Airborne multi-source integrity monitoring method and device for aviation navigation operation network

Abstract

The invention discloses an airborne multi-source integrity monitoring method and device for an aviation navigation operation network, and belongs to the field of satellite navigation, and the method comprises the steps: constructing an overall design architecture of an airborne multi-source high-performance navigation receiver; an ionosphere gradient monitoring algorithm based on carrier phase difference is provided, IGM detection statistical error characteristics of the carrier phase are researched, an ionosphere gradient monitoring threshold is established, and a switching mechanism from an airborne DFree mode to an IFree mode is realized; an aviation navigation multipath mitigation suppression algorithm is researched, multivariate feature extraction of observation data is combined, an observation data feature matrix is established, a parallel time convolutional neural network architecture is designed, and the weight is updated through a stochastic gradient descent method. According to the airborne multi-source integrity monitoring method and device for the aviation navigation operation network provided by the invention, accurate monitoring of the ionosphere gradient is realized, a switching mechanism between a DFree mode and an IFree mode is optimized, and the integrity monitoring capability and the system availability of the aviation navigation operation network are improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to an onboard multi-source integrity monitoring method and device for an aviation navigation operation network. Background Art

[0002] In today's globalized aviation environment, the reliability and efficiency of aviation navigation systems are crucial. With the advancement of technology and the increase in air traffic, traditional land-based navigation systems and satellite-based navigation systems have gradually shown their limitations. Although land-based navigation is stable and reliable, its coverage is limited; while satellite-based navigation provides wide-area coverage, it is often affected by various interferences. These problems not only limit the improvement of aviation navigation services, but also affect aviation safety and operational efficiency. Therefore, it is urgent to shift from traditional land-based navigation systems to more advanced aviation navigation operation networks that support the high safety requirements of civil aviation flights and can provide high-precision and high-integrity navigation guidance information.

[0003] In a complex aviation environment, the implementation of an airborne multi-source integrity monitoring method and device for an aviation navigation operation network requires the following problems to be solved: First, the ground-based monitoring of satellite navigation ionospheric anomalies has low resolution and conservative threat models. Existing ionospheric anomaly monitoring mainly relies on simple models, which lack real-time and accuracy. Second, in a complex environment, multipath effects occur frequently and have spatiotemporal complexity, and are the main source of error in carrier phase differential positioning. Existing multipath mitigation and suppression mainly considers single data features and fails to make full use of observation data to mitigate and suppress multipath errors.

[0004] In response to the first ionospheric gradient monitoring problem, in 2017, DLR, KAIST, and TUM jointly proposed ionospheric gradient monitoring (IGM) for DFMCGBAS, based the positioning algorithm on single-frequency observations and using the second navigation frequency for ionospheric monitoring. In 2022, DLR proposed a dual-frequency space-based ionospheric gradient monitoring method. Based on the original monitoring concept, a new unbiased test statistic was derived, which successfully eliminated the impact of common biases on ionospheric estimation. The current DFMCGBAS architecture uses an ionospheric-free smoothing mode (IFree) and a divergence-free smoothing mode (DFree) to minimize the impact of ionospheric anomalies. Among them, IFree can completely eliminate the impact of ionospheric gradients, so there is no need for ionospheric gradient monitoring. However, the noise level of IFree is high, which makes the navigation system error larger. In contrast, the noise level of DFree is lower, but it will be affected by the ionospheric gradient. Therefore, it is necessary to study the carrier phase gradient monitoring algorithm and optimize the switching mode from DFree to IFree.

[0005] For the second multipath mitigation and suppression problem, Feng Shen et al. considered multipath and NLOS signals as additive sparse errors in 2020, established a linear collaborative navigation observation model, and solved the navigation problem by studying sparse estimation algorithms. However, this method is limited by multipath interference and the increased number of NLOS signal channels. The method only uses range and Doppler observations, which has certain limitations. In 2021, Chao Liu et al. used the neural network CNN-LSTM to mine multipath features in the coordinate domain and frequency domain, which has better multipath mitigation performance than traditional methods. However, the possibility of multipath modeling in the spatial domain using machine learning has not been studied. Therefore, it is necessary to use machine learning, a data-driven method, to explore the intelligent identification and mitigation suppression methods of multipath effects in air-ground collaboration, and to improve the network integrity monitoring capability and system availability of aviation navigation operations. Summary of the invention

[0006] The purpose of the present invention is to provide an onboard multi-source integrity monitoring method and device for an aviation navigation operation network to solve the problems mentioned in the background technology.

[0007] To achieve the above-mentioned object, the present invention provides an airborne multi-source integrity monitoring device for an aviation navigation operation network, comprising a navigation source signal receiving module, a pan-source navigation information processing module and a communication receiving module, wherein the navigation source signal receiving module comprises: GNSS module, used to receive signals transmitted by the global positioning system to determine the position and time information of the aircraft; VDB module, used to receive navigation correction information sent from the ground; ILS / VOR receiver module to provide additional navigation signals; An inertial navigation system module that calculates position and velocity by measuring the device's own acceleration; Dual-frequency multi-mode receiver for simultaneous reception and processing of multiple navigation signals; The pan-source navigation information processing module includes: GBAS module and SBAS module are used to provide additional navigation correction information; GNSS / VOR / DME / INS / LDACS data fusion processing module, integrating information from multiple sources; The communication receiving module includes: ARINC429 interface and RS422 interface are used to complete the data communication standards between avionics components to transmit information between devices; RJ45 port, used for network connection, for device configuration or maintenance communication.

[0008] The present invention also provides an airborne multi-source integrity monitoring method for an aviation navigation operation network, comprising the following steps: S1. Propose an ionospheric gradient monitoring algorithm based on carrier phase difference, study the statistical error characteristics of IGM detection of carrier phase, establish the ionospheric gradient monitoring threshold, and implement the switching mechanism from airborne DFree to IFree mode; S2. An aviation navigation multipath mitigation and suppression algorithm is proposed. Combined with the multivariate feature extraction of observation data, the observation data feature matrix is ​​established, and a parallel time convolutional neural network architecture is designed. The weights are updated through the stochastic gradient descent method to achieve the prediction and mitigation of multipath errors.

[0009] Preferably, S1 specifically includes: S11. Optimize multiple navigation sources according to different flight phases of route operation and design a flexible integrated navigation algorithm; S12, taking the carrier phase observations at the L1 and L5 frequency points, and using the carrier phase difference to estimate the ionospheric gradient at the airborne end; S13, carrier phase difference and pseudo-range residual are combined to eliminate ionospheric delay term, and time average is used to eliminate ; S14, project the receiver differential code bias to the clock error term, eliminate the satellite differential code bias by differentiating it with the ground observation, and then use the carrier phase to code balance CCL to estimate the ionospheric delay ; S15, using the carrier phase observation, by combining pseudorange and carrier phase Variable averaging, constructing the test statistic for ionospheric gradient monitoring, and calculating using recursive filters In iteration Noise variance and error standard deviation, and calculate the monitoring threshold.

[0010] Preferably, the content of S11 is as follows: The ground station uploads the original observation data to the airborne terminal instead of the differential correction value, and uses the pure carrier phase for positioning on the airborne terminal. The observation values ​​broadcast by the ground station to the airborne terminal are as follows: (1); (2); in, Represents a specific frequency Pseudorange measurement under ; represents the carrier phase measurement at that frequency; Indicates the direct path LOS; and are the satellite and receiver clock biases, respectively; and represent the ionospheric and tropospheric delays, respectively; Indicates the ionospheric divergence at frequency The cumulative error of the filter under this condition; DFree and IFree smoothing this item is 0; and is a term related to multipath error; and It represents the receiver thermal noise factor; Indicates the frequency Antenna group delay under and Respectively represent the code deviation of the receiver and satellite RF front end at this frequency; Indicates the frequency Changes in the antenna phase center under ; and Indicates the phase deviation between the receiver and the satellite RF front end; Represents the carrier phase ambiguity.

[0011] Preferably, S12 and S13 are as follows: Taking the difference in carrier phase measurement between the L1 and L5 frequencies yields: (3); in represents the sum of carrier phase thermal noise and multipath error; and represent the differential phase deviations of the receiver and the satellite respectively; Indicates the differential phase center change; represents the combined form of the ambiguity after dual-frequency carrier difference; the difference between the carrier phase measurements of L1 and L5 frequencies is used to derive an expression for the airborne ionospheric gradient, which is used to separate the observables and isolate the noise term on the right side of the equation: (4); Carrier Phase Ambiguity , Differential Phase Center Change , and the differential phase deviation are included as known quantities in the measurement equation; The difference observation is simplified as follows: (5); (6); Error term , Random and zero mean, and represent the sum of thermal noise and multipath error of pseudorange and carrier phase respectively; the carrier difference is combined with the pseudorange difference to obtain: (7); in, represents the antenna group delay variation; Indicates the receiver differential code deviation; Indicates the satellite differential code deviation; The ionospheric term is eliminated and the average term By averaging the above formula over time, we get: (8).

[0012] Preferably, the content of S14 is as follows: According to the calculation method of ionospheric observation in CCL, the estimated ionospheric delay is: (9); Estimated Used to make differential estimates of the ionospheric differences between the airborne terminal and the ground station: (10); in, is the receiver differential airborne code deviation, the receiver differential ground code deviation is the noise term, is the ground-side antenna group delay characteristic, is the group delay characteristic of the airborne antenna, is the carrier phase differential ionospheric gradient estimation at the ground end, It is the ionospheric gradient estimation of the airborne end at the L1 frequency point; Since the receiver DCB term in the ionospheric delay is common across all satellites, the DCB term is solved jointly using weighted least squares or multi-epoch filters; Using the VHF Data Broadcast (VDB), the airborne system can estimate the combined ionospheric delay of each satellite observed by the ground reference receiver using the equation for ionospheric delay, which can be reconstructed as follows: (11); in, It represents the observed value of the wave phase at the L5 frequency. It represents the observed value of the wave phase at the L1 frequency; The onboard equipment calculates an estimate of the ionospheric delay seen by the ground station using the following expression: (12); The airborne system uses the CCL process described above to estimate the ionospheric delay in the air. .

[0013] Preferably, the content of S15 is as follows: Over a period of time, through The variable is averaged and the test statistic is calculated as: (13); in After averaging the pseudorange and carrier phase noise of L1 and L5, The residual error in is obtained by averaging the data for N seconds. ; Run a recursive filter of the form , and change the Substitute the following formula: (14); in represents the pseudorange and carrier phase noise at two frequencies, represents the differential pseudorange, represents the differential carrier phase; is a complex fuzzy term in dual frequency carrier processing, ignoring , assuming that thermal noise and multipath noise are white Gaussian noise, in the In iteration The variance of the noise is as follows: (15); The standard deviation of the error is given by the following equation: (16); in is the standard deviation describing the combination of thermal noise and multipath errors on L1 and L5.

[0014] Preferably, step S2 is as follows: S21. Use historical observation data, including observation residual values, signal strength, and elevation and azimuth angles, to construct a multipath error feature matrix, eliminate the influence of unit and scale differences between observation features, and normalize the observation features; S22. Design a parallel time convolutional neural network model to model multipath errors offline and suppress multipath online; S23. Use the stochastic gradient descent method to update the neural network parameters, realize the multipath prediction using the data-driven method, and alleviate the multipath effect.

[0015] Preferably, the content of S21 is as follows: The multipath error feature matrix is ​​constructed, and the selected observation features are divided into three categories: the first category is the features closely related to the multipath error studied by previous researchers; the second category is GNSS time; the third category is the quality features of the observation values; the observation features are normalized, and the normalized data set is as follows: (17); in Indicates The input of samples is the selected observation data features; Indicates the The output of samples is the unmodeled multipath error.

[0016] Preferably, the contents of S22 and S23 are as follows: The parallel time convolutional neural network consists of an input layer, 4 convolutional layers, a pooling layer, a Flatten layer, a fully connected layer and an output layer; the size of each convolution kernel in the network is , the stride in both directions is set to 1; the number of convolution kernels in each convolution layer is set to 64, 64, 32, and 32 respectively; the convolution layer The convolution operation is as follows: (18); in Indicates The convolutional layer input feature matrix on each channel; Indicates the number of channels of the convolutional layer input; Indicates The convolutional layer corresponding to the input convolution kernels; represents the convolutional layer The bias of the convolution kernel; It is the convolutional layer The output feature matrix of the convolution kernel; The Flatten layer is used to convert multidimensional data into one-dimensional data. Through each epoch of forward propagation, the value of the loss function is obtained. The loss function is defined as follows: (19); in To optimize the parameters; is the number of samples; The parameter information is adjusted through the gradient of the loss function, and the weights are updated using the stochastic gradient descent method, as shown below: (20); After obtaining the gradient of the loss function, the parameters are updated and the network training enters the next epoch.

[0017] Therefore, the present invention adopts the above-mentioned method and device for airborne multi-source integrity monitoring of aviation navigation operation network, which has the following beneficial effects: (1) The present invention helps to solve the aviation safety risks and efficiency degradation caused by frequent interference of aviation radio navigation signals; it provides an air-ground coordinated integrity monitoring method for future aviation navigation positioning, which improves system reliability while ensuring navigation accuracy; it establishes a new overall design architecture of an airborne multi-source high-performance navigation receiver, which can fully utilize multi-source information to ensure efficient operation of aviation navigation during the entire flight phase; (2) The present invention adopts the ionospheric gradient monitoring method of carrier phase difference to accurately detect ionospheric anomalies and reduce the problem of low resolution of traditional land-based navigation monitoring; optimizes the switching mechanism from DFree to IFree mode, effectively eliminates the influence of ionospheric gradient on the navigation system, and improves the stability and reliability of the navigation system under different ionospheric conditions; combines the multivariate characteristics of the observation data, establishes a feature matrix, and improves the recognition and processing capabilities of multipath errors; (3) The present invention uses a parallel time convolutional neural network to predict and mitigate the multipath effect, significantly improving the error processing effect of carrier phase differential positioning; through intelligent algorithms and machine learning technology, it realizes real-time processing and updating of navigation data, improving the real-time response capability of the system; it can effectively improve the economic benefits of aviation navigation, and by improving the accuracy and safety of aviation navigation and positioning systems, it can reduce the costs of airlines, reduce the number of flight delays and cancellations, and improve the efficiency and operating benefits of flights.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of the overall design architecture of an airborne multi-source high-performance navigation receiver according to an embodiment of the present invention; Figure 2 This is a flow chart of ionospheric gradient monitoring based on carrier phase differential according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a convolutional neural network model for multipath mitigation suppression according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0021] See also Figure 1 , an onboard multi-source integrity monitoring device for aviation navigation operation network, The overall design of each module of the airborne multi-source aviation navigation technology device and their functions are shown. The development environment is built based on ARM A76 + A55, and the overall development framework and basic dynamic library development are completed. The figure includes the navigation source signal receiving module and the pan-source navigation information processing module, as well as interfaces such as ARINC 429 and RS422, showing how these components interact with each other in the system. It also shows the internal connection method of the system, such as using RJ45 connection.

[0022] The GNSS module is responsible for receiving signals transmitted by the global positioning system to determine the position and time information of the aircraft. The VDB module receives navigation correction information sent from the ground to improve the accuracy of GNSS. The instrument landing system / very high frequency omnidirectional beacon receiver provides additional navigation signals to help the aircraft perform precise landing or heading positioning. The inertial navigation system is independent of external signals and calculates position and speed by measuring the acceleration of the device itself, thereby enhancing the redundancy and independence of navigation. The dual-frequency multi-mode receiver can receive and process multiple navigation signals at the same time. The GBAS and SBAS modules provide additional navigation correction information to improve the accuracy of the position. The GNSS / VOR / DME / INS / LDACS data fusion processing module fuses information from multiple sources to achieve comprehensive and flexible navigation in different flight phases. In the communication reception design, the ARINC429 and RS422 interfaces are used to complete the data communication standards between avionics components for information transmission between devices. The RJ45 interface is used for network connection and can be used for equipment configuration or maintenance communication. These modules work together to ensure that the aircraft can obtain accurate position and navigation information in various environments and conditions, thereby improving flight safety and efficiency.

[0023] See also Figure 2 , an airborne multi-source integrity monitoring method for an aviation navigation operation network, including an ionospheric gradient monitoring algorithm applicable to aviation navigation and a multipath suppression mitigation algorithm applicable to aviation navigation, Applicable to aviation navigation ionospheric gradient monitoring algorithm, the specific calculation steps are as follows: Step 1: Ionospheric gradient estimation based on carrier phase difference Consider that the ground station uploads the original observation data to the airborne terminal instead of the differential correction value, and uses the pure carrier phase for positioning on the airborne terminal. The observations broadcast by the ground station to the airborne terminal are as follows: ; ; in, Represents a specific frequency Pseudorange measurement under Represents the carrier phase measurement at this frequency. represents the path of direct access (LOS), and are the satellite and receiver clock biases, respectively. and represent the ionospheric and tropospheric delays, respectively. Indicates the ionospheric divergence at frequency The cumulative error of the filter under DFree and IFree smoothing is 0. and is a term related to multipath error. and It indicates factors such as receiver thermal noise. Indicates the frequency The antenna group delay under and They represent the code deviation of the receiver and satellite RF front end at this frequency respectively. Indicates the frequency The change of the antenna phase center under . and represents the phase deviation between the receiver and the satellite RF front end, Represents the carrier phase ambiguity. The combined effect of these parameters determines the positioning accuracy of aviation navigation.

[0024] Taking the difference in carrier phase measurement between the L1 and L5 frequencies yields: ; in represents the sum of carrier phase thermal noise and multipath error, and represent the differential phase deviations of the receiver and the satellite respectively. represents the combined form of the ambiguity after dual-frequency carrier differencing. Similar to the pseudorange case, the difference between the carrier phase measurements at the L1 and L5 frequencies can be used to derive an expression that separates the observables and isolates the noise term on the right side of the equation: (4); Carrier Phase Ambiguity , Differential Phase Center Change , and the differential phase deviation are known quantities contained in the measurement equation. If these values ​​can be determined, they can be used to Calculation, thus estimating , this estimate is affected only by thermal noise and multipath errors in the carrier phase, which are typically very small. Carrier tracking errors due to thermal noise are typically on the order of millimeters, and carrier phase multipath errors are limited to a physical quarter wavelength (approximately ±2.5 cm at L1 frequency). In the case where all multipath signal powers are lower than the direct signal power, the impact of multipath is typically on the order of centimeters or less. The change in phase center typically fluctuates over a range of a few centimeters or less. The carrier phase ambiguity is constant as long as carrier phase tracking is not interrupted.

[0025] The estimate will , , and are considered as known quantities. However, they are not usually known quantities. One possible strategy is to estimate these quantities first and then use the differences in carrier phase observations to calculate Ionospheric studies often use carrier phase to code balance (CCL) for estimation. Some key concepts can be borrowed to derive ionospheric estimates based mainly on carrier measurements.

[0026] Consider simplifying the difference observation: (5); (6); Note the error term above , are random and zero-mean, and represent the sum of thermal noise and multipath errors for pseudorange and carrier phase, respectively. Therefore, averaging over time will reduce the impact of these errors.

[0027] Combining the carrier difference with the pseudorange difference yields: (7); in, represents the antenna group delay variation; Thus, the ionospheric term can be eliminated, and the remaining are noise and bias terms. It can be averaged over time using the above formula, so after a long period of averaging we can get: (8).

[0028] In a typical CCL, the ionospheric observations are calculated as follows: (twenty one); By rearranging the above formula, the estimated ionospheric delay is: (9); The above formula shows that the carrier-code average ionospheric delay estimate will still contain the antenna group delay term and the differential code bias between the receiver and the satellite, but the carrier-related noise and multipath are reduced. will be projected into the clock error term, It will be eliminated by difference processing with ground observations. It can be used to make a differential estimate of the ionospheric difference between the airborne terminal and the ground station: (10); in, is the receiver differential airborne code deviation, the receiver differential ground code deviation is the noise term, is the ground-side antenna group delay characteristic, is the group delay characteristic of the airborne antenna, is the carrier phase differential ionospheric gradient estimation at the ground end, It is the ionospheric gradient estimation of the airborne end at the L1 frequency point; It should be noted that here, the satellite differential code bias has been eliminated. However, there is antenna group delay in both the airborne antenna and the ground reference station antenna. The two additional terms are and should be common in all satellites, and when the test statistic is projected into the position domain, these bias terms will be projected into the clock error state. Finally, the antenna group delay term will still be The error sources on the and , the thermal noise and multipath effects of the ground station and airborne receiver are at the centimeter level. Finally, the noise term is related to The averaging time scale reduces the noise, but does not eliminate it completely. However, compared to the ionospheric estimates based on pseudoranges, and The proportion of will be reduced. Both of the above methods will have differential group delay terms in the test statistics. In order to improve the accuracy of ionospheric gradient estimation, the ionospheric delay of the L1 frequency can be estimated as accurately as possible at the ground station and broadcast to the airborne end.

[0029] Since the receiver DCB term in (9) is common across all satellites, the DCB term can be jointly solved using weighted least squares or multi-epoch filters to improve the accuracy of ionospheric delay estimation and eliminate the impact of DCB.

[0030] The current GAST X proposal is for the ground station to estimate the ionospheric delay on the L1 frequency as accurately as possible and uplink this value into MT23. The ground station should know the antenna group delay characteristics , and over time, the ionospheric delay can be accurately estimated. By providing this measurement to the airborne system, using VHF Data Broadcast (VDB), the airborne system can use equation (9) to estimate the ionospheric delay combination for each satellite observed by the ground reference receiver. Equation (9) is reconstructed as follows: (11); in, It represents the observed value of the wave phase at the L5 frequency. Represents the observed value of the carrier phase at L1 frequency.

[0031] Airborne equipment can calculate an estimate of the ionospheric delay seen by the ground station using the following expression: (12); The accuracy of this ionospheric estimate can be inferred from the ground uplink's Sigma_Iono_L1 parameter. Airborne systems can also estimate their own ionospheric delay using the CCL process described above. However, this requires a long averaging period to converge to , and the air receiver does not understand characteristics of ground stations and therefore cannot mitigate this source of error as effectively as ground stations.

[0032] Step 2: IGM detection statistical error characteristics based on carrier phase Over a period of time, through The variable is averaged and the test statistic is calculated as: (13); in After averaging the pseudorange and carrier phase noise of L1 and L5, The residual error in is obtained by averaging the data for N seconds. . Examining the other terms in (13), we can see other deviations, and will be projected into the clock state again or will be eliminated after the air and ground estimates are differentiated. Therefore, the only term that will contribute to the residual is the one after averaging , antenna group delay variation , and carrier phase tracking noise The carrier phase tracking noise should be in the millimeter range and will be ignored in the rest of this analysis, including the effect of carrier phase tracking noise on Therefore, there are two terms that contribute to the error, one is like noise , and the other is like We will assume that the noise-like errors are effectively reduced by long-term averaging, while the slowly varying biases are not. We will assume that the DUFMAN model is sufficient to accurately describe the L1 and L5 Therefore, it is necessary to Describe the statistical characteristics.

[0033] calculate One way to do this is to run a recursive filter of the following form and substitute equation (6) into the following: (14); in Representing the pseudorange and carrier phase noise on the two frequencies, these errors resemble noise and will be reduced by averaging. represents the differential pseudorange, Represents the differential carrier phase. is a complex ambiguity term in the dual frequency carrier process, which should be a constant and will not decrease by averaging as long as carrier tracking is maintained. Therefore, only the noise term and will be reduced by averaging. Again, ignoring , because it is very small, assuming that thermal noise and multipath noise are white Gaussian noise, in the In iteration The variance of the noise is given by equation (15): (15); The standard deviation of the error is given by the following equation: (16); in is the standard deviation describing the combination of thermal noise and multipath error on L1 and L5. Therefore, for Seconds of averaging actually reduces the standard deviation of code and multipath errors by Unfortunately, multipath is not truly white Gaussian noise, so factor is too optimistic. As mentioned earlier, the extent to which longer smoothing times will reduce multipath noise remains an open question. For the purposes of this study, we assume that the multipath errors are Gaussian white noise and that their associated standard deviations are proportional to Zoom.

[0034] Applicable to aviation navigation multipath suppression mitigation algorithm, the specific calculation steps are as follows: Step 1: Establish multipath error characteristic matrix To construct the multipath error feature matrix, the selected observation features can be divided into three categories: one is the features closely related to the multipath error studied by previous researchers, such as satellite elevation and azimuth, signal strength, and observation residuals; the other is GNSS time, which reflects the temporal characteristics of the error; and the last is the quality features of the observations, such as the number of cycle slips, ambiguity resolution success rate, and ambiguity resolution status. In order to eliminate the influence of unit and scale differences between observation features and treat all dimensional features equally, the observation features need to be normalized. The normalized data set is as follows: (17); in Indicates The input of samples is the selected observation data features, Indicates the The output of samples is the unmodeled multipath error.

[0035] Step 2: Design a parallel temporal convolutional neural network A parallel time convolutional neural network architecture is designed to suppress multipath online. The network consists of an input layer, 4 convolutional layers, a pooling layer, a Flatten layer, a fully connected layer and an output layer, such as Figure 3 shown.

[0036] The size of each convolution kernel in this network is , the stride in both directions is set to 1. The convolution layer adopts an effective zero padding strategy, that is, refusing to add zeros, resulting in a reduction of 2 in the length and width of the output data after each convolution layer. The number of convolution kernels in each convolution layer is set to 64, 64, 32, and 32, respectively, to extract multiple types of features. Convolution layer The convolution operation is as follows: (18); in Indicates The convolutional layer input feature matrix on each channel, Indicates the number of channels of the convolutional layer input, Indicates The convolutional layer corresponding to the input convolution kernels; represents the convolutional layer The bias of the convolution kernel; It is the convolutional layer The output feature matrix of the convolution kernel. The final Flatten layer is responsible for converting multi-dimensional data into one-dimensional data. Through each epoch of forward propagation, the value of the loss function can be obtained. The loss function is defined as follows: (19); in To optimize the parameters, is the number of samples. The parameter information is adjusted through the gradient of the loss function, and the weight is updated using the stochastic gradient descent method, that is: (20); After obtaining the gradient of the loss function, the parameters are updated and the network training enters the next epoch. Finally, the multipath error prediction is realized by using the data-driven method, which effectively alleviates and suppresses multipath.

[0037] Therefore, the present invention adopts the above-mentioned airborne multi-source integrity monitoring method and device for aviation navigation operation network, and provides an improved navigation technology device and its air-ground coordinated integrity monitoring method to address the deficiencies of the existing airborne multi-source aviation navigation operation network in ionospheric gradient monitoring and multipath effect mitigation. By constructing a high-performance navigation receiver architecture and fusing multi-source data, the present invention achieves accurate monitoring of ionospheric gradients and optimizes the switching mechanism between DFree and IFree modes. At the same time, a parallel time convolutional neural network model is proposed for intelligent identification and mitigation of multipath errors, thereby improving the integrity monitoring capability and system availability of the aviation navigation operation network.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An airborne multi-source integrity monitoring device for an aviation navigation operation network, comprising a navigation source signal receiving module, a pan-source navigation information processing module and a communication receiving module, characterized in that: The navigation source signal receiving module includes: GNSS module, used to receive signals transmitted by the global positioning system to determine the position and time information of the aircraft; VDB module, used to receive navigation correction information sent from the ground; ILS / VOR receiver module to provide additional navigation signals; An inertial navigation system module that calculates position and velocity by measuring the device's own acceleration; Dual-frequency multi-mode receiver for simultaneous reception and processing of multiple navigation signals; The pan-source navigation information processing module includes: GBAS module and SBAS module are used to provide additional navigation correction information; GNSS / VOR / DME / INS / LDACS data fusion processing module, integrating information from multiple sources; The communication receiving module includes: ARINC429 interface and RS422 interface are used to complete the data communication standards between avionics components to transmit information between devices; RJ45 port, used for network connection, for device configuration or maintenance communication.

2. A method for airborne multi-source integrity monitoring of an aviation navigation operation network, using the airborne multi-source integrity monitoring device for an aviation navigation operation network as claimed in claim 1, characterized in that: The following steps are involved: S1. Propose an ionospheric gradient monitoring algorithm based on carrier phase difference, study the statistical error characteristics of IGM detection of carrier phase, establish the ionospheric gradient monitoring threshold, and implement the switching mechanism from airborne DFree to IFree mode; S2. An aviation navigation multipath mitigation and suppression algorithm is proposed. Combined with the multivariate feature extraction of observation data, the observation data feature matrix is ​​established, and a parallel time convolutional neural network architecture is designed. The weights are updated through the stochastic gradient descent method to achieve the prediction and mitigation of multipath errors.

3. The method for airborne multi-source integrity monitoring of an aviation navigation operation network according to claim 2, characterized in that: S1 specifically includes: S11. Optimize multiple navigation sources according to different flight phases of route operation and design a flexible integrated navigation algorithm; S12, taking the carrier phase observations at the L1 and L5 frequency points, and using the carrier phase difference to estimate the ionospheric gradient at the airborne end; S13, carrier phase difference and pseudo-range residual are combined to eliminate ionospheric delay term, and time average is used to eliminate ; S14, project the receiver differential code bias to the clock error term, eliminate the satellite differential code bias by differentiating it with the ground observation, and then use the carrier phase to code balance CCL to estimate the ionospheric delay ; S15, using the carrier phase observation, by combining pseudorange and carrier phase Variable averaging, constructing the test statistic for ionospheric gradient monitoring, and calculating using recursive filters In iteration Noise variance and error standard deviation, and calculate the monitoring threshold.

4. The method for airborne multi-source integrity monitoring of an aviation navigation operation network according to claim 3, characterized in that: S11 content is as follows: The ground station uploads the original observation data to the airborne terminal instead of the differential correction value, and uses the pure carrier phase for positioning on the airborne terminal. The observation values ​​broadcast by the ground station to the airborne terminal are as follows: （1）； （2）； in, Represents a specific frequency Pseudorange measurement under ; represents the carrier phase measurement at that frequency; Indicates the direct path LOS; and are the satellite and receiver clock biases, respectively; and represent the ionospheric and tropospheric delays, respectively; Indicates the ionospheric divergence at frequency The cumulative error of the filter under this condition; DFree and IFree smoothing this item is 0; and is a term related to multipath error; and It represents the receiver thermal noise factor; Indicates the frequency Antenna group delay under and Respectively represent the code deviation of the receiver and satellite RF front end at this frequency; Indicates the frequency Changes in the antenna phase center under ; and Indicates the phase deviation between the receiver and the satellite RF front end; Represents the carrier phase ambiguity.

5. The method for airborne multi-source integrity monitoring of an aviation navigation operation network according to claim 4, characterized in that: The contents of S12 and S13 are as follows: Taking the difference in carrier phase measurement between the L1 and L5 frequencies, we obtain equation (3): （3）； in represents the sum of carrier phase thermal noise and multipath error; and represent the differential phase deviations of the receiver and the satellite respectively; Indicates the differential phase center change; represents the combined form of the ambiguity after dual-frequency carrier difference; the difference between the carrier phase measurements of L1 and L5 frequencies is used to derive an expression for the ionospheric gradient at the airborne end, as shown in equation (4), which is used to separate the observable values ​​and isolate the noise term on the right side of the equation: （4）； Carrier Phase Ambiguity , Differential Phase Center Change , and the differential phase deviation are included as known quantities in the measurement equation; The difference observation is simplified as follows: （5）； （6）； Error term , Random and zero mean, and represent the sum of thermal noise and multipath error of pseudorange and carrier phase respectively; the carrier difference is combined with the pseudorange difference to obtain: （7）； in, represents the antenna group delay variation; Indicates the receiver differential code deviation; Indicates the satellite differential code deviation; The ionospheric term is eliminated and the average term By averaging the above formula over time, we get: （8）。 6. The method for airborne multi-source integrity monitoring of an aviation navigation operation network according to claim 5, characterized in that: S14 content is as follows: According to the calculation method of ionospheric observation in CCL, the estimated ionospheric delay is: （9）； Estimated Used to make differential estimates of the ionospheric differences between the airborne terminal and the ground station: （10）； in, is the receiver differential airborne code deviation and the receiver differential ground code deviation, is the noise term, is the ground-side antenna group delay characteristic, is the group delay characteristic of the airborne antenna, is the carrier phase differential ionospheric gradient estimation at the ground end, It is the ionospheric gradient estimation of the airborne end at the L1 frequency point; Since the receiver DCB term in the ionospheric delay is common across all satellites, the DCB term is solved jointly using weighted least squares or multi-epoch filters; Using VHF data broadcast VDB, the airborne system uses equation (9) to estimate the ionospheric delay combination of each satellite observed by the ground reference receiver. Equation (9) is reconstructed as follows: （11）； in, It represents the observed value of the wave phase at the L5 frequency. It represents the observed value of the phase of the download wave at the L1 frequency; The onboard equipment calculates an estimate of the ionospheric delay seen by the ground station using the following expression: （12）； The airborne system uses the CCL process described above to estimate the ionospheric delay in the air. .

7. The method for airborne multi-source integrity monitoring of aviation navigation operation network according to claim 6, characterized in that: S15 content is as follows: Over a period of time, through The variables are averaged and the test statistic is calculated as: （13）； in After averaging the pseudorange and carrier phase noise of L1 and L5, The residual error in is obtained by averaging the data for N seconds. ; Run a recursive filter of the form , and substitute equation (6) into the following equation: （14）； in represents the pseudorange and carrier phase noise at two frequencies, represents the differential pseudorange, represents the differential carrier phase; is a complex fuzzy term in dual frequency carrier processing, ignoring , assuming that thermal noise and multipath noise are white Gaussian noise, in the In iteration The variance of the noise is as follows (15): （15）； The standard deviation of the error is given by the following equation: （16）； in is the standard deviation describing the combination of thermal noise and multipath errors on L1 and L5.

8. The method for airborne multi-source integrity monitoring of an aviation navigation operation network according to claim 7, characterized in that: The steps of S2 are as follows: S21. Use historical observation data, including observation residual values, signal strength, and elevation and azimuth angles, to construct a multipath error feature matrix, eliminate the influence of unit and scale differences between observation features, and normalize the observation features; S22. Design a parallel time convolutional neural network model to model multipath errors offline and suppress multipath online; S23. Use the stochastic gradient descent method to update the neural network parameters, realize the multipath prediction using the data-driven method, and alleviate the multipath effect.

9. The method for airborne multi-source integrity monitoring of aviation navigation operation network according to claim 8, characterized in that: S21 content is as follows: The multipath error feature matrix is ​​constructed, and the selected observation features are divided into three categories: the first category is the features closely related to the multipath error studied by previous researchers; the second category is GNSS time; the third category is the quality features of the observation values; the observation features are normalized, and the normalized data set is as follows: （17）； in Indicates The input of samples is the selected observation data features; Indicates The output of samples is the unmodeled multipath error.

10. The method for airborne multi-source integrity monitoring of aviation navigation operation network according to claim 9, characterized in that: The contents of S22 and S23 are as follows: The parallel time convolutional neural network consists of an input layer, 4 convolutional layers, a pooling layer, a Flatten layer, a fully connected layer and an output layer; the size of each convolution kernel in the network is , the stride in both directions is set to 1; the number of convolution kernels in each convolution layer is set to 64, 64, 32, and 32 respectively; the convolution layer The convolution operation is as follows: （18）； in Indicates The convolutional layer input feature matrix on each channel; Indicates the number of channels of the convolutional layer input; Indicates The convolutional layer corresponding to the input convolution kernels; represents the convolutional layer The bias of the convolution kernel; It is the convolutional layer The output feature matrix of the convolution kernel; The Flatten layer is used to convert multidimensional data into one-dimensional data. Through each epoch of forward propagation, the value of the loss function is obtained. The loss function is defined as follows: （19）； in To optimize the parameters; is the number of samples; The parameter information is adjusted through the gradient of the loss function, and the weights are updated using the stochastic gradient descent method, as shown below: （20）； After obtaining the gradient of the loss function, the parameters are updated and the network training enters the next epoch.

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