Aircraft height multi-source observation fusion method and device

Abstract

The invention provides an aircraft height multi-source observation fusion method and device. The method comprises the following steps: setting a simulation area; generating a meteorological initial field set and a meteorological boundary field set according to meteorological data in the simulation area; deriving a corresponding meteorological background field set based on the meteorological initial field set and the meteorological boundary field set; obtaining meteorological data of multi-source observation, wherein the meteorological data of multi-source observation comprises meteorological data of open source observation, meteorological data of field air pressure observation and meteorological data of aircraft GPS height observation; converting the meteorological background field set to an observation space through an observation operator model; and fusing meteorological data of multi-source observation through an ensemble Kalman filtering algorithm so as to correct the meteorological background field set in the observation space.

Description

Technical Field

[0001] The present invention relates to the field of aircraft pressure altitude calculation, and more specifically, to a method and device for fusion of aircraft altitude multi-source observation. Background Art

[0002] To ensure safe operation, the aircraft needs to have sufficient vertical margin during test flights and operations, and the crew is informed of the current altitude of the aircraft through pressure altitude, onboard GPS altitude, and radio altitude. Among them, the Terrain Awareness and Warning System (TAWS) has high requirements for the accuracy of aircraft altitude. Since GPS signals are easily affected by terrain and caused interference or loss, radio altitude will only be displayed within a range of about 2,500 feet from the ground. Therefore, pressure altitude is still the main reference parameter of TAWS. Generally, the aircraft pressure altitude relies on real-time onboard pressure observations, with reference to the sea level pressure, sea level temperature, and vertical lapse rate defined by the International Standard Atmosphere (ISA), and is calculated using the pressure altitude formula derived from the statics equation. The algorithm involves many assumptions, which cannot reflect the complex changes of the real atmosphere, and there is a "principle error", which seriously affects the accuracy of the aircraft pressure altitude calculation.

[0003] Traditional pressure altitude correction methods include correction using correction tables, etc., which is to make altitude corrections by referring to the operating manuals issued by the International Civil Aviation Organization (ICAO), Boeing and Airbus when the temperature deviates from the ISA standard atmospheric temperature, especially in low temperature environments. In addition, there are other correction methods, such as establishing a system mathematical model, starting from the relationship between air pressure and altitude under standard atmospheric conditions in the pressure altitude calculation formula, performing a binary n-order Taylor expansion, and inputting temperature and pressure data provided by the meteorological department to correct the "principle error" that occurs when it does not match the preset parameters.

[0004] However, most of the aircraft pressure altitude algorithms currently used are based on the sea level pressure and temperature of the standard atmosphere, and the use of correction table compensation is not conducive to real-time viewing by pilots. When compensating for pressure altitude based on mathematical models, the meteorological department is required to provide the sea level pressure and temperature of the nearby area, but the meteorological observation stations are sparsely distributed, making it difficult to obtain the measured values ​​or forecast values ​​of the aircraft's location. After compensation, the measurement accuracy of pressure altitude is difficult to give, and empirical relationships are often used for estimation. Therefore, it is difficult to integrate multi-source meteorological element observations and GPS altitude, which is highly accurate but not stable enough. Summary of the invention

[0005] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] One of the purposes of the present invention is to provide a method for fusion of aircraft altitude multi-source observations based on field observation sites and GPS signals. The method collects field pressure observations, GPS altitude measured by aircraft in real time, and observations by satellites and soundings of meteorological departments, and based on given observation errors, fuses and assimilates multi-source observations based on an ensemble Kalman filter (EnKF) algorithm, corrects aircraft pressure altitude, and thus improves the accuracy of aircraft pressure altitude.

[0007] According to one aspect of the present disclosure, a method for fusing multi-source observations of aircraft altitude is provided, the method comprising: setting a simulation area; generating a meteorological initial field set and a meteorological boundary field set based on meteorological data in the simulation area; deriving a corresponding meteorological background field set based on the meteorological initial field set and the meteorological boundary field set; acquiring meteorological data of multi-source observations, the meteorological data of multi-source observations including meteorological data of open source observations, meteorological data of field air pressure observations, and meteorological data of aircraft GPS altitude observations; converting the meteorological background field set to an observation space through an observation operator model; and fusing the meteorological data of multi-source observations through a set Kalman filter algorithm, thereby correcting the meteorological background field set in the observation space.

[0008] In one embodiment of the present disclosure, generating a meteorological initial field set and a meteorological boundary field set based on meteorological data in a simulation area further includes: acquiring meteorological data in a global forecast system; interpolating the meteorological data to the simulation area; and perturbing the interpolated meteorological data to generate a meteorological initial field set and a meteorological boundary field set.

[0009] In a further embodiment of the present disclosure, a random noise method is used to perturb the interpolated meteorological data.

[0010] In another embodiment of the present disclosure, deriving a corresponding meteorological background field set based on a meteorological initial field set and a meteorological boundary field set further includes: operating the meteorological initial field set and the meteorological boundary field set through a set numerical forecasting model to generate a corresponding meteorological background field set.

[0011] In yet another embodiment of the present disclosure, the meteorological data of the field air pressure observation includes at least the wind speed, air pressure and temperature in the simulation area collected in advance.

[0012] In another embodiment of the present disclosure, converting the meteorological background field set to the observation space through the observation operator model further includes: setting corresponding observation operator models for the meteorological data of open source observations, the meteorological data of field air pressure observations, and the meteorological data of aircraft GPS altitude observations; and converting the meteorological background field set to the observation space through the set observation operator model.

[0013] In another embodiment of the present disclosure, the ensemble Kalman filter algorithm adopts an adjusted Kalman gain matrix, the Kalman gain matrix is ​​associated with the background error covariance, and the adjusted Kalman gain matrix is ​​associated with the observation error covariance set according to the observation device and the observation position.

[0014] In a further embodiment of the present disclosure, the background error covariance is estimated by a mesoscale numerical model.

[0015] According to another aspect of the present disclosure, a device for fusion of aircraft altitude multi-source observations is provided, the device comprising: a memory; and at least one processor communicatively coupled to the memory, the at least one processor being configured to execute the aircraft altitude multi-source observation fusion method of the present invention.

[0016] According to another aspect of the present disclosure, a non-transitory computer-readable medium storing computer-executable instructions is provided. When the computer-executable instructions are executed by a processor, the aircraft altitude multi-source observation fusion method of the present invention is performed.

[0017] These and other features and advantages will become apparent by reading the following detailed description and by reference to the associated drawings.It is to be understood that the foregoing general description and the following detailed description are illustrative only and are not restrictive of the aspects of what is claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to understand the manner in which the above features of the present invention are used in detail, the above briefly summarized contents can be described in more detail with reference to various embodiments, some of which are shown in the accompanying drawings. However, it should be noted that the accompanying drawings only show some typical aspects of the present invention and should not be considered to limit its scope, because the description may allow for other equally effective aspects.

[0019] Figure 1 The present invention is a flowchart of a method for fusing aircraft altitude multi-source observations according to an embodiment of the present invention.

[0020] Figure 2 It is a process schematic diagram of a method for fusion of aircraft altitude multi-source observations according to an embodiment of the present invention.

[0021] Figure 3 4 is a schematic diagram comparing pressure altitude deviations according to an embodiment of the present invention.

[0022] Figure 4 2 is a schematic diagram comparing pressure altitude errors according to an embodiment of the present invention.

[0023] Figure 5 is a schematic diagram of an aircraft altitude multi-source observation fusion device according to an embodiment of the present invention.

[0024] In the drawings, the drawings are not drawn to scale. DETAILED DESCRIPTION

[0025] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0026] In the description of the present disclosure, it should be noted that, unless otherwise specified, "multiple" means more than two; the terms "upper", "lower", "left", "right", "inside", "outside", etc. indicate positions or positional relationships only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present disclosure. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. "Vertical" is not strictly vertical, but is within the allowable error range. "Parallel" is not strictly parallel, but is within the allowable error range.

[0027] The directional words appearing in the following description are all directions shown in the figures, and do not limit the specific structure of the present disclosure. In the description of the present disclosure, it should also be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to the specific circumstances.

[0028] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present disclosure. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] In the description of the embodiments of the present disclosure, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0030] Figure 1 The present invention is a flowchart of a method for fusing aircraft altitude multi-source observations according to an embodiment of the present invention.

[0031] like Figure 1 As shown in FIG. 1 , in step 102 , a simulation area may be set. For example, with the aircraft position as the center, multiple layers of nesting may be set, the outermost layer being the parent domain with the lowest resolution, and the inner layer being the child domain with a higher resolution, thereby setting the simulation area.

[0032] In step 104, a meteorological initial field set and a meteorological boundary field set may be generated for the meteorological data in the simulation area.

[0033] In a non-limiting example, meteorological data in the Global Forecast System (GFS) can be obtained, the meteorological data can be interpolated to the simulation area set in step 102, and the interpolated meteorological data can be disturbed to generate a meteorological initial field set and a meteorological boundary field set. Among them, GFS is an open source global numerical weather forecast model and one of the most widely used numerical weather forecast systems in the world, so the required meteorological data can be downloaded from it. In addition, after the obtained meteorological data is interpolated, the interpolated meteorological data can be disturbed, for example, by using a random noise method.

[0034] It can be understood that the interpolation and random noise methods described above are only exemplary, and other methods can also be used to obtain the meteorological initial field set and the meteorological boundary field set according to actual conditions.

[0035] In step 106, a corresponding meteorological background field set may be derived based on the meteorological initial field set and the meteorological boundary field set.

[0036] In a non-limiting example, the meteorological initial field set and the meteorological boundary field set can be operated by an ensemble numerical forecast model to generate a corresponding meteorological background field set. For example, the Weather Research and Forecasting (WRF) model can be run for each meteorological initial field in the meteorological initial field set, and the meteorological background field set of a corresponding future moment can be obtained by integrating over time. The WRF model is a numerical weather forecast and atmospheric research tool, and it can be understood that other similar models can also be used to obtain the meteorological background field set.

[0037] In step 108, meteorological data observed from multiple sources may be obtained, and the meteorological data observed from multiple sources may include meteorological data observed from open sources, meteorological data observed from field pressure, and meteorological data observed from aircraft GPS altitude, etc. The meteorological data observed from open sources, meteorological data observed from field pressure, meteorological data observed from aircraft GPS altitude, etc., and the meteorological data in the GFS mentioned in step 104 include various meteorological variables such as wind speed, air pressure, and temperature, and the types of these meteorological variables depend on their sources.

[0038] In a non-limiting example, the meteorological data of the field pressure observation includes at least the wind speed, air pressure and temperature in the simulation area collected in advance. For example, the meteorological data of the field pressure observation can be self-constructed by using a detection instrument to detect at an observation point.

[0039] In step 110, the meteorological background field set may be converted into the observation space through the observation operator model.

[0040] In a non-limiting example, corresponding observation operator models can be set for meteorological data observed by open source, meteorological data observed by field pressure, and meteorological data observed by aircraft GPS altitude, and then the meteorological background field set is converted to the observation space through the set observation operator model. For example, for meteorological data observed by field pressure, spatial three-dimensional interpolation can be used; and for meteorological data observed by aircraft GPS altitude, variables such as sea level pressure and temperature can be converted to altitude.

[0041] In addition, the observation operator model can be obtained, for example, through a grid statistical interpolation (GSI) model. The GSI model is a variational assimilation system that can be used to assimilate a variety of observation data, including conventional observation data, satellite data, radar data, and GPS occultation data.

[0042] In step 112, the meteorological data observed from multiple sources may be fused by using an ensemble Kalman filter algorithm, thereby correcting the meteorological background field set in the observation space.

[0043] In a non-limiting example, the ensemble Kalman filter algorithm uses an adjusted Kalman gain matrix, wherein the Kalman gain matrix is ​​associated with a background error covariance, and the adjusted Kalman gain matrix is ​​associated with an observation error covariance set according to the observation device and the observation position. Further, the background error covariance can be estimated, for example, by a mesoscale numerical model, while the observation error covariance can be directly given according to the characteristics and experience of the observation device itself.

[0044] Figure 2The process diagram of the aircraft altitude multi-source observation fusion method according to one embodiment of the present invention is shown. This embodiment is described by taking the commonly used forecast model WRF and assimilation model GSI as examples, but it can be appreciated that other numerical forecast models and assimilation models with similar functions can also be used.

[0045] In step 202, the simulation area is set. For example, the simulation area of ​​the WRF processing system (WPS) is set according to the flight plan, including the parent domain and the multi-layer nested subdomain, to ensure that the route is covered while identifying refined meteorological variables. For example, the d01 area can be set to a 9km resolution and a grid number of 721*562, including China and the northwest Pacific Ocean, to resolve large-scale weather conditions. The nested d02 and d03 have a resolution of 3km and 1km, respectively, and the grid numbers are 187*175 and 181*181, respectively, to resolve high-resolution meteorological elements. The number of vertical layers is 57, and the model top is 10hPa, to fully cover the flight altitude of aircraft takeoffs and landings and routes.

[0046] In step 204, the meteorological initial field set and meteorological boundary field set of the ensemble disturbance are generated. For example, the GFS global model forecast data of the corresponding time is downloaded, combined with the static terrain data, WPS is run, and the meteorological data on the simulated grid is generated by horizontal interpolation. Real.exe in WRF is run to perform preprocessing steps such as vertical interpolation to generate wrfinput_d01 (i.e., the meteorological initial field set) and wrfbdy_d01 (i.e., the meteorological boundary field set). The updated sea surface temperature, terrain data and other conditions in wrfinput_d01 are extracted and written into the ensemble analysis field file of the current time generated by the cyclic assimilation forecasting system (i.e., GSI) for replacement.

[0047] In step 206, the ensemble numerical forecast model in WRF is run to obtain the meteorological background field set x corresponding to the meteorological initial field set and the meteorological boundary field set generated in step 204. b (As a non-limiting example, the meteorological background field set x after 6 hours can be derived by WRF. b ). For example, 80 ensemble members are set to minimize sampling errors while balancing the amount of calculation. The parameterization schemes used by WRF include: WSM6 microphysics scheme (WRFsingle-moment 6-class microphysics scheme), GCM rapid radiative transfer model (RRTM-G) and shortwave and longwave radiation schemes, Yonsei University (YSU) planetary boundary layer (PBL) scheme, NOAH land surface model (unified Noahland surface model) and Tiedtke cumulus parameterization scheme.

[0048] In step 208, obtain meteorological data y from multiple sources o , which can include open source observations, self-built field pressure observations, and aircraft GPS altitude observations, and the observation error covariance R is given according to the equipment and observation location.

[0049] The above meteorological data o It can include conventional field observation data and cloud wind guidance and clear sky radiation observation data, which can be obtained using the following detection equipment: Advanced Microwave Sounding Unit-A (AMSU-A), Atmospheric Infrared Sounding Unit (AIRS), Microwave Humidity Sounding Unit (MHS) and High Resolution Infrared Radiation Sounding Unit (HIRS / 4), etc. In addition, the meteorological data observed by satellite can be sparsely processed, for example, the sparse grid is 60km, and the observation errors are considered to be independent of each other.

[0050] For meteorological data observed in the field, a single-site pressure measuring instrument can be set up in the area with special terrain to monitor and transmit the pressure observation data in real time. According to the statistics of the previous data, the observed meteorological data varies greatly with the altitude, and the changes are small in the short time of the test.

[0051] In step 210, the observation operator model H is run to transform the pattern variable x b Convert to observation space and get observation prior Hx b As a non-limiting example, the observation prior Hx b It can be calculated by GSI, while the bias correction of meteorological data observed by satellite radiation can be achieved using EnKF calculation based on analysis of error covariance.

[0052] For the meteorological data observed at the field observation station, spatial three-dimensional interpolation can be used to transform the pattern variable x b Convert to observation space; for meteorological data observed by GPS altitude, variables such as sea level pressure and temperature can be converted to pressure altitude.

[0053] In step 212, an ensemble mean square Kalman filter (EnSRF) assimilation model is run to obtain a corrected meteorological background field ensemble.

[0054] For the meteorological background field set x obtained in step 206 b , additional operations are needed to reduce the sampling error caused by the limited number of sets and pattern errors, and to avoid filter divergence.

[0055] Specifically, the corrected meteorological background field set is set as:

[0056]

[0057] Among them, x aIt can also be called the analysis field, that is, the variable after assimilation and update; x b represents the background field, that is, the variables before assimilation update (i.e., the meteorological background field set x b , see step 206); y o represents meteorological data from multiple sources including open source observations, self-built field stations, and aircraft GPS (see step 208); H represents the observation operator, which converts meteorological variables into the observation space (see step 210). o -Hx b That is, the observation increment, which is the difference between the observation and the model. represents the adjusted Kalman gain matrix, and R is the observation error covariance calculated as follows:

[0058]

[0059] Formula (2) indicates that a scalar is multiplied on the basis of the original Kalman gain matrix K to reduce the sampling error caused by the observation, where the superscript T represents the transpose of the matrix. The calculation formula of the Kalman gain matrix K is:

[0060] K=P b H T (HP b H T +R) -1 (3)

[0061] Among them, P b Represents the background error covariance. In actual operation, in order to avoid matrix operation, we can use the Monte Carlo idea to estimate the error covariance of background and observation:

[0062]

[0063] Among them, x′ b and Hx′ b represents the disturbance of the set member in the pattern space and the observation space, respectively, by x calculated in step 206 and step 210 b and Hx b Subtract its own average value to obtain. In particular, when processing a single observation, both are one-dimensional vectors. Assimilate all observed meteorological data (i.e., the meteorological data y observed by multiple sources in step 208) o ) and finally generate the corrected analysis pressure altitude x a .

[0064] It can be appreciated that the above steps 202 to 206 ( Figure 2 220) is an initialization process to obtain an initial meteorological background field set. For the sake of distinction, it is set to x b 0, and steps 208 to 212 ( Figure 2230) is an iterative process, which can cyclically correct the meteorological background field set. Specifically, in the iterative process, the corrected analysis pressure height x obtained in step 212 is a It can be used as the meteorological background field set in step 206 again. For the sake of distinction, it is set to x b’ Then proceed to step 208 again to obtain the multi-source meteorological data at this time in real time. For the sake of distinction, set it to y o’ Then, in step 210, the observation operator model is run. For the sake of distinction, it is set to H', and the pattern variable x b’ Convert to observation space and get observation prior Hx b’ Then, in step 212, the EnSRF assimilation model is run to b’ Make modifications.

[0065] Figure 3 and Figure 4 A comparison diagram of the pressure altitude deviation and the comparison diagram of the pressure altitude error according to an embodiment of the present invention are shown, wherein HGT_Pres corresponds to the pressure altitude before correction, HGT_ANL1 corresponds to the pressure altitude after assimilating all meteorological observations and field single-point station observations, and HGT_ANL2 corresponds to the pressure altitude after continuing to assimilate GPS on the basis of HGT_ANL1.

[0066] Assume that on September 9, 2023, the aircraft flew to a mountain peak at an altitude of 1366m at a pressure altitude of 5400ft for a test mission, and collected three sets of flight data. When the aircraft was close to the ground, the radio altimeter started to work, and the radio altitude plus the terrain altitude was selected as the true altitude. The pressure altitude displayed on the aircraft was verified, and there was a large error, see Figure 3 and Figure 4 HGT_Pres in

[0067] Adopt the aircraft altitude multi-source observation fusion method described above, download GFS forecast data and terrain data, run WPS and real.exe to generate the meteorological initial field ensemble and meteorological boundary field ensemble, run the WRF simulation of 80 ensemble members, and make an ensemble forecast after 6 hours, that is, the meteorological background field ensemble.

[0068] The model output file with the innermost 1km resolution is read, the airborne latitude, longitude and pressure observations are read, the meteorological variables are interpolated, and the two-dimensional distribution of sea level pressure, sea surface temperature and vertical lapse rate is diagnosed.

[0069] Download the meteorological observation data at 08:00, collect the air pressure observations of the field observation sites, run GSI to generate observation priors, and then run the Ensemble Kalman Filter (EnKF) module in GSI to assimilate the existing observed meteorological background field set to generate a more accurate meteorological background field set under observation constraints. Figure 3 and Figure 4 HGT_ANL2 in.

[0070] like Figure 3 As shown, the results show that for the three flight missions (corresponding to 1, 2, and 3 on the horizontal axis), the deviations of the corrected pressure altitude are all smaller than the original pressure altitude, that is, compared with HGT_Pres, the deviation of HGT_ANL2 is closer to the 0 line.

[0071] like Figure 4 As shown, after statistics, referring to the mean absolute error (MAE) group, it can be seen that the pressure altitude before correction (corresponding to Figure 4 The MAE of HGT_Pres in is 163ft. After assimilating all meteorological observations and field single-point station observations (corresponding to Figure 4 HGT_ANL1 in the figure), MAE is reduced to 113ft, and after continuing to assimilate GPS (corresponding to Figure 4 The MAE is reduced to 46 ft. Referring to the root mean square error (RMSE) group, it can be seen that the pressure altitude before correction (corresponding to Figure 4 The RMSE of HGT_Pres in is 199ft. After assimilating all meteorological observations and field single-point station observations (corresponding to Figure 4 HGT_ANL1 in the RMSE is reduced to 159ft. After continuing to assimilate GPS (corresponding to Figure 4 The RMSE is reduced to 116 ft by using the HGT_ANL2 in the above figure. It can be seen that the pressure altitude error can be effectively reduced by the solution of the present invention.

[0072] Figure 5 A block diagram of an aircraft altitude multi-source observation fusion device according to an embodiment of the present invention is given. The device shows a general hardware environment in which the present invention can be applied according to an exemplary embodiment of the present invention. The device can be any machine configured to perform processing and / or calculations, and can be but not limited to a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant (PDA), a smart phone, or any combination thereof. The above system can be implemented in whole or in part by the device or a similar device or system.

[0073] The apparatus may include components connected to or in communication with a bus 520. For example, the apparatus may include a bus 520, a processor 505, and one or more memories 510, among other things.

[0074] The processor(s) 505 may be any type of processor and may include, but is not limited to, a general purpose processor and / or a dedicated processor (e.g., a special processing chip), an intelligent hardware device (e.g., a general purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 505 may be configured to operate a memory array using a memory controller. In other cases, a memory controller (not shown) may be integrated into the processor(s) 505. The processor(s) 505 may be responsible for managing the bus 520 and general processing, including executing software 515 stored on the memory 510. The processor(s) 505 may also be configured to perform various functions described herein related to the aircraft altitude multi-source observation fusion method. For example, the processor(s) 505 may be configured to: set a simulation area; generate a meteorological initial field set and a meteorological boundary field set based on the meteorological data in the simulation area; derive a corresponding meteorological background field set based on the meteorological initial field set and the meteorological boundary field set; obtain meteorological data from multi-source observations, the meteorological data from multi-source observations including meteorological data from open source observations, meteorological data from field air pressure observations, and meteorological data from aircraft GPS altitude observations; convert the meteorological background field set to an observation space through an observation operator model; and fuse the meteorological data from multi-source observations through a ensemble Kalman filter algorithm, thereby correcting the meteorological background field set in the observation space.

[0075] The memory 510 may be any storage device that can implement data storage. The memory 510 may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a floppy disk, a hard disk, a tape or any other magnetic medium, an optical disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory and / or any other memory chip or box, and / or any other medium from which a computer can read data, instructions and / or codes. The memory 510 may store computer executable software 515 including computer readable instructions that, when executed, cause the processor to perform the various functions described herein. The memory 510 may have various data / instructions / codes for implementing the various functions described herein related to the design of the aircraft altitude multi-source observation fusion device.

[0076] Software 515 may be stored in memory 510, including but not limited to an operating system, one or more applications, drivers, and / or other data and codes. Instructions for performing the various functions described herein may be included in one or more applications, and the components of the device may be implemented by the processor 505 reading and executing the instructions of one or more applications. In some cases, the software 515 may not be directly executable by the processor, but may (for example, when compiled and executed) enable the computer to perform the various functions described herein related to the management of technical publication compilation.

[0077] What has been described above includes examples of various aspects of the claimed subject matter. Of course, it is not possible to describe every conceivable combination of components or methods for the purpose of describing the claimed subject matter, but one of ordinary skill in the art will recognize that many further combinations and permutations of the claimed subject matter are possible. Thus, the disclosed subject matter is intended to encompass all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A method for fusion of aircraft altitude multi-source observations, the method comprising: Set up the simulation area; Generating a meteorological initial field set and a meteorological boundary field set according to the meteorological data in the simulation area; Derivation of a corresponding meteorological background field set based on the meteorological initial field set and the meteorological boundary field set; Acquire meteorological data observed from multiple sources, wherein the meteorological data observed from multiple sources include meteorological data observed from open sources, meteorological data observed from field pressure, and meteorological data observed from aircraft GPS altitude; The meteorological background field set is converted into an observation space through an observation operator model; as well as The meteorological data of the multi-source observations are fused by using an ensemble Kalman filter algorithm, so as to correct the meteorological background field set in the observation space.

2. The method according to claim 1, characterized in that Generating a meteorological initial field set and a meteorological boundary field set for the meteorological data in the simulation area further comprises: Access to meteorological data from the Global Forecast System; interpolating the meteorological data to the simulation area; and The interpolated meteorological data are disturbed to generate a meteorological initial field set and a meteorological boundary field set.

3. The method according to claim 2, characterized in that A random noise method is used to perturb the interpolated meteorological data.

4. The method according to claim 1, characterized in that Derivation of a corresponding meteorological background field set based on the meteorological initial field set and the meteorological boundary field set further includes: The meteorological initial field set and the meteorological boundary field set are calculated by using an ensemble numerical forecast model to generate a corresponding meteorological background field set.

5. The method according to claim 1, characterized in that The meteorological data of the field air pressure observation at least includes the wind speed, air pressure and temperature in the simulation area collected in advance.

6. The method according to claim 1, characterized in that Converting the meteorological background field set to the observation space through the observation operator model further includes: Setting corresponding observation operator models for the open source observation meteorological data, the field pressure observation meteorological data, and the aircraft GPS altitude observation meteorological data; and The meteorological background field set is converted into the observation space through the set observation operator model.

7. The method according to claim 1, characterized in that The ensemble Kalman filter algorithm uses an adjusted Kalman gain matrix, which is associated with a background error covariance, and the adjusted Kalman gain matrix is ​​associated with an observation error covariance set according to an observation device and an observation position.

8. The aircraft altitude multi-source observation fusion method according to claim 7, characterized in that: The background error covariance is estimated by a mesoscale numerical model.

9. An aircraft altitude multi-source observation fusion device, the device comprising: Memory; as well as At least one processor is communicatively coupled to the memory, and the at least one processor is configured to execute the aircraft altitude multi-source observation fusion method according to any one of claims 1 to 8.

10. A non-transitory computer-readable medium storing computer-executable instructions, wherein when the computer-executable instructions are executed by a processor, the aircraft altitude multi-source observation fusion method according to any one of claims 1 to 8 is performed.

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