A method, device and system for positioning and monitoring water equipment based on GNSS

Abstract

The present invention relates to a method, device and system for positioning and monitoring water equipment based on GNSS. The method for positioning and monitoring water equipment based on GNSS eliminates errors in observation values ​​caused by natural signal interference by establishing a mixed altitude angle and signal-to-noise ratio random model that takes both altitude angle and signal-to-noise ratio into consideration, and eliminates multipath errors in observation values ​​caused by water surface reflection by establishing a multipath error correction model. At the same time, a GNSS long baseline solution algorithm that takes both multipath errors into consideration is adopted. In the long baseline solution process, natural signal interference and the influence of water surface multipath errors are further eliminated through error correction, thereby ensuring high-precision and stable solution in a large area of ​​water, and solving the problem of inaccurate detection results due to water surface fluctuations and multipath signal interference in the existing position monitoring process of water equipment.

Description

Technical Field

[0001] The present invention relates to the field of waterborne equipment monitoring, and in particular to a method, device and system for positioning and monitoring waterborne equipment based on GNSS. Background Art

[0002] The Global Navigation Satellite System (GNSS) has been widely used in water conservancy projects such as deformation monitoring, water conservancy inspection, and hydrological monitoring data transmission. However, there is a lack of effective displacement monitoring methods for monitoring water equipment such as photovoltaic platforms, floating barriers, floating monitoring equipment, and pontoons. When floating equipment is disconnected, it will have a serious impact on downstream ships, bridges, and dams.

[0003] When monitoring the displacement of water equipment based on GNSS, there are the following interference problems:

[0004] The water surface fluctuations will affect the water equipment, which will move irregularly up, down, left, and right with the water surface fluctuations. The monitoring information will change at any time, affecting the monitoring results.

[0005] Human interference: electromagnetic radiation from the equipment itself, radio transmitters, radar equipment, etc. can interfere with GNSS signals;

[0006] Natural interference, atmospheric shielding, ionospheric disturbances, multipath propagation and other factors can also cause GNSS signal attenuation and distortion;

[0007] Malicious interference, which involves intentionally sending false or spoofed signals to impair the positioning performance of a GNSS receiver or cause erroneous positioning results.

[0008] For water equipment, since its displacement changes with the fluctuation of the water surface, when GNSS monitors water equipment, the satellite signal is more significantly affected by the water surface fluctuation during the transmission process than other water conservancy monitoring. As a result, the existing GNSS-based monitoring and positioning methods have problems with reduced positioning accuracy or even failure when applied to water equipment. Summary of the invention

[0009] Based on this, the object of the present invention is to provide a method for positioning and monitoring water equipment based on GNSS.

[0010] A method for positioning and monitoring waterborne equipment based on GNSS, comprising the following steps:

[0011] S10 obtains original observation data of each satellite received by a plurality of GNSS receivers installed in different water equipment;

[0012] S20 performs standardization processing on the original observation data to obtain preprocessed observation data;

[0013] S30: performing ionospheric correction, multipath correction and observation value weight assignment on the pre-processed observation data to obtain narrow lane purified observation data; performing double difference narrow lane observation value solution on the narrow lane purified observation data to obtain narrow lane floating point ambiguity and floating point coordinate information;

[0014] S40 performs double-difference wide-lane observation value resolution and ambiguity fixation on the pre-processed observation data to obtain wide-lane fixed ambiguity;

[0015] S50 uses the constrained wide lane fixed ambiguity to constrain and fix the narrow lane floating point ambiguity to obtain the narrow lane fixed ambiguity; updates the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the coordinate information of the GNSS receiver.

[0016] The GNSS-based aquatic equipment positioning and monitoring method described in the present invention eliminates the error influence of natural signal interference on the observation value to a certain extent by establishing a mixed altitude angle and signal-to-noise ratio random model that takes into account the altitude angle and the signal-to-noise ratio, and eliminates the multipath error in the observation value caused by water surface reflection to a certain extent by establishing a multipath error correction model. At the same time, a GNSS long baseline filtering solution algorithm that takes into account the multipath error is adopted. In the long baseline solution process, the natural signal interference and the influence of water surface multipath error are further eliminated through error correction, thereby ensuring high-precision and stable solution in large areas of water, and solving the problem of inaccurate detection results due to water surface fluctuations and multipath signal interference in the existing position monitoring process of aquatic equipment.

[0017] Furthermore, the step S30 includes the following sub-steps:

[0018] S31A uses the ionospheric random walk model and the ionospheric correction parameters of the previous epoch to perform noise superposition on the preprocessed ionospheric information of the current epoch;

[0019] S31B uses the multipath error correction model to perform multipath correction on the preprocessed current epoch observations;

[0020] S31C uses a mixed altitude angle and signal-to-noise ratio stochastic model to assign weights to the preprocessed current epoch observations;

[0021] S32 uses the double-difference narrow-lane observation equation based on carrier phase to filter and solve the narrow-lane purified observation data that has been ionosphere corrected, multipath corrected, and weighted, and obtains the narrow-lane floating-point ambiguity and floating-point coordinate information.

[0022] Furthermore, the step S31C of assigning weights to the preprocessed current epoch observations includes the following steps:

[0023] SD1 is based on the signal-to-noise ratio value of the frequency point corresponding to each altitude angle of the current satellite Fitted SNR value of the altitude angle corresponding to the random model of mixed altitude angle and SNR Calculating the expansion factor , the expansion factor satisfy:

[0024]

[0025]

[0026] Where: is the signal-to-noise ratio value actually observed at the current moment, is the signal-to-noise ratio value fitted according to the altitude angle-signal-to-noise ratio fitting model, and represents the empirical constant, Indicates the altitude angle The maximum signal-to-noise ratio value of the corresponding frequency point, Indicates the altitude angle The minimum signal-to-noise ratio value of the corresponding frequency point, Indicates the altitude angle Standard deviation of the signal-to-noise ratio value at the corresponding frequency point;

[0027] SD2 is based on the expansion factor , correct the covariance R matrix of the estimated observation accuracy;

[0028] SD3 weights the preprocessed current epoch observations based on the modified covariance R matrix to determine the weight of each observation.

[0029] Furthermore, the mixed altitude angle and signal-to-noise ratio random model is established in the following manner:

[0030] SC1 sets up a group of GNSS stations in an open environment to form a base station network;

[0031] SC2 obtains the satellite observation information of the reference station network in the set time period, and the satellite observation information meets the following conditions: data efficiency>90%, cycle slip ratio>400, and multipath<1.0m;

[0032] SC3 calculates the maximum signal-to-noise ratio and minimum signal-to-noise ratio of each frequency point of each satellite at different sites;

[0033] SC4 calculates the normalized signal-to-noise ratio value of each satellite frequency at different sites, sorts the normalized signal-to-noise ratio values ​​at intervals of 1° altitude, and calculates the average normalized signal-to-noise ratio value at each altitude. and its standard deviation ;

[0034] SC5 is based on the average value of the normalized signal-to-noise ratio at each altitude angle and its standard deviation A cubic polynomial fitting coefficient function is established to solve the coefficients of the cubic polynomial. Based on the solved coefficients, a mixed altitude angle and signal-to-noise ratio random model is established, and the fitting signal-to-noise ratio values ​​at all altitude angles are obtained. :

[0035] Among them, the cubic polynomial fitting function based on the normalized signal-to-noise ratio average satisfies:

[0036] ;

[0037] The cubic polynomial fitting coefficient function based on standard deviation satisfies:

[0038] ;

[0039] In the formula, represents the altitude angle, represents the model parameters fitted based on the average value of the normalized signal-to-noise ratio, represents the model parameters based on the standard deviation fit.

[0040] Furthermore, the multipath error correction model is established by the following method;

[0041] SA1 preprocesses the original observation data;

[0042] SA2 calculates single-difference observations between stations;

[0043] SA3 calculates double difference observation value sequences of satellite pairs formed by the first satellite observed and the second satellite to the nth satellite respectively according to the single difference observation values; and calculates double difference observation value residual sequences according to the double difference observation value sequence and the double difference observation values ​​calculated by the observation value model;

[0044] SA4 maps the double difference observation residual sequence into a corresponding single difference observation residual sequence according to a certain mapping relationship and stores it in a single difference observation residual sequence array; taking GNSS receiver A and GNSS receiver B as an example, the mapping relationship satisfies:

[0045]

[0046] Among them, based on the characteristic that the weighted average of the single difference residual is zero , represents the weighted function value of the altitude angle of the nth satellite, It represents the single difference observation residual of GNSS receiver A and GNSS receiver B for the nth satellite. It represents the double difference observation residual of GNSS receiver A and GNSS receiver B for the satellite pair consisting of the 1st satellite and the nth satellite, i represents the satellite number, A represents receiver A, and B represents receiver B;

[0047] SA5 monitors the data volume N of the single-difference observation residual sequence array. When the data volume N is greater than or equal to the data volume threshold, the multipath error model is used to perform low-pass filtering on all single-difference observation residuals in the array to obtain multipath error correction values ​​and store them in the multipath error correction value database.

[0048] Furthermore, the double-difference narrow lane observation equation based on carrier phase in step S32 is:

[0049] ;

[0050] In the formula, is the wavelength of narrow-lane observations, and are the signal frequencies of two different frequencies, where i and j represent the satellite numbers, b and r represent the base station and monitoring station numbers, is the narrow-lane double-difference carrier phase observation value, are the direction cosines of the location parameters, is a positional parameter, is the altitude angle The mapping function of is the double-difference zenith tropospheric delay, and are the ionospheric delay errors of satellites i and j, is the double-difference narrow lane ambiguity, represents the multipath error, represents the narrow-lane phase residual.

[0051] Furthermore, the step S50 includes the following sub-steps:

[0052] S51 constrains the wide lane fixed ambiguity to obtain a constrained wide lane fixed ambiguity, and constrains the narrow lane floating ambiguity based on the constrained wide lane fixed ambiguity to obtain a constrained narrow lane floating ambiguity; wherein the constraint condition adopted by the wide lane fixed ambiguity is:

[0053]

[0054] Where: represents the estimated integer ambiguity, Represents widelane floating point ambiguity;

[0055] S52 searches and fixes the constrained narrow lane floating point ambiguity to obtain the narrow lane fixed ambiguity;

[0056] S53 updates the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the position coordinate information of the receiver.

[0057] At the same time, the present invention provides a GNSS-based water equipment positioning and monitoring device, which is characterized by comprising:

[0058] A data acquisition module is used to acquire original observation data of each satellite received by a number of GNSS receivers installed on different water equipment;

[0059] A data preprocessing module is used to perform standardization processing on the original observation data to obtain preprocessed observation data;

[0060] A narrow lane floating point ambiguity resolution module is used to perform ionosphere correction, multipath correction and observation value weight assignment on the pre-processed observation data to obtain narrow lane purified observation data; perform double difference narrow lane observation value resolution on the narrow lane purified observation data to obtain narrow lane floating point ambiguity and floating point coordinate information;

[0061] A wide lane fixed ambiguity resolution module is used to perform double-difference wide lane observation value resolution and ambiguity fixation on the pre-processed observation data to obtain wide lane fixed ambiguity;

[0062] The position solving module is used to constrain and fix the narrow lane floating point ambiguity by using the constrained wide lane fixed ambiguity to obtain the narrow lane fixed ambiguity; update the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the coordinate information of the GNSS receiver.

[0063] Compared with the prior art, the beneficial effects of the GNSS-based aquatic equipment positioning and monitoring device of the present invention are the same as those of the GNSS-based aquatic equipment positioning and monitoring method, which will not be described in detail here.

[0064] At the same time, the present invention provides a GNSS-based water equipment positioning and monitoring system, comprising: a plurality of GNSS receivers arranged on different water equipment, a GNSS-based water equipment positioning and monitoring device for implementing any of the above-mentioned GNSS-based water equipment positioning and monitoring methods, a posture control module, a communication module and a power supply module;

[0065] The GNSS receiver is used to receive raw observation data from different satellites;

[0066] The attitude control module determines whether the position of the water equipment where the GNSS receiver is installed is within a preset safety area according to the position information output by the water equipment positioning monitoring device based on GNSS, and performs corresponding operations on the water equipment to ensure the safety of the equipment;

[0067] The communication module is used for data transmission and communication with external device ad hoc network and wide area network;

[0068] The power module supplies power to the GNSS receiver, the GNSS-based aquatic equipment positioning and monitoring device, the attitude control module, and the communication module.

[0069] Compared with the prior art, the beneficial effects of the GNSS-based aquatic equipment positioning and monitoring system of the present invention are the same as those of the GNSS-based aquatic equipment positioning and monitoring method, which will not be described in detail here.

[0070] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a schematic diagram of a GNSS-based positioning and monitoring device for marine equipment;

[0072] Figure 2 The flowchart of the GNSS-based positioning and monitoring method for water equipment. DETAILED DESCRIPTION

[0073] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention.

[0074] In order to solve the technical problem that when GNSS is used to monitor water equipment, the influence of water surface fluctuations on satellite signals during transmission is more significant than that of other water conservancy monitoring, resulting in a decrease in the accuracy of positioning and monitoring of water equipment or even failure, the present invention proposes a GNSS-based water equipment positioning and monitoring method. The positioning and monitoring method is based on a long baseline solution method. By constructing a mixed altitude angle and signal-to-noise ratio random model, when long baseline solution is used to solve satellite observation values, the satellite observation values ​​are more reasonably weighted, so that the altitude angle observation values ​​with poor signals obtain large filtering weights, and multipath errors are taken into account in the long baseline solution process, so that the long baseline solution is more accurate when performing subsequent filtering, floating point position, and ambiguity calculation, thereby eliminating the influence of water surface fluctuations on the positioning and monitoring of water equipment to a certain extent, and improving the positioning and monitoring accuracy of water equipment.

[0075] Based on the above-mentioned GNSS-based aquatic equipment positioning and monitoring method, a GNSS-based aquatic equipment positioning and monitoring device and a monitoring system are proposed.

[0076] The GNSS-based water equipment positioning and monitoring system of the present invention comprises: a plurality of GNSS receivers arranged on different water equipment, a GNSS-based water equipment positioning and monitoring device, a posture control module, a communication module and a power supply module.

[0077] The GNSS receiver is used to receive original observation data from different satellites.

[0078] The original observation data includes but is not limited to carrier phase, pseudorange, signal-to-noise ratio, satellite orbit parameters, clock error, etc.

[0079] The GNSS receiver is a dual-frequency receiver, and the distance between each GNSS receiver is several kilometers to tens of kilometers, so as to use the long baseline solution algorithm. The GNSS-based water equipment positioning and monitoring device is used to process the observation data and ephemeris data of different satellites received by different GNSS receivers, eliminate multipath errors, and obtain high-precision position information of each GNSS receiver.

[0080] The attitude control module determines whether the position of the water equipment where the GNSS receiver is installed is within a preset safety area according to the position information output by the GNSS-based water equipment positioning monitoring device, and performs corresponding operations on the water equipment to ensure the safety of the equipment.

[0081] The communication module is used for data transmission and communication with external device ad hoc network and wide area network.

[0082] The power module is integrated on the water equipment to power the GNSS receiver, the GNSS-based water equipment positioning monitoring device, the attitude control module, and the communication module. The power module can be a battery energy storage device that uses natural resources such as water flow, wind power, and solar energy to convert energy and provide a stable power supply for the equipment.

[0083] Among them, see Figure 1 and Figure 2 The GNSS-based water equipment positioning and monitoring device includes a data acquisition module 10, a data preprocessing module 20, a narrow lane floating point ambiguity resolution module 30, a wide lane fixed ambiguity resolution module 40 and a position resolution module 50.

[0084] The data acquisition module 10 is used to execute step S10 to acquire original observation data of each satellite received by a plurality of GNSS receivers arranged on different water-based equipment.

[0085] The original observation data includes but is not limited to original observation values, ephemeris information, ionosphere information, UTC information, station information, etc. The original observation values ​​include but are not limited to original L1 carrier phase observation values, original L2 carrier phase observation values, pseudorange observation values, etc.

[0086] The data preprocessing module 20 is used to execute step S20 to perform standardization processing on the original observation data to obtain preprocessed observation data.

[0087] The standardization process includes the estimation of the initial integer ambiguity of the original observation data and the detection and repair of cycle slips.

[0088] The narrow lane floating point ambiguity resolution module 30 is used to execute step S30, perform ionospheric correction, multipath correction and observation value weight assignment on the pre-processed observation data to obtain narrow lane purified observation data; perform double difference narrow lane observation value resolution on the narrow lane purified observation data to obtain narrow lane floating point ambiguity and floating point coordinate information.

[0089] The narrow lane floating point ambiguity resolution module 30 includes a narrow lane data purification unit 31 and a double difference narrow lane Kalman filter 32. The narrow lane data purification unit 31 includes an ionospheric random walk model 311, a multipath error correction model 312, and a mixed altitude angle and signal-to-noise ratio random model 313.

[0090] The narrow lane data purification unit 31 is used to execute step S31 to perform ionospheric correction, multipath correction and observation value weight assignment on the pre-processed observation data to obtain narrow lane purified observation data.

[0091] The ionospheric random walk model 311 is used to execute step S31A, using the ionospheric correction of the previous epoch to perform noise superposition on the pre-processed ionospheric information of the current epoch.

[0092] During specific implementation, the ionospheric random walk model may adopt an existing conventional model, and this application does not impose any limitation thereto.

[0093] The multipath error correction model 312 is used to execute step S31B to perform multipath correction on the preprocessed current epoch observation value file.

[0094] In specific implementation, the multipath error correction model is determined by the following method, taking GNSS receiver A and GNSS receiver B as examples.

[0095] SA1 Preprocessing the original observation data: Preprocessing the original observation data of each satellite received by GNSS receiver A and GNSS receiver B at the same time, the preprocessing includes: 1) detecting data integrity and quality, removing outliers and erroneous data; 2) performing cycle slip detection and repair to ensure the continuity of carrier phase data.

[0096] SA2 Calculate the single difference observation value between stations: Calculate the single difference observation value between stations of GNSS receiver A and GNSS receiver B for each satellite at the same time ;in, satisfy , i represents the i-th satellite, represents the carrier phase observation value of the i-th satellite obtained by GNSS receiver A at the same time, It represents the carrier phase observation value of the i-th satellite obtained by GNSS receiver B at the same time, i∈(1,n), and n is the total number of observable satellites.

[0097] SA3 Calculate double difference observations and double difference observation residuals: Calculate the double difference observation sequence of the satellite pairs formed by the first satellite observed and the second to nth satellites according to the single difference observations obtained in step SA2. ; According to the double difference observation sequence , double difference observations calculated by the observation model , and obtain the double difference observation residual sequence .

[0098] Among them, the double difference observation sequence Expressed as ,and ; Double difference observation residual sequence Expressed as ,and ; Where 1i represents the satellite pair formed by the observed first satellite and the i-th satellite.

[0099] SA4 Obtaining a single-difference observation residual sequence: mapping the double-difference observation residual sequence into a corresponding single-difference observation residual sequence according to a certain mapping relationship and storing the sequence in a single-difference observation residual sequence array.

[0100] The mapping relationship satisfies:

[0101]

[0102] Among them, based on the characteristic that the weighted average of the single difference residual is zero , represents the weighted function value of the altitude angle of the nth satellite, It represents the single difference observation residual of GNSS receiver A and GNSS receiver B for the nth satellite. It represents the double difference observation residual of GNSS receiver A and GNSS receiver B for the satellite pair consisting of the 1st satellite and the nth satellite, i represents the satellite number, A represents receiver A, and B represents receiver B.

[0103] SA5 monitors the data volume N of the single-difference observation residual sequence array. When the data volume N is greater than or equal to the data volume threshold, the multipath error model is used to perform low-pass filtering on all single-difference observation residuals in the array to obtain multipath error correction values ​​and store them in the multipath error correction value database.

[0104] The mixed altitude angle and signal-to-noise ratio random model 313 is used to execute step S31C to assign weights to the preprocessed current epoch observation values.

[0105] During specific implementation, the mixed altitude angle and signal-to-noise ratio random model is established in the following manner.

[0106] SC1 A group of GNSS stations are set up in an open environment to form a base station network, and the base station network consists of 10 GNSS stations.

[0107] SC2 obtains satellite observation information of the reference station network in a set time period, where the satellite observation information satisfies: data efficiency>90%, cycle slip ratio>400, multipath<1.0m, and the set time period is 10 days.

[0108] Specifically, taking into account that different satellites have different return periods, and the MEO satellite return period of BDS is seven sidereal days, and the GAL return period is 10 sidereal days, 10 days of observation data can basically guarantee the capture of all theoretically observable satellite observation data at the corresponding altitude angle. In addition, the 10 days of GNSS observation data can also ensure the accuracy of the model data source.

[0109] SC3 calculates the maximum signal-to-noise ratio and minimum signal-to-noise ratio of each frequency point of each satellite at different sites.

[0110] Specifically, the maximum signal-to-noise ratio value and the minimum signal-to-noise ratio value are determined according to the median of the maximum value array and the minimum value array in the observation data of each day within the set time period in step SC2.

[0111] For a base station network with n observable satellites, m integrated frequency points, and 10 GNSS stations, the measured signal-to-noise ratio array is: , where the signal-to-noise ratio array of the pth frequency point of the ith satellite measured by the kth station is The signal-to-noise ratio observation data covers 10 days, where k∈(1,10), i∈(1,n), p∈(1,m), ; Determine the signal-to-noise ratio array The maximum signal-to-noise ratio value per day , minimum signal-to-noise ratio ; Determine the median of the 10 largest signal-to-noise ratio values ​​and the median of the 10 smallest signal-to-noise ratio values ​​in the signal-to-noise ratio array; and then determine the maximum signal-to-noise ratio value of the signal-to-noise ratio array and minimum signal-to-noise ratio .

[0112] SC4 calculates the normalized signal-to-noise ratio (SNR) of each frequency point of each satellite at different sites, sorts and classifies the SNR values ​​at intervals of 1° elevation angle, and obtains the average SNR value and its standard deviation at each elevation angle.

[0113] Specifically, the normalized signal-to-noise ratio value satisfy:

[0114]

[0115] in, represents a certain signal-to-noise ratio value of the p-th frequency point of the ith satellite measured by the k-th station, k∈(1,10N), N represents the total signal-to-noise ratio of the signal-to-noise ratio array measured in one day, It represents the minimum signal-to-noise ratio value of the p-th frequency point of the ith satellite measured by the k-th station, It represents the maximum signal-to-noise ratio of the p-th frequency point of the ith satellite measured by the k-th station.

[0116] SC5 establishes a cubic polynomial fitting coefficient function based on the normalized signal-to-noise ratio average value and its standard deviation at each altitude angle, solves the coefficients of the cubic polynomial, and establishes a mixed altitude angle and signal-to-noise ratio random model based on the solved coefficients, and obtains the fitting signal-to-noise ratio values ​​at all altitude angles ,in:

[0117] The cubic polynomial fitting function based on the normalized signal-to-noise ratio average satisfies:

[0118] ;

[0119] The cubic polynomial fitting coefficient function based on standard deviation satisfies:

[0120] ;

[0121] In the formula, represents the altitude angle, represents the model fitting parameters based on the average of the normalized signal-to-noise ratio, represents the model fitting parameters based on standard deviations.

[0122] Furthermore, based on the above-mentioned mixed altitude angle and signal-to-noise ratio random model, the following method is used to reasonably weight the observation values, that is, to assign weights to the observation values ​​of the current epoch.

[0123] SD1 is based on the signal-to-noise ratio value of the frequency point corresponding to each altitude angle of the current satellite Fitted SNR value of the altitude angle corresponding to the random model of mixed altitude angle and SNR , calculate the expansion factor .

[0124] The expansion factor satisfies :

[0125]

[0126]

[0127] Where: is the signal-to-noise ratio value actually observed at the current moment, is the signal-to-noise ratio value fitted according to the altitude angle-signal-to-noise ratio fitting model, and represents the empirical constant, The recommended value is 3mm. The recommended value is 4mm.

[0128] SD2 is the dilation factor based on step SD1 , the covariance R matrix of the estimated observation accuracy is modified.

[0129] A more accurate covariance R matrix can assign high precision to good observations and low precision to bad observations, further improving positioning accuracy and convergence time.

[0130] SD3 weights the preprocessed current epoch observations based on the modified covariance R matrix to determine the weight of each observation.

[0131] The low-altitude satellite observations contain large atmospheric delays and multipath effect errors. The observations of low-altitude satellites can enhance the strength of the geometric figures of satellite distribution, and the elevation angle is set to weight the observations. At the same time, GNSS signals are extremely susceptible to interference, and the system errors of satellites with higher elevation angles may be larger than those of low-altitude satellites. Since the signal-to-noise ratio is the energy density ratio of the carrier signal to the noise, when the signal is diffracted or interfered by other natural factors, its signal-to-noise ratio is generally smaller than the corresponding value at the same elevation angle. Therefore, the influence is reflected in the GNSS observations by setting the signal-to-noise ratio value. Based on the above analysis, the present application pre-establishes the relationship between the elevation angle and the signal-to-noise ratio for a fixed receiver antenna combination in an open environment, and based on the signal-to-noise ratio model of the board card, polynomial fitting modeling is performed on the signal-to-noise ratio of each frequency point at the corresponding elevation angle to obtain a mixed elevation angle and signal-to-noise ratio random model. The mixed elevation angle and signal-to-noise ratio random model adjusts the observation value weights for other natural interference signals, eliminates the error influence of natural signal interference, and ensures its high-precision solution.

[0132] The double-difference narrow lane Kalman filter 32 is used to execute step S32, and filter and solve the narrow lane purified observation data using the double-difference narrow lane observation equation based on the carrier phase to obtain narrow lane floating point ambiguity and floating point coordinate information.

[0133] In specific implementation, the double-difference narrow lane observation equation based on carrier phase is expressed as:

[0134] ;

[0135] In the formula, is the wavelength of narrow-lane observations, and are the signal frequencies of two different frequencies, where i and j represent the satellite numbers, b and r represent the base station and monitoring station numbers, is the narrow-lane double-difference carrier phase observation value, are the direction cosines of the location parameters, is a positional parameter, is the altitude angle The mapping function of is the double-difference zenith tropospheric delay, and are the ionospheric delay errors of satellites i and j, is the double-difference narrow lane ambiguity, represents the multipath error, represents the narrow-lane phase residual.

[0136] During the solution process, the ionospheric delay error is iteratively updated based on the ionospheric random walk model, the weight of the observation value is iteratively updated based on the mixed altitude angle and signal-to-noise ratio random model, the observation value is corrected based on the multipath error correction model and the multipath error correction is iteratively updated.

[0137] The wide lane fixed ambiguity resolution module 40 is used to execute step S40 to perform double difference wide lane observation value resolution and ambiguity fixation on the pre-processed observation data to obtain wide lane fixed ambiguity.

[0138] The wide lane fixed ambiguity resolution module 40 includes a double-difference wide lane Kalman filter 41 and a wide lane LAMBDA search fixed unit 42 .

[0139] The double-difference wide-lane Kalman filter 41 is used to execute step S41, and filter and solve the wide-lane purified observation data using a double-difference wide-lane observation equation based on carrier phase to obtain wide-lane floating-point ambiguity.

[0140] In specific implementation, the double-difference wide-lane observation equation based on carrier phase is expressed as:

[0141] ;

[0142] In the formula, is the wavelength of wide-lane observations, and are two wavelengths of different frequencies, where i and j represent satellite numbers, b and r represent base station and monitoring station numbers, is the wide-lane double-difference carrier phase observation value, are the direction cosines of the location parameters, is a positional parameter, is the altitude angle The mapping function of is the double-difference zenith tropospheric delay, and are the ionospheric delay errors of satellites i and j, is the double-difference wide-lane ambiguity, represents the wide-lane phase residual.

[0143] The wide lane LAMBDA search fixing unit 42 is used to execute step S43 to screen and fix the wide lane floating point ambiguity to obtain the wide lane fixed ambiguity.

[0144] The position solving module 50 is used to execute step S50, use wide lane fixed ambiguity to constrain and fix narrow lane floating point ambiguity to obtain narrow lane fixed ambiguity; update floating point coordinate information according to the narrow lane fixed ambiguity to obtain coordinate information of the GNSS receiver.

[0145] The position solving module 50 includes a narrow lane floating point ambiguity constraint unit 51 , a narrow lane LAMBDA search fixing unit 52 and a position solving unit 53 .

[0146] The narrow lane floating ambiguity constraint unit 51 is used to execute step S51, constrain the wide lane fixed ambiguity to obtain the constrained wide lane fixed ambiguity, and constrain the narrow lane floating ambiguity based on the constrained wide lane fixed ambiguity to obtain the constrained narrow lane floating ambiguity; wherein the constraint condition adopted by the wide lane fixed ambiguity is:

[0147]

[0148] Where: represents the estimated integer ambiguity, Represents widelane floating point ambiguity.

[0149] The narrow lane LAMBDA search fixing unit 52 is used to execute step S52 to search and fix the constrained narrow lane floating point ambiguity to obtain the narrow lane fixed ambiguity.

[0150] The position solving unit 53 is used to execute step S53, update the floating point coordinate information according to the narrow lane fixed ambiguity, and obtain the position coordinate information of the receiver.

[0151] Furthermore, the position solution module also includes a narrow lane fixed ambiguity correctness verification module, which is used to verify the correctness of the narrow lane fixed ambiguity: for those that pass the verification, the narrow lane fixed ambiguity can be used for the calculation of step S53; for those that fail the verification, a partial ambiguity fixing strategy is adopted to retain the ambiguity that fails the verification as a floating point ambiguity.

[0152] Based on the same inventive concept, the present application also provides an electronic device, which may be a terminal device such as a server, a desktop computing device or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). The device includes one or more processors and a memory, wherein the processor is used to execute a program to implement the GNSS-based water equipment positioning and monitoring method of an embodiment of the present invention; and the memory is used to store a computer program that can be executed by the processor.

[0153] Based on the same inventive concept, the present application also provides a computer-readable storage medium, corresponding to the embodiment of the aforementioned GNSS-based water equipment positioning and monitoring method, and the computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the GNSS-based water equipment positioning and monitoring method recorded in any of the above-mentioned embodiments.

[0154] The present application may take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-usable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0155] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application and the claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that, unless otherwise specified, "multiple" and "several" refer to two or more; "and / or" refers to and includes any or all possible combinations of one or more associated listed items; "first", "second", "third", etc. are only used to distinguish, rather than to describe a specific order or sequence, and cannot be understood as indicating or implying relative importance. When the above description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of the present application, for those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0156] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, and the present invention is also intended to include these modifications and modifications.

Claims

1. A method for positioning and monitoring waterborne equipment based on GNSS, characterized in that: The following steps are involved: S10 obtains original observation data of each satellite received by a plurality of GNSS receivers installed in different water equipment; S20 performs standardization processing on the original observation data to obtain preprocessed observation data; S30 performs ionospheric correction, multipath correction and observation value weight assignment on the pre-processed observation data to obtain narrow lane purified observation data; performs double difference narrow lane observation value solution on the narrow lane purified observation data to obtain narrow lane floating point ambiguity and floating point coordinate information; wherein, performing observation value weight assignment on the pre-processed observation data comprises the following steps: SD1 is based on the signal-to-noise ratio value of the frequency point corresponding to each altitude angle of the current satellite Fitted SNR value of the altitude angle corresponding to the random model of mixed altitude angle and SNR Calculating the expansion factor , the expansion factor satisfy: Where: is the signal-to-noise ratio value actually observed at the current moment, is the signal-to-noise ratio value fitted according to the altitude angle-signal-to-noise ratio fitting model, and represents the empirical constant, Indicates the altitude angle The maximum signal-to-noise ratio value of the corresponding frequency point, Indicates the altitude angle The minimum signal-to-noise ratio value of the corresponding frequency point, Indicates the altitude angle Standard deviation of the signal-to-noise ratio value at the corresponding frequency point; SD2 is based on the expansion factor , correct the covariance R matrix of the estimated observation accuracy; SD3 weights the preprocessed current epoch observations based on the modified covariance R matrix to determine the weight of each observation; S40 performs double-difference wide-lane observation value resolution and ambiguity fixation on the pre-processed observation data to obtain wide-lane fixed ambiguity; S50 uses the constrained wide lane fixed ambiguity to constrain and fix the narrow lane floating point ambiguity to obtain the narrow lane fixed ambiguity; updates the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the coordinate information of the GNSS receiver.

2. The GNSS-based water equipment positioning and monitoring method according to claim 1, characterized in that: The step S30 includes the following sub-steps: S31A uses the ionospheric random walk model and the ionospheric correction parameters of the previous epoch to perform noise superposition on the preprocessed ionospheric information of the current epoch; S31B uses the multipath error correction model to perform multipath correction on the preprocessed current epoch observations; S31C uses a mixed altitude angle and signal-to-noise ratio stochastic model to assign weights to the preprocessed current epoch observations; S32 uses the double-difference narrow-lane observation equation based on carrier phase to filter and solve the narrow-lane purified observation data that has been ionosphere corrected, multipath corrected, and weighted, and obtains the narrow-lane floating-point ambiguity and floating-point coordinate information.

3. The method for positioning and monitoring water equipment based on GNSS according to claim 2, characterized in that: The mixed altitude angle and signal-to-noise ratio random model is established in the following way: SC1 sets up a group of GNSS stations in an open environment to form a base station network; SC2 obtains the satellite observation information of the reference station network in the set time period, and the satellite observation information meets the following conditions: data efficiency>90%, cycle slip ratio>400, and multipath<1.0m; SC3 calculates the maximum signal-to-noise ratio and minimum signal-to-noise ratio of each frequency point of each satellite at different sites; SC4 calculates the normalized signal-to-noise ratio value of each frequency point of each satellite at different sites, and sorts the normalized signal-to-noise ratio values ​​at intervals of 1° altitude, and calculates the average normalized signal-to-noise ratio value at each altitude angle. and its standard deviation ; SC5 is based on the average value of the normalized signal-to-noise ratio at each altitude angle and its standard deviation A cubic polynomial fitting coefficient function is established to solve the coefficients of the cubic polynomial. Based on the solved coefficients, a mixed altitude angle and signal-to-noise ratio random model is established, and the fitting signal-to-noise ratio values ​​at all altitude angles are obtained. : Among them, the cubic polynomial fitting function based on the normalized signal-to-noise ratio average satisfies: ； The cubic polynomial fitting coefficient function based on standard deviation satisfies: ； In the formula, represents the altitude angle, represents the model parameters fitted based on the average value of the normalized signal-to-noise ratio, represents the model parameters based on the standard deviation fit.

4. The method for positioning and monitoring water equipment based on GNSS according to claim 3, characterized in that: The multipath error correction model is established by the following method; SA1 preprocesses the original observation data; SA2 calculates single-difference observations between stations; SA3 calculates double difference observation value sequences of satellite pairs formed by the first satellite observed and the second satellite to the nth satellite respectively according to the single difference observation values; and calculates double difference observation value residual sequences according to the double difference observation value sequence and the double difference observation values ​​calculated by the observation value model; SA4 maps the double difference observation value residual sequence into a corresponding single difference observation value residual sequence according to a certain mapping relationship and stores the sequence in a single difference observation value residual sequence array; Taking GNSS receiver A and GNSS receiver B as an example, the mapping relationship satisfies: Among them, based on the characteristic that the weighted average of the single difference residual is zero , represents the weighted function value of the altitude angle of the nth satellite, It represents the single difference observation residual of GNSS receiver A and GNSS receiver B for the nth satellite. It represents the double difference observation residual of GNSS receiver A and GNSS receiver B for the satellite pair consisting of the 1st satellite and the nth satellite, i represents the satellite number, A represents receiver A, and B represents receiver B; SA5 monitors the data volume N of the single-difference observation residual sequence array. When the data volume N is greater than or equal to the data volume threshold, the multipath error model is used to perform low-pass filtering on all single-difference observation residuals in the array to obtain multipath error correction values ​​and store them in the multipath error correction value database.

5. The method for positioning and monitoring water equipment based on GNSS according to any one of claims 2 to 4, characterized in that: The double-difference narrow-lane observation equation based on carrier phase in step S32 is: ； In the formula, is the wavelength of narrow-lane observations, and are the signal frequencies of two different frequencies, where i and j represent the satellite numbers, b and r represent the base station and monitoring station numbers, is the narrow-lane double-difference carrier phase observation value, are the direction cosines of the location parameters, is a positional parameter, is the altitude angle The mapping function of is the double-difference zenith tropospheric delay, and are the ionospheric delay errors of satellites i and j, is the double-difference narrow lane ambiguity, represents the multipath error, represents the narrow-lane phase residual.

6. The method for positioning and monitoring water equipment based on GNSS according to claim 5, characterized in that: The step S50 includes the following sub-steps: S51 constrains the wide lane fixed ambiguity to obtain a constrained wide lane fixed ambiguity, and constrains the narrow lane floating ambiguity based on the constrained wide lane fixed ambiguity to obtain a constrained narrow lane floating ambiguity; wherein the constraint condition adopted by the wide lane fixed ambiguity is: Where: represents the estimated integer ambiguity, Represents widelane floating point ambiguity; S52 searches and fixes the constrained narrow lane floating point ambiguity to obtain the narrow lane fixed ambiguity; S53 updates the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the position coordinate information of the receiver.

7. A GNSS-based water equipment positioning and monitoring device, characterized in that: include: A data acquisition module is used to acquire original observation data of each satellite received by a number of GNSS receivers installed on different water equipment; A data preprocessing module is used to perform standardization processing on the original observation data to obtain preprocessed observation data; The narrow lane floating point ambiguity resolution module is used to perform ionosphere correction, multipath correction and observation value weight assignment on the pre-processed observation data to obtain narrow lane purified observation data; perform double difference narrow lane observation value resolution on the narrow lane purified observation data to obtain narrow lane floating point ambiguity and floating point coordinate information; wherein, the observation value weight assignment on the pre-processed observation data includes the following steps: SD1 is based on the signal-to-noise ratio value of the frequency point corresponding to each altitude angle of the current satellite Fitted SNR value of the altitude angle corresponding to the random model of mixed altitude angle and SNR Calculating the expansion factor , the expansion factor satisfy: Where: is the signal-to-noise ratio value actually observed at the current moment, is the signal-to-noise ratio value fitted according to the altitude angle-signal-to-noise ratio fitting model, and represents the empirical constant, Indicates the altitude angle The maximum signal-to-noise ratio value of the corresponding frequency point, Indicates the altitude angle The minimum signal-to-noise ratio value of the corresponding frequency point, Indicates the altitude angle Standard deviation of the signal-to-noise ratio value at the corresponding frequency point; SD2 is based on the expansion factor , correct the covariance R matrix of the estimated observation accuracy; SD3 weights the preprocessed current epoch observations based on the modified covariance R matrix to determine the weight of each observation; A wide lane fixed ambiguity resolution module is used to perform double-difference wide lane observation value resolution and ambiguity fixation on the pre-processed observation data to obtain wide lane fixed ambiguity; The position solving module is used to constrain and fix the narrow lane floating point ambiguity by using the constrained wide lane fixed ambiguity to obtain the narrow lane fixed ambiguity; update the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the coordinate information of the GNSS receiver.

8. The GNSS-based water equipment positioning and monitoring device according to claim 7, characterized in that: The narrow lane floating point ambiguity resolution module includes a narrow lane data purification unit and a double difference narrow lane Kalman filter, wherein the narrow lane data purification unit includes an ionospheric random walk model, a multipath error correction model, a mixed altitude angle and signal-to-noise ratio random model, The ionospheric random walk model is used to perform noise superposition on the pre-processed ionospheric information of the current epoch using the ionospheric correction of the previous epoch; The multipath error correction model is used to perform multipath correction on the preprocessed current epoch observation value file; The mixed altitude angle and signal-to-noise ratio random model is used to assign weights to the preprocessed current epoch observations; The double-difference narrow-lane Kalman filter is used to filter and solve the narrow-lane purified observation data using a double-difference narrow-lane observation equation based on carrier phase to obtain narrow-lane floating-point ambiguity and floating-point coordinate information.

9. The GNSS-based water equipment positioning and monitoring device according to claim 8, characterized in that: The mixed altitude angle and signal-to-noise ratio random model is established in the following way: SC1 sets up a group of GNSS stations in an open environment to form a base station network; SC2 obtains the satellite observation information of the reference station network in the set time period, and the satellite observation information meets the following conditions: data efficiency>90%, cycle slip ratio>400, and multipath<1.0m; SC3 calculates the maximum signal-to-noise ratio and minimum signal-to-noise ratio of each frequency point of each satellite at different sites; SC4 calculates the normalized signal-to-noise ratio value of each frequency point of each satellite at different sites, and sorts the normalized signal-to-noise ratio values ​​at intervals of 1° altitude, and calculates the average normalized signal-to-noise ratio value at each altitude angle. and its standard deviation ; SC5 is based on the average value of the normalized signal-to-noise ratio at each altitude angle and its standard deviation A cubic polynomial fitting coefficient function is established to solve the coefficients of the cubic polynomial. Based on the solved coefficients, a mixed altitude angle and signal-to-noise ratio random model is established, and the fitting signal-to-noise ratio values ​​at all altitude angles are obtained. : Among them, the cubic polynomial fitting function based on the normalized signal-to-noise ratio average satisfies: ； The cubic polynomial fitting coefficient function based on standard deviation satisfies: ； In the formula, represents the altitude angle, represents the model parameters fitted based on the average value of the normalized signal-to-noise ratio, represents the model parameters based on the standard deviation fit.

10. The GNSS-based water equipment positioning and monitoring device according to claim 9, characterized in that: The multipath error correction model is established by the following method; SA1 preprocesses the original observation data; SA2 calculates single-difference observations between stations; SA3 calculates double difference observation value sequences of satellite pairs formed by the first satellite observed and the second satellite to the nth satellite respectively according to the single difference observation values; and calculates double difference observation value residual sequences according to the double difference observation value sequence and the double difference observation values ​​calculated by the observation value model; SA4 maps the double difference observation value residual sequence into a corresponding single difference observation value residual sequence according to a certain mapping relationship and stores the sequence in a single difference observation value residual sequence array; Taking GNSS receiver A and GNSS receiver B as an example, the mapping relationship satisfies: Among them, based on the characteristic that the weighted average of the single difference residual is zero , represents the weighted function value of the altitude angle of the nth satellite, It represents the single difference observation residual of GNSS receiver A and GNSS receiver B for the nth satellite. It represents the double difference observation residual of GNSS receiver A and GNSS receiver B for the satellite pair consisting of the 1st satellite and the nth satellite, i represents the satellite number, A represents receiver A, and B represents receiver B; SA5 monitors the data volume N of the single-difference observation residual sequence array. When the data volume N is greater than or equal to the data volume threshold, the multipath error model is used to perform low-pass filtering on all single-difference observation residuals in the array to obtain multipath error correction values ​​and store them in the multipath error correction value database.

11. The GNSS-based water equipment positioning and monitoring device according to any one of claims 8 to 10, characterized in that: The double-difference narrow-lane observation equation of the double-difference narrow-lane Kalman filter based on carrier phase is: ； In the formula, is the wavelength of narrow-lane observations, and are the signal frequencies of two different frequencies, where i and j represent the satellite numbers, b and r represent the base station and monitoring station numbers, is the narrow-lane double-difference carrier phase observation value, are the direction cosines of the location parameters, is a positional parameter, is the altitude angle The mapping function of is the double-difference zenith tropospheric delay, and are the ionospheric delay errors of satellites i and j, is the double-difference narrow lane ambiguity, represents the multipath error, represents the narrow-lane phase residual.

12. The GNSS-based water equipment positioning and monitoring device according to claim 11, characterized in that: The position solving module includes a narrow lane floating point ambiguity constraint unit, a narrow lane LAMBDA search fixed unit and a position solving unit. The narrow lane floating ambiguity constraint unit is used to constrain the wide lane fixed ambiguity to obtain the constrained wide lane fixed ambiguity, and constrain the narrow lane floating ambiguity based on the constrained wide lane fixed ambiguity to obtain the constrained narrow lane floating ambiguity; wherein the constraint condition adopted by the wide lane fixed ambiguity is: Where: represents the estimated integer ambiguity, Represents widelane floating point ambiguity; The narrow lane LAMBDA search fixing unit is used to search and fix the constrained narrow lane floating point ambiguity to obtain the narrow lane fixed ambiguity; The position solving unit is used to update the floating point coordinate information according to the narrow lane fixed ambiguity to obtain the position coordinate information of the receiver.

13. A GNSS-based water equipment positioning and monitoring system, characterized in that: include: Several GNSS receivers arranged on different waterborne equipment, a GNSS-based waterborne equipment positioning and monitoring device as claimed in any one of claims 7 to 12, a posture control module, a communication module and a power supply module; The GNSS receiver is used to receive raw observation data from different satellites; The attitude control module determines whether the position of the water equipment where the GNSS receiver is installed is within a preset safety area according to the position information output by the water equipment positioning monitoring device based on GNSS, and performs corresponding operations on the water equipment to ensure the safety of the equipment; The communication module is used for data transmission and communication with external device ad hoc network and wide area network; The power module supplies power to the GNSS receiver, the GNSS-based aquatic equipment positioning and monitoring device, the attitude control module, and the communication module.

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