A method for flight check evaluation of a microwave landing system

Abstract

The application provides a flight calibration evaluation method of a microwave landing system, and aims at the problem of insufficient spatial resolution of collected data in the MLS flight calibration process. The radial basis function neural network interpolation method is used to post-process the sparse data collected in the flight calibration, the collected sparse data is used as a training set, high-precision interpolation is realized in a three-dimensional space, MLS three-dimensional space signal field type inversion reconstruction is realized, and thus the MLS complete space signal field type is evaluated. The application provides an MLS flight calibration evaluation method without the aid of a large number of complicated flight subjects, and improves the accuracy and efficiency of the MLS flight calibration.

Description

Technical Field

[0001] This invention belongs to the field of flight calibration technology, specifically relating to a flight calibration and evaluation method for a microwave landing system (MLS). Background Technology

[0002] Flight calibration is an essential technical means to ensure aviation safety. It aims to check and evaluate the spatial signal quality and tolerances of various navigation, radar, and communication equipment. Microwave Landing Systems (MLS), as a new type of landing guidance system, are currently mainly used in military aviation. They can provide position information over a wide coverage area, measured in azimuth, elevation, and distance. Given the increasingly complex air traffic conditions, MLS has broad development prospects in the civil aviation field, aligning with the trend of military-civilian integration. Flight calibration of microwave landing systems is a crucial guarantee for the safe and reliable operation of MLS and a testing and certification process to ensure the safe landing of aircraft under MLS guidance.

[0003] Compared to traditional instrument landing systems (ILS), microwave landing systems offer more available channels, wider coverage, and more flexible approach methods, making flight calibration inherently more complex. Currently, the domestic civil aviation sector lacks comprehensive evaluation methods for MLS flight calibration. Using traditional flight calibration evaluation methods during MLS flight calibration could result in numerous missed spatial signals, making it difficult to achieve a complete three-dimensional spatial signal field assessment of the MLS.

[0004] Microwave landing systems (MLS) require more reliable and comprehensive flight verification and evaluation methods. Based on sparse discrete data conditions, this paper proposes a method that involves post-processing the data and using radial basis function neural networks for spatial interpolation to achieve three-dimensional inversion and reconstruction of the signal field pattern, thereby realizing a complete three-dimensional spatial signal field pattern evaluation for MLS. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for flight verification and evaluation of microwave landing systems. It employs a radial basis function neural network, utilizing collected sparse data as training samples to perform high-precision interpolation in three-dimensional space, thereby achieving three-dimensional inversion and reconstruction of the signal field pattern and improving the accuracy and efficiency of flight verification and evaluation of microwave landing systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for flight verification and evaluation of a microwave landing system includes the following steps:

[0008] Step 1: Plan the flight path of the calibration aircraft according to the coverage of the ground equipment of the microwave landing system. The calibration aircraft should be equipped with a Global Navigation Satellite System (GNSS) receiver, an MLS receiver, and a precision ranging device (DME / P).

[0009] Step 2: Verify that the aircraft is flying along the planned route and send real-time satellite navigation positioning information and microwave landing system positioning information to the MLS flight verification data processing unit;

[0010] Step 3: The MLS flight verification data processing unit performs coordinate transformation on the received microwave landing system positioning information, converting the MLS positioning results and the satellite navigation reference positioning results to the same coordinate system;

[0011] Step 4: The MLS positioning information and satellite navigation reference positioning information in the same coordinate system in Step 3 are filtered to obtain the channel following noise error component and the control motion noise error component, and the spatial signal quality of MLS is evaluated accordingly.

[0012] Step 5: After the flight is completed, the error component data obtained from the flight test is inverted and reconstructed to obtain the distribution of the error data in the entire three-dimensional space;

[0013] Step 6: Evaluate the MLS signal in three-dimensional space in accordance with the signal tolerance requirements in space.

[0014] Furthermore, in step 1, the MLS ground equipment radiates radio signals to verify that the aircraft obtains real-time position information, including azimuth, elevation, and distance information, through the MLS receiver and precision ranging equipment; and obtains position information output by satellite navigation, including longitude, latitude, and altitude information, through the GNSS receiver, and uses it as a spatial position reference.

[0015] Furthermore, the pseudorange positioning observation equation for satellite navigation and positioning is:

[0016]

[0017] In the formula: Let X, Y, and Z be the three-dimensional coordinates of the satellite at time i; X, Y, and Z be the coordinates of the ground receiver to be determined; c be the speed of light; and δt be the velocity of light. k For receiver clock bias correction; ρ i The pseudorange measurement value at time i; and These represent ionospheric and tropospheric corrections, respectively; δt i To correct for satellite clock bias.

[0018] Furthermore, in step 2, the MLS receiver should continuously track the radio signals of the MLS ground equipment and output the MLS position information at a fixed frequency in combination with the DME / P response frequency, wherein the refresh rate of the satellite navigation positioning information needs to be higher than this fixed frequency.

[0019] Furthermore, in step 3, the MLS flight verification data processing unit converts the MLS positioning results and the satellite navigation reference positioning results into a station-centered coordinate system with the runway landing point as the origin.

[0020] Furthermore, in step 4, the MLS position information of the aircraft at the sampling time and the satellite navigation reference position information are subtracted, and the error components are removed by filtering the output.

[0021] Furthermore, in step 5, the specific process of data inversion and reconstruction is as follows: Based on the data characteristics, a radial basis function neural network model is established, and the error data obtained at different locations during the flight test is used as training samples, thereby interpolating and reconstructing the data of unknown points in space.

[0022] Furthermore, in step 6, the data after interpolation and reconstruction in step 5 is compared with the tolerance requirements in the MLS flight calibration international standard document, and the abnormal locations and related information that exceed the tolerance requirements are recorded, and they are marked in the three-dimensional space map.

[0023] The beneficial effects of this invention compared to the prior art are as follows:

[0024] This invention is based on sampling sparse data samples and uses a radial basis function neural network to realize the inversion and reconstruction of the three-dimensional spatial signal field of MLS, thereby realizing the MLS flight verification and evaluation.

[0025] (1) Improve the accuracy of MLS flight verification assessment. Achieve high-precision spatial interpolation over a wide range to reduce the possibility of missed spatial signals.

[0026] (2) Improve the efficiency of MLS flight verification and evaluation. Reduce the testing costs caused by a large number of flight tests, and achieve evaluation of the complete spatial signal field pattern of MLS based on a small amount of sampled data. Attached Figure Description

[0027] Figure 1 A schematic diagram of a filter configuration for error component stripping;

[0028] Figure 2 This is a diagram of the topology of a radial basis function neural network.

[0029] Figure 3 This is a flowchart of the microwave landing system flight verification and evaluation method of the present invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0031] like Figure 3 As shown, this invention proposes a flight verification and evaluation method for a microwave landing system, comprising the following steps:

[0032] Step 1: The calibration aircraft, equipped with a Global Navigation Satellite System (GNSS) receiver, an MLS receiver, and a precision ranging device (DME / P), conducts flight tests along the planned route.

[0033] The calibration aircraft obtains real-time position information, including azimuth, elevation, and distance information, through an MLS receiver and precision ranging equipment; and obtains position information output by satellite navigation, including longitude, latitude, and altitude information, through a GNSS receiver, which serves as a spatial position reference.

[0034] The pseudorange positioning observation equation for satellite navigation is:

[0035]

[0036] In the formula: Let X, Y, and Z be the three-dimensional coordinates of the satellite at time i; X, Y, and Z be the coordinates of the ground receiver to be determined; c be the speed of light; and δt be the velocity of light. k For receiver clock bias correction; ρ i The pseudorange measurement value at time i; and These represent ionospheric corrections and tropospheric corrections, respectively; δt i To correct for satellite clock bias.

[0037] Step 2: Verify that during flight, the aircraft sends real-time satellite navigation positioning information and microwave landing system positioning information to the MLS flight verification data processing unit.

[0038] The MLS receiver should continuously track the radio signals of the MLS ground equipment and output MLS position information at a fixed frequency in conjunction with the DME / P response frequency. The refresh rate of the satellite navigation positioning information needs to be higher than this fixed frequency. If the calibration aircraft deviates from the preset route during flight testing, the flight path should be corrected in a timely manner to ensure that the calibration aircraft completes the test on the planned route.

[0039] Step 3: The MLS flight calibration data processing unit performs coordinate transformation on the received microwave landing system positioning information, converting the MLS positioning results and the satellite navigation reference positioning results to the same coordinate system. Considering that the calculations of flight path following error and control motion noise are based on flight path solutions, both positioning information are transformed to a station-centered coordinate system with the runway landing point as the origin. The specific process is as follows:

[0040] Step 3.1: Establish a Cartesian coordinate system with the landing point as the origin, and solve for the aircraft's MLS coordinates. The X-axis of the coordinate system lies on the extended line of the runway center, with negative values ​​indicating the direction towards the end of the runway. Let the coordinates of the azimuth antenna phase center, elevation antenna phase center, and ranging antenna phase center in the Cartesian coordinate system be (X... A ,Y A Z A ), (X E ,Y E Z E ), (X D ,Y D Z D ), to verify the aircraft's coordinate position using (X) T ,Y T Z T (This is represented by a symbol). The azimuth angle of the aircraft relative to the azimuth antenna is known. The elevation angle θ of the aircraft relative to the elevation antenna and the distance ρ of the aircraft relative to the ranging antenna can be established by the following equations:

[0041]

[0042]

[0043]

[0044] The coordinate position (X) of the aircraft is solved by employing the rotating Gauss-Gande iterative algorithm. T ,Y T Z T ).

[0045] Step 3.2: Convert the aircraft coordinates from MLS coordinates to a station-centered coordinate system. This is achieved by rotating the Cartesian coordinate system from Step 3.1 by γ (the true north angle of the runway) around the Z-axis to obtain the station-centered coordinate system with the landing point as the origin. The specific transformation relationship is as follows:

[0046]

[0047] This determines the aircraft's coordinates (X) in the MLS coordinate system. T ,Y T Z T Convert to the station-centered coordinate system (Δe, Δn, Δu)

[0048] Step 3.3: First, the aircraft's satellite navigation positioning information is converted from the geodetic coordinate system to the geocentric coordinate system. The transformation formula from the geodetic coordinate system (φ, λ, h) to the geocentric coordinate system (x, y, z) in satellite navigation positioning calculations is as follows:

[0049] x=(N+h)cosφcosλ (6)

[0050] y=(N+h)cosφsinλ (7)

[0051] z=[N(1-e 2 )+h]sinφ (8)

[0052] In the formula: φ is latitude; λ is longitude; h is altitude; N is the radius of curvature of the mahogany of the reference ellipsoid; e is the eccentricity of the ellipsoid. This yields the coordinates (x, y, z) of the aircraft's satellite navigation positioning information in the geocentric coordinate system.

[0053] Step 3.4: Transform the geocentric coordinate system from Step 3.4 to the station-centered coordinate system with the runway landing point as the origin. Let the geocentric coordinate position of the runway landing point be (x0, y0, z0), and the position in the geodetic coordinate system be (φ0, λ0, h0). Then the coordinate transformation relationship of (x, y, z) relative to the station center is as follows:

[0054]

[0055]

[0056] This yields the aircraft's satellite navigation positioning information in the station-centered coordinate system with the runway landing point as the origin (e, n, u), thus enabling the conversion of MLS positioning results and satellite navigation reference positioning results to the same coordinate system.

[0057] Step 4: The MLS positioning information and satellite navigation reference positioning information obtained in Step 3 in the same coordinate system are filtered to obtain the channel following noise error component and the control motion noise error component, and the spatial signal quality of MLS is evaluated accordingly.

[0058] The aircraft's MLS position information and satellite navigation reference position information at the sampling time are subtracted, and the result is filtered to remove error components. The specific filter configuration is as follows: Figure 1 .

[0059] Step 5: After the flight, the error component data obtained from the flight test are inverted and reconstructed to obtain the distribution of the error data in the entire three-dimensional space. The specific process is as follows:

[0060] Step 5.1, based on the characteristics of the MLS three-dimensional field, define the input layer of the RBFNN radial basis function neural network topology as three nodes, corresponding to the coordinate values ​​P of points on the surface. i (x, y, z), the output layer is a point P on the surface corresponding to a node. i The check parameter values ​​corresponding to (x, y, z). Its network topology model is as follows: Figure 2 .

[0061] Step 5.2: Select the Gaussian function as the basis function and use the collected sparse sample data points as training samples. Obtain the validation parameter value y0 at any point in the space:

[0062]

[0063] In the formula: m is the number of nodes in the hidden layer, w i To construct the network's weight vector: W = [w1, w2, ... w m ], h i For the selected radial basis functions:

[0064]

[0065] In the formula: X is the input vector, C i Let b be the center vector of the i-th hidden layer neuron. i Let be the base width of the neuron node, and exp() denote the exponential function with the natural constant e as the base.

[0066] Based on the raw sparse data collected by the receiver, the parameter estimates at unknown spatial points can be obtained using the radial basis function neural network interpolation method. The MLS flight calibration and evaluation method proposed in this invention can calculate the calibration parameters of MLS at any location (x, y, z) within the MLS coverage area, such as flight path following error and control motion noise, thereby achieving a complete spatial signal field pattern evaluation of MLS.

[0067] Step 6: Evaluate the MLS signal in three-dimensional space in accordance with the signal tolerance requirements in space.

[0068] The data after interpolation and reconstruction in step 5 is compared with the tolerance requirements in the MLS flight calibration international standard document. The abnormal locations that exceed the tolerance requirements and related information are recorded, and they are marked in the three-dimensional space map.

[0069] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes will be obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A method for flight verification and evaluation of a microwave landing system, characterized in that, Includes the following steps: Step 1: Plan the flight path of the calibration aircraft based on the coverage area of ​​the microwave landing system ground equipment. The calibration aircraft is equipped with a GNSS receiver, an MLS receiver, and a precision ranging device; MLS stands for Microwave Landing System. Step 2: Verify that the aircraft is flying along the planned route and send real-time satellite navigation positioning information and microwave landing system positioning information to the MLS flight verification data processing unit; Step 3: The MLS flight verification data processing unit performs coordinate transformation on the received microwave landing system positioning information, converting the MLS positioning results and the satellite navigation reference positioning results to the same coordinate system; Step 4: The MLS positioning information and satellite navigation reference positioning information in the same coordinate system in Step 3 are filtered to obtain the channel following noise error component and the control motion noise error component, and the spatial signal quality of MLS is evaluated accordingly. Step 5: After the flight is completed, the error component data obtained from the flight test is inverted and reconstructed to obtain the distribution of the error data in the entire three-dimensional space; Step 6: Evaluate the MLS signal in three-dimensional space in accordance with the signal tolerance requirements in space.

2. The method for flight verification and evaluation of a microwave landing system according to claim 1, characterized in that: In step 1, the MLS ground equipment radiates radio signals to verify that the aircraft obtains real-time position information, including azimuth, elevation and distance information, through the MLS receiver and precision ranging equipment. The azimuth information output by satellite navigation is obtained through a GNSS receiver, including longitude, latitude, and altitude information, and used as a spatial position reference.

3. The method for flight verification and evaluation of a microwave landing system according to claim 2, characterized in that: The pseudorange positioning observation equation for satellite navigation is: ； In the formula: , respectively The three-dimensional coordinates of the satellite at any given time; These are the coordinates of the ground receiver to be determined; The speed of light; Correction for receiver clock bias; for Time pseudorange measurement value; and These represent corrections for the ionosphere and troposphere, respectively. To correct for satellite clock bias.

4. The method for flight verification and evaluation of a microwave landing system according to claim 1, characterized in that: In step 2, the MLS receiver continuously tracks the radio signals of the MLS ground equipment and outputs the MLS position information at a fixed frequency in combination with the response frequency of the precision ranging equipment, wherein the refresh rate of the satellite navigation positioning information is higher than the fixed frequency.

5. The method for flight verification and evaluation of a microwave landing system according to claim 1, characterized in that: In step 3, the MLS flight verification data processing unit converts the MLS positioning results and the satellite navigation reference positioning results into a station-centered coordinate system with the runway landing point as the origin.

6. The method for flight verification and evaluation of a microwave landing system according to claim 1, characterized in that: In step 4, the MLS position information of the aircraft at the sampling time is subtracted from the satellite navigation reference position information, and the error component is removed by filtering the output.

7. The method for flight verification and evaluation of a microwave landing system according to claim 1, characterized in that: In step 5, the specific process of data inversion and reconstruction is as follows: based on the data characteristics, a radial basis function neural network model is established, and the error data obtained at different locations during the flight test is used as training samples, and then the data of unknown points in space is interpolated and reconstructed.

8. The method for flight verification and evaluation of a microwave landing system according to claim 1, characterized in that: In step 6, the data after inversion and reconstruction in step 5 is compared with the tolerance requirements in the MLS flight calibration international standard document, and the abnormal locations and related information that exceed the tolerance requirements are recorded and marked in the three-dimensional space map.

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