current moment is greater than the mean value of the interference signal to obtain the first judgment result";

[0025] If the first judgment result is no, extracting a signal-to-noise ratio sequence according to the screened satellite signal and incrementing a sequence counter by one;

[0026] Determine whether the sequence counter is greater than a set period, and obtain a second determination result;

[0027] If the second judgment result is yes, outputting the extracted signal-to-noise ratio sequence;

[0028] If the second judgment result is no, return to the step of "extracting the signal-to-noise ratio sequence according to the screened satellite signals and increasing the sequence counter by one".

[0029] Optionally, performing empirical mode decomposition on the signal-to-noise ratio sequence to obtain multiple modal components specifically includes:

[0030] Initializing the remaining sequence components according to the signal-to-noise ratio sequence; initializing the time sequence according to the remaining sequence components;

[0031] Determine the envelope of the extreme point and the average value of the envelope according to the initialized timing;

[0032] Updating the initialization timing according to the envelope average value;

[0033] determining decomposed modal components of the time series according to the updated time series;

[0034] A plurality of modal components are determined based on the initialized residual sequence components and the decomposed modal components of the time series.

[0035] Optionally, performing effective spectrum estimation on each modal component to obtain the main oscillation frequency of each modal component specifically includes:

[0036] Perform LS spectrum estimation on each modal component to obtain characteristic observations; the characteristic observations include peak power, spectrum width and primary-secondary peak ratio of the spectrum;

[0037] Extracting the characteristic observation quantity by using a threshold method to obtain an effective spectrum;

[0038] The main oscillation frequency of each modal component is determined according to the effective spectrum.

[0039] Optionally, performing height inversion using the main oscillation frequencies of the modal components to obtain the sea surface height specifically includes:

[0040] Determine the height of the Android smartphone from the sea surface according to the main oscillation frequencies of each modal component;

[0041] The sea level is determined according to the height of the Android smartphone from the sea level.

[0042] Optionally, the expression for the height of the Android smartphone from the sea surface is:

[0043] h r =f peak λ / 2

[0044] Among them, h r is the height of the Android smartphone from the sea surface, f peak is the main oscillation frequency of each modal component, and λ is the signal wavelength.

[0045] Optionally, the expression of the sea level height is:

[0046] h ssh =H r -h r

[0047] Among them, h ssh is the sea level height, H r is the height of the smartphone from the geodetic reference plane, h r The height of the Android smartphone from the sea surface.

[0048] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0049] The present invention obtains a navigation signal of an Android smart phone; performs satellite screening according to the navigation signal to obtain a screened satellite signal; performs signal-to-noise ratio sequence extraction on the screened satellite signal to obtain a signal-to-noise ratio sequence; performs empirical mode decomposition on the signal-to-noise ratio sequence to obtain a plurality of modal components; performs effective spectrum estimation on each modal component to obtain a main oscillation frequency of each modal component; performs height inversion using the main oscillation frequency of each modal component to obtain a sea level, thereby reducing interference of a satellite navigation signal reflected in a non-detection area around a smart phone on sea level measurement and improving the accuracy of sea level measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A flow chart of the sea level height measurement method based on an Android smartphone provided by the present invention;

[0052] Figure 2 Screening flow chart for satellites;

[0053] Figure 3 Extract flow charts for sequences;

[0054] Figure 4 It is the EMD processing flow chart;

[0055] Figure 5 Extracting effective spectrum flow chart for spectrum estimation;

[0056] Figure 6 This is a schematic diagram of the sea surface height measurement system based on Android smartphone. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The invention aims to provide a sea level height measurement method based on an Android smart phone, reduce the interference of satellite navigation signals reflected in a non-detection area around the smart phone on the sea level height measurement, and improve the accuracy of the sea level height measurement.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] The present invention utilizes the Empirical Mode Decomposition (EMD) method to decompose the signal-to-noise ratio, and obtains the Intrinsic Mode Function (IMF) based on the time scale of the local characteristics of the time series. The decomposed intrinsic mode components contain the local characteristic signals of the original signal at different time scales. Because the basis function is decomposed from the data itself, the method is intuitive, direct, a posteriori and adaptive. The present invention proposes a method for extracting the interference oscillation characteristics of satellite navigation signals reflected from the sea surface by using the Empirical Mode Decomposition, conducts research on the sea surface height measurement of Android smartphones, and preliminarily evaluates its performance.

[0061] like Figure 1 As shown, the present invention provides a sea level height measurement method based on an Android smartphone, comprising:

[0062] Step 101: Acquire the navigation signal of the Android smartphone.

[0063] The present invention adopts a smart Android phone equipped with a Qualcomm Snapdragon 835 processor, 6GB LPDDDR 4x memory, supports dual-frequency GPS positioning, and the GPS antenna adopts a linear polarization antenna. The present invention extracts and stores the navigation data of the GPS L1 signal of the mobile phone through the Geo++RINEX Logger App. Afterwards, the above-mentioned stored information is associated with a self-made APP, and the APP is used to compare with the GPS official website information at this moment, verify the information, and generate an SNR sequence, and then use the EMD method to decompose the characteristic sequence, obtain the estimation of each decomposed modal spectrum, and finally obtain the effective spectrum for height inversion.

[0064] Step 102: Screen satellites according to the navigation signal to obtain screened satellite signals.

[0065] Step 102 specifically includes: obtaining a GPS standard ephemeris file; comparing the navigation signal with the ephemeris file to obtain a filtered satellite signal.

[0066] like Figure 2As shown, the GNSS data extracted by Geo++RINEX Logger does not have satellite altitude and azimuth information. The present invention calculates the altitude and azimuth of the corresponding satellite at the time of data collection through the ephemeris file published on the GPS official website. Since the back-facing signal of the smartphone is blocked by buildings and is restricted by the environment where the equipment is installed, the present invention selects GNSS signals with an azimuth of 100° to 300° for sea level measurement, reduces the environmental impact of the collected satellite signals, and then extracts satellite altitude information and obtains the SNR sequence received by the mobile phone. The altitude angle of the present invention is not strictly limited.

[0067] First, the Geo++RINEX Logger APP receives the direct signal, reflected signal and interference signal obtained by the smartphone.

[0068]

[0069] Among them, A d represents the direct signal amplitude value, θ represents the GNSS satellite altitude angle, is the seawater reflection coefficient, λ is the signal wavelength, h r is the height of the mobile phone from the sea surface.

[0070] The mobile phone extracts GNSS satellite ephemeris Rinex format data, and obtains the satellite PRN number in the data after parsing. Since the mobile phone Geo++RINEX Logger APP lacks satellite elevation angle (Elevation Angle, Ele) and azimuth angle (AzimuthAngle, Azi) information, a self-made APP is used to access the GPS official website, compare the ephemeris file, and calculate the elevation angle and azimuth angle of the corresponding satellite at the time of data collection.

[0071] The calculation method is as follows:

[0072] Calculate the average angular velocity n of the satellite

[0073]

[0074] In the formula, GM is the product of the gravitational constant G and the total mass of the earth M. In the calculation, 3.986005E+14m is taken. 3 / s 2 ;n 0 is the average angular velocity of TOE at the reference time; Δn is the perturbation correction number given in the broadcast ephemeris.

[0075] Calculate the satellite's mean anomaly when the signal is transmitted

[0076] Δt=a 0 +a 1 (t'-t oc )+a2 (t'-t oc ) 2

[0077] t=t'-Δt

[0078] t k =tt oc

[0079] t' is the GPS week second corresponding to the observation time, t oc is the GPS week second corresponding to the reference time, t is the time corrected by the satellite clock, and t k is the naturalization time. 0 is the satellite clock error, a 1 is the satellite clock number, a 2 is the satellite clock change rate, and Δt is the satellite clock correction.

[0080] M=M 0 +n×t k

[0081] M 0 is the mean anomaly at the reference time TOE, and M is the mean anomaly of the satellite when the signal is transmitted.

[0082] Calculate the eccentric anomaly angle E

[0083] E=M+e×sin E

[0084] An iterative method is used to solve E. The initial value is M, the orbital eccentricity is e, and convergence can be achieved after three iterations.

[0085] Calculate the true anomaly angle V k

[0086]

[0087] Where e is the eccentricity of the satellite orbit.

[0088] Calculate the ascending angle u

[0089] u=ω+V k

[0090] Where ω is the periapsis angular distance.

[0091] Calculate perturbation corrections

[0092] δ u =C uc ×cos 2u+C us ×sin 2u

[0093] δ r =C rc ×cos 2u+Crs ×sin 2u

[0094] δ i =C ic ×cos 2u+C is ×sin 2u

[0095] C uc , C us , C rc , C rs , C ic , C is are 6 perturbation correction parameters, δ u is the perturbation correction term of the ascending distance angle, δ r is the perturbation correction term for satellite path loss, δ i is the perturbation correction term for the satellite orbit inclination.

[0096] Calculate the perturbation-corrected ascending angle u k 、Satellite radius vector r k and orbital inclination i k

[0097] u k =u+δ u

[0098] r k =a×(1-e×cos E)+δ r

[0099] i k =i 0 +δ i +I×t k

[0100] Where a is the major radius of the satellite orbit, i 0 is the orbital inclination at the reference time TOE, and I is the rate of change of the orbital inclination i.

[0101] Calculate the coordinates of the satellite in the orbital plane coordinate system

[0102] x=r×cos u k

[0103] y=r×sin u k

[0104] Calculate the longitude of the ascending node at the time of launch

[0105] L=Ω 0 +Ω×t k -ω e ×(t k +t oe )

[0106] Ω0 is the right ascension of the ascending node at the reference time TOE, Ω is the rate of change of the longitude of the ascending node with respect to time, t k is the time difference between the launch time and the reference time TOE, ω e is the Earth's rotation speed, 7.29211567E-5rad / s is used in the calculation. oe is the ephemeris reference time.

[0107] Calculate the coordinates of the satellite in the Earth-fixed coordinate system

[0108] Xs=x×cos Ly×cos i k ×sin L

[0109] Ys=x×sin L+y×cos i k ×cos L

[0110] Zs=y×sin i k

[0111] Calculate altitude and azimuth

[0112]

[0113]

[0114] Where, Ele is the altitude angle, and A is the azimuth angle. The Earth-fixed coordinate system has the origin O (0,0,0) as the center of mass of the Earth, the z-axis is parallel to the Earth's axis and points to the North Pole, the x-axis points to the intersection of the prime meridian and the equator, and the y-axis is perpendicular to the xOz plane (i.e. the intersection of 90 degrees east longitude and the equator). Xs is the x-axis coordinate value of the satellite in this coordinate system, Ys is the y-axis coordinate value of the satellite in this coordinate system, and Zs is the z-axis coordinate value of the satellite in this coordinate system.

[0115] Step 103: extracting a signal-to-noise ratio sequence from the screened satellite signals to obtain a signal-to-noise ratio sequence.

[0116] Step 103 specifically includes:

[0117] The interference signal mean is determined according to the filtered satellite signals.

[0118] It is determined whether the signal-to-noise ratio sequence value of the filtered satellite signal at the current moment is greater than the mean value of the interference signal to obtain a first determination result; if the first determination result is yes, the signal-to-noise ratio sequence value at the next moment is used as the signal-to-noise ratio sequence value at the current moment, and the process returns to the step of “determining whether the signal-to-noise ratio sequence value of the filtered satellite signal at the current moment is greater than the mean value of the interference signal to obtain a first determination result”; if the first determination result is no, a signal-to-noise ratio sequence is extracted according to the filtered satellite signal and a sequence counter is incremented by one.

[0119] Determine whether the sequence counter is greater than a set period to obtain a second determination result; if the second determination result is yes, output the extracted signal-to-noise ratio sequence; if the second determination result is no, return to the step of "extracting the signal-to-noise ratio sequence according to the screened satellite signal and increasing the sequence counter by one".

[0120] like Figure 3 As shown, first calculate the mean value of the interference signal; then from the very beginning t 0 Start judging whether the current interference signal is greater than the mean value; if not, move back to t 1 At the moment, return to the previous step and re-judge; if so, start SNR sequence extraction, add 1 to the sequence counter, and start from the first mean value point, take a period T, that is, a single period. Determine whether the sequence counter is greater than the period T. If so, output the extracted SNR sequence; if not, return to the previous step to continue extraction.

[0121] Step 104: Performing empirical mode decomposition on the signal-to-noise ratio sequence to obtain multiple modal components.

[0122] Step 104 specifically includes:

[0123] The remaining sequence components are initialized according to the signal-to-noise ratio sequence; and the timing is initialized according to the remaining sequence components.

[0124] The envelope of the extreme point and the envelope average are determined according to the initialized timing.

[0125] The initialization timing is updated according to the envelope average value.

[0126] Decomposed modal components of the time series are determined based on the updated time series.

[0127] A plurality of modal components are determined based on the initialized residual sequence components and the decomposed modal components of the time series.

[0128] In the present invention, EMD is a method for decomposing a signal, which decomposes the signal into the superposition of independent components; and the decomposition completely abandons the constraints of basis functions and decomposes the signal only according to the time scale characteristics of the data itself, especially in the decomposition of nonlinear and non-stationary signals. The reflected signal SNR sequence is just such a nonlinear sequence.

[0129] After extracting the SNR sequence, empirical mode decomposition (EMD) signal processing is performed. This method decomposes the time series into several intrinsic mode components (IMFs) based on the time scale of the local characteristics of the time series. Assume r i (t) is the residual time series component, hi (t) is the i-th decomposed modal component of the time series. The specific steps of EMD of SNR sequence x(t) are as follows: Figure 4 As shown:

[0130] 1) Initialize r 0 (t) = x(t), i = 1. 0 (t) is the original signal-to-noise ratio sequence.

[0131] 2) Let h j (t) = r i (t).h j (t) is the jth decomposed modal component of the sequence.

[0132] 3) Use cubic spline to fit the envelope e of the upper extreme point of the time series h(t) max (t) and the envelope of the lower extreme point e min (t), and the average value m(t) of the upper and lower extreme envelopes.

[0133] 4) In the timing h j (t) minus the mean value m j (t) Get the new time series h j+1 (t), and determine the new time series h j+1 (t) Whether the conditions of the intrinsic modal component are met, if so, let h i (t) = h j+1 (t), otherwise let h j (t) = h j+1 (t) and repeat steps 3) and 4).

[0134] 5) From the time series r i (t) minus the i-th decomposed modal component h i (t) to obtain the residual component r i+1 (t), judge r i+1 Are there more than two extreme values ​​in (t)? If so, repeat steps 2) to 5), otherwise, end and complete the EMD decomposition.

[0135] Finally, we conclude:

[0136] Step 105: perform effective spectrum estimation on each modal component to obtain the main oscillation frequency of each modal component.

[0137] Step 105 specifically includes:

[0138] LS spectrum estimation is performed on each modal component to obtain characteristic observation quantities; the characteristic observation quantities include peak power, spectrum width and primary-secondary peak ratio of the spectrum.

[0139] The characteristic observation quantity is extracted by using a threshold method to obtain an effective spectrum.

[0140] The main oscillation frequency of each modal component is determined according to the effective spectrum.

[0141] Step 106: Perform height inversion using the main oscillation frequencies of each modal component to obtain the sea surface height.

[0142] Step 106 specifically includes:

[0143] The height of the Android smartphone from the sea surface is determined according to the main oscillation frequencies of the modal components.

[0144] The sea level is determined according to the height of the Android smartphone from the sea level.

[0145] In order to obtain the main oscillation frequency of each modal component (IMF component), the steps are as follows: Figure 5 As shown:

[0146] First, perform LS spectrum estimation on each modal component:.

[0147]

[0148] Where: x k and t k are the kth sampling value and sampling point time respectively, and σ 2 are the sample mean and variance respectively; τ is the time translation invariant, which ensures that the estimated power spectrum is a uniformly sampled spectrum.

[0149] Then, according to the spectrum estimation image of each modal component, three characteristic observations of the spectrum, namely the peak frequency, spectrum width and primary-secondary peak ratio, are extracted.

[0150] The threshold method is used to perform quality control on the spectrum estimation results to extract the effective spectrum, where the spectrum width is less than the preset spectrum width T w , the primary-secondary peak ratio is greater than the preset ratio T R , and the peak frequency is in the interval [T fmin ,T fmax ], T fmin is the minimum peak frequency, T fmax is the maximum value of the peak frequency. w represents the spectrum width threshold, T R The threshold value of the primary-secondary peak ratio, T fmin represents the peak frequency minimum threshold and T fmaxrepresents the maximum value threshold of the peak frequency, which is preset to 1.0, 1.5, 0.66 and 1.14 respectively. If the above conditions are met, the modal component traversal is exited, and the sea surface height is obtained by using the height of the Android smartphone from the sea surface and the sea surface height expression. If not met, the next modal component is traversed until all components are traversed.

[0151] The expression of the height of the Android smartphone from the sea surface is:

[0152] h r =f peak λ / 2

[0153] Among them, h r is the height of the Android smartphone from the sea surface, f peak is the main oscillation frequency of each modal component, and λ is the signal wavelength.

[0154] The expression of the sea level height is:

[0155] h ssh =H r -h r

[0156] Among them, h ssh is the sea level height, H r is the height of the smartphone from the geodetic reference plane, h r The height of the Android smartphone from the sea surface.

[0157] The purpose of the present invention is to use smartphone GNSS data to carry out research on sea level height measurement, and to use measured data to evaluate height measurement performance. The GNSS antenna of a smartphone is a linearly polarized omnidirectional antenna that can effectively receive multipath signals reflected by all surrounding environments. To obtain an SNR sequence that meets the azimuth restriction condition, first perform EMD processing to obtain each intrinsic mode function component, and traverse each mode component. In order to obtain the main oscillation frequency of each modal component, first perform LS spectrum estimation on each modal component, and then extract the three characteristic observations of the peak frequency, spectrum width, and primary-secondary peak ratio of the spectrum. Afterwards, the threshold method is used to perform quality control on the spectrum estimation result and extract the effective spectrum, where the spectrum width is less than the preset T w , the primary-secondary peak ratio is greater than T R , and the peak frequency is in the interval [T fmin ,T fmax ]. If all the above conditions are met, the modal component traversal is exited, and the sea surface height is obtained using the height of the Android smartphone from the sea surface and the sea surface height expression. If not, the next modal component is traversed until all components are traversed.

[0158] The present invention can realize sea surface height measurement by an Android smartphone; effectively remove the burr effect caused by the difference in anti-multipath signal of the linear polarization of the Android smartphone antenna; and effectively suppress the multipath signal interference in the non-detection area on the test sea surface.

[0159] Figure 6 The system structure diagram of the sea surface height measurement method based on Android smartphone is as follows: Figure 6 As shown, the system includes:

[0160] Satellite screening module, SNR sequence extraction module, EMD processing module, and effective spectrum estimation module. First, use the mobile phone App: Geo++RINEX Logger to collect data, compare the self-made APP with the ephemeris file published on the GPS official website, calculate the altitude angle and azimuth angle of the corresponding satellite at the time of data collection, and select a reasonable satellite; then, extract the SNR sequence through the APP to obtain the SNR sequence that meets the azimuth restriction conditions, complete the conversion of the signal-to-noise ratio sequence from dB to W, and smooth the satellite azimuth; then, perform EMD processing in the APP to obtain the components of each intrinsic mode function, and traverse each mode component, perform LS spectrum estimation on each mode component, and then extract the three characteristic observations of the spectrum peak frequency, spectrum width and main-secondary peak ratio, and use the threshold method to perform quality control on the spectrum estimation results to extract the effective spectrum, that is, the main oscillation frequency of each mode; finally, perform height inversion based on the main oscillation frequency. Except for the collection process, other data processing processes are completed by the self-made APP.

[0161] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0162] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A sea surface height measurement method based on Android smartphone, It is characterized in that include: Get navigation signals from Android smartphones; Perform satellite screening according to the navigation signal to obtain screened satellite signals; Extracting a signal-to-noise ratio sequence from the screened satellite signal to obtain a signal-to-noise ratio sequence; extracting a signal-to-noise ratio sequence from the screened satellite signal to obtain a signal-to-noise ratio sequence specifically includes: determining an interference signal mean value according to the screened satellite signal; judging whether a signal-to-noise ratio sequence value of the screened satellite signal at a current moment is greater than the interference signal mean value to obtain a first judgment result; if the first judgment result is yes, taking a signal-to-noise ratio sequence value at a next moment as a signal-to-noise ratio sequence value at a current moment, and returning to the step of "judging whether a signal-to-noise ratio sequence value of the screened satellite signal at a current moment is greater than the interference signal mean value to obtain a first judgment result"; if the first judgment result is no, extracting a signal-to-noise ratio sequence from the screened satellite signal and incrementing a sequence counter by one; judging whether the sequence counter is greater than a set period to obtain a second judgment result; if the second judgment result is yes, outputting the extracted signal-to-noise ratio sequence; if the second judgment result is no, returning to the step of "extracting a signal-to-noise ratio sequence from the screened satellite signal and incrementing a sequence counter by one"; Performing empirical mode decomposition on the signal-to-noise ratio sequence to obtain multiple modal components; Perform effective spectrum estimation on each modal component to obtain the main oscillation frequency of each modal component; The main oscillation frequencies of the modal components are used to perform height inversion to obtain the sea surface height.

2. The sea level height measurement method based on Android smart phone according to claim 1, It is characterized in that The performing satellite screening according to the navigation signal to obtain the screened satellite signal specifically includes: Get GPS standard ephemeris file; The navigation signal is compared with the ephemeris file to obtain a filtered satellite signal.

3. The sea level height measurement method based on Android smart phone according to claim 1, It is characterized in that The performing of empirical mode decomposition on the signal-to-noise ratio sequence to obtain multiple modal components specifically includes: Initializing the remaining sequence components according to the signal-to-noise ratio sequence; initializing the time sequence according to the remaining sequence components; Determine the envelope of the extreme point and the average value of the envelope according to the initialized timing; Updating the initialization timing according to the envelope average value; determining decomposed modal components of the time series according to the updated time series; A plurality of modal components are determined based on the initialized residual sequence components and the decomposed modal components of the time series.

4. The sea level height measurement method based on Android smart phone according to claim 1, It is characterized in that The effective spectrum estimation of each modal component is performed to obtain the main oscillation frequency of each modal component, specifically including: Perform LS spectrum estimation on each modal component to obtain characteristic observations; the characteristic observations include peak power, spectrum width and primary-secondary peak ratio of the spectrum; Extracting the characteristic observation quantity by using a threshold method to obtain an effective spectrum; The main oscillation frequency of each modal component is determined according to the effective spectrum.

5. The sea level height measurement method based on Android smart phone according to claim 1, It is characterized in that The method of performing height inversion using the main oscillation frequencies of the modal components to obtain the sea surface height specifically includes: Determine the height of the Android smartphone from the sea surface according to the main oscillation frequencies of each modal component; The sea level is determined according to the height of the Android smartphone from the sea level.

6. The sea level height measurement method based on an Android smartphone according to claim 5, It is characterized in that The expression of the height of the Android smartphone from the sea surface is: h r =f peak ·l / 2 Among them, h r is the height of the Android smartphone from the sea surface, f peak is the main oscillation frequency of each modal component, and λ is the signal wavelength.

7. The sea level height measurement method based on an Android smartphone according to claim 1, It is characterized in that The expression of the sea level height is: h ssh =H r -h r Among them, h ssh is the sea level height, H r is the height of the smartphone from the geodetic reference plane, h r The height of the Android smartphone from the sea surface.

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