ILS VOR Navigation Station Flight Calibration Processing Method under Wideband and High-Noise Conditions

Abstract

The present invention relates to the field of aviation navigation systems, and discloses a flight verification processing method for an ILSVOR navigation station under wide-band large-noise conditions, including: constructing flight trajectories of an aircraft at multiple altitude levels, and calculating the coverage coefficient of the flight trajectories at multiple altitude levels for the corresponding verification area of the ILSVOR navigation station; collecting the transmitted signals of the ILSVOR navigation station by using a high-precision receiver, separating the noise of the transmitted signals by using a noise separation model to obtain navigation signals; reconstructing the navigation signals to obtain reconstructed navigation signals; analyzing the filtering parameters of the reconstructed navigation signals, filtering the reconstructed navigation signals to obtain filtered navigation signals, and fusing the filtered navigation signals with pre-collected inertial navigation system data to obtain high-precision navigation signals; calculating the signal error of the high-precision navigation signals, and verifying the high-precision navigation signals to obtain target navigation signals. The present invention can improve the accuracy of the navigation information of the ILSVOR navigation station.

Description

Technical Field

[0001] The present invention relates to a method for processing the flight inspection of an ILSVOR navigation station under wide - band and high - noise conditions, belonging to the field of aviation navigation systems. Background Art

[0002] The flight inspection of an ILSVOR navigation station refers to a series of data processing and analysis work for the flight inspection of an Instrument Landing System (ILS) and a Very High Frequency Omnidirectional Range (VOR) navigation station. The purpose is to verify whether the signals of the navigation station meet the requirements of the International Civil Aviation Organization (ICAO) and relevant standards, and ensure that it can provide accurate and reliable navigation services for aircraft.

[0003] The flight inspection of an ILSVOR navigation station usually relies on an aircraft equipped with specific instruments (such as an ILS receiver) to fly along a predetermined flight trajectory for actual flight to directly measure the accuracy and coverage of navigation signals. This method often lacks in - depth analysis of signal processing in a wide - band and high - noise environment, and cannot effectively separate and filter out noise, resulting in large signal errors, thus making the flight inspection of the ILSVOR navigation station inaccurate. Summary of the Invention

[0004] The present invention provides a method for processing the flight inspection of an ILSVOR navigation station under wide - band and high - noise conditions, and its main purpose is to improve the accuracy of the navigation information of the ILSVOR navigation station.

[0005] To achieve the above - mentioned purpose, a method for processing the flight inspection of an ILSVOR navigation station under wide - band and high - noise conditions provided by the present invention includes:

[0006] Determine the aircraft of the ILSVOR navigation station, analyze the sensitivity value of the high - precision receiver corresponding to the aircraft, based on the sensitivity value, construct the multi - altitude flight trajectory of the aircraft, and calculate the coverage coefficient of the multi - altitude flight trajectory for the corresponding inspection area of the ILSVOR navigation station;

[0007] According to the coverage coefficient, use the high - precision receiver to collect the transmitted signal of the ILSVOR navigation station, identify the wide - band noise characteristics of the transmitted signal, and through the wide - band noise characteristics, use a pre - trained noise separation model to separate the noise from the transmitted signal to obtain a navigation signal;

[0008] Construct an observation matrix of the navigation signal, and based on the observation matrix, reconstruct the navigation signal to obtain a reconstructed navigation signal;

[0009] Analyze the filtering parameters of the reconstructed navigation signal using a preset intelligent optimization algorithm, filter the reconstructed navigation signal through the filtering parameters to obtain a filtered navigation signal, and fuse the filtered navigation signal with the pre-collected inertial navigation system data to obtain a high-precision navigation signal;

[0010] Calculate the signal error of the high-precision navigation signal, and based on the signal error, verify the high-precision navigation signal to obtain a target navigation signal.

[0011] Optionally, the analyzing the sensitivity value of the high-precision receiver corresponding to the aircraft includes:

[0012] Construct a non-interference environment for the high-precision receiver;

[0013] Determine the signal source of the high-precision receiver corresponding to the ILSVOR navigation station;

[0014] Define the test intensity gradient of the signal source;

[0015] Analyze the intensity demodulation coefficient of the test intensity gradient;

[0016] Determine the sensitivity value of the high-precision receiver according to the intensity demodulation coefficient.

[0017] Optionally, the determining the sensitivity value of the high-precision receiver according to the intensity demodulation coefficient includes:

[0018] Determine the demodulation threshold of the high-precision receiver;

[0019] Analyze the minimum detectable signal of the high-precision receiver according to the intensity demodulation coefficient;

[0020] According to the demodulation threshold and the minimum detectable signal, calculate the sensitivity value of the high-precision receiver using the following formula:

[0021]

[0022] Where, represents the sensitivity value of the high-precision receiver, represents the bandwidth of the high-precision receiver, represents the noise generated by the bandwidth of the high-precision receiver, represents the noise factor, represents the demodulation threshold, represents the noise floor of the high-precision receiver, represents the minimum detectable signal.

[0023] Optionally, the constructing the multi-altitude flight trajectory of the aircraft includes:

[0024] Obtain the flight area map of the aircraft;

[0025] Mark the obstacles and dangerous areas in the flight area map;

[0026] Define the test requirements of the aircraft;

[0027] Analyze the multi - altitude layers of the aircraft according to the test requirements;

[0028] Based on the obstacles, the dangerous areas, and the multi - altitude layers, determine the flight parameters of the aircraft, where the flight parameters include the take - off point, turning points, multi - altitude layer transition points, and multi - altitude layer landing points;

[0029] Determine the multi - altitude layer flight trajectory of the aircraft through the flight parameters.

[0030] Optionally, the calculating the coverage coefficient of the multi - altitude layer flight trajectory for the corresponding calibration area of the ILSVOR navigation station includes:

[0031] Determine the navigation station parameters of the ILSVOR navigation station;

[0032] Determine the flight trajectory parameters of the multi - altitude layer flight trajectory;

[0033] Construct a radio propagation model of the multi - altitude layer flight trajectory;

[0034] Based on the navigation station parameters and the flight trajectory parameters, analyze the effective coverage area of the multi - altitude layer flight trajectory using the radio propagation model;

[0035] Based on the effective coverage area, analyze the coverage coefficient of the multi - altitude layer flight trajectory for the corresponding calibration area of the ILSVOR navigation station

[0036] Optionally, the using a pre - trained noise separation model to separate the noise from the transmitted signal to obtain a navigation signal includes:

[0037] Construct a time - frequency diagram of the transmitted signal;

[0038] Normalize the time - frequency diagram to obtain a normalized time - frequency diagram;

[0039] Use the noise separation model to output the noise probability diagram and the signal probability diagram of the normalized time - frequency diagram;

[0040] Separate the noise from the transmitted signal through the noise probability diagram and the signal probability diagram to obtain a navigation signal.

[0041] Optionally, the constructing the observation matrix of the navigation signal includes:

[0042] Analyze the observation matrix indices of the navigation signal;

[0043] Determine the sparse basis of the navigation signal according to the observation matrix indices;

[0044] Analyze the sparsity of the navigation signal through the sparse basis;

[0045] Construct the observation matrix of the navigation signal based on the sparsity;

[0046] Optionally, the analyzing the sparsity of the navigation signal through the sparse basis includes:

[0047] Convert the navigation signal according to the sparse basis to obtain a sparse navigation signal;

[0048] Identify the signal length of the sparse navigation signal;

[0049] Define the indicator function of the sparse navigation signal;

[0050] Based on the signal length and the indicator function, calculate the number of non-zero elements of the sparse navigation signal:

[0051]

[0052] where, represents the number of non-zero elements, represents the th element of the sparse navigation signal, represents the signal length of the sparse navigation signal, represents the indicator function;

[0053] Determine the sparsity of the navigation signal according to the number of non-zero elements.

[0054] Optionally, the analyzing the filtering parameters of the reconstructed navigation signal using a preset intelligent optimization algorithm includes:

[0055] Determine the filtering objective and filter type of the reconstructed navigation signal;

[0056] Identify the initial filtering parameters of the filter type;

[0057] Construct a filter parameter group of the initial filtering parameters;

[0058] According to the filtering objective, analyze the filtering performance of the filter parameter group using the intelligent optimization algorithm;

[0059] Determine the filtering parameters of the reconstructed navigation signal based on the filtering performance.

[0060] Optionally, calculating the signal error of the high-precision navigation signal includes:

[0061] Analyzing the position PVT solution of the high-precision navigation signal;

[0062] Defining a reference benchmark for the high-precision navigation signal;

[0063] Based on the reference benchmark, analyzing and calculating the position error of the position PVT solution;

[0064] Determining the signal error of the high-precision navigation signal according to the position error.

[0065] To solve the above problems, the present invention also provides an electronic device, which includes:

[0066] At least one processor; and,

[0067] A memory communicatively connected to the at least one processor; wherein,

[0068] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned ILSVOR navigation station flight verification processing method under wide-band large-noise conditions.

[0069] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned ILSVOR navigation station flight verification processing method under wide-band large-noise conditions.

[0070] Compared with the problems described in the background art, firstly, by analyzing the sensitivity value of the high-precision receiver corresponding to the aircraft and constructing flight trajectories at multiple altitude levels, it is possible to ensure that flight calibration covers all important levels of the calibration area corresponding to the navigation station, improving the comprehensiveness and accuracy of calibration. The calculation of the coverage coefficient makes the planning of flight trajectories more scientific and reasonable, ensuring the uniformity and effectiveness of signal acquisition. Secondly, by using a high-precision receiver to collect transmitted signals and identify the characteristics of wideband noise, combined with a pre-trained noise separation model, it is possible to effectively separate noise from the transmitted signals, improving the quality of navigation signals. This method is particularly effective in an environment with wideband large noise, significantly enhancing the credibility of the signals. Furthermore, by constructing an observation matrix to reconstruct navigation signals and analyzing filtering parameters in combination with an intelligent optimization algorithm, it is possible to achieve precise filtering of the reconstructed navigation signals. This method not only improves the signal processing efficiency but also reduces signal errors, enhancing the stability of the navigation system. Finally, by fusing the filtered navigation signals with the data of the inertial navigation system, high-precision navigation signals are obtained, and calibration is performed by calculating signal errors, ultimately obtaining the target navigation signals. This process greatly improves the navigation accuracy of the navigation system, provides more reliable and accurate navigation information for the aircraft, and ensures flight safety. Therefore, the present invention can improve the accuracy of the navigation information of the ILSVOR navigation station. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 FIG. is a schematic flowchart of a flight calibration processing method for an ILSVOR navigation station under wideband large noise conditions provided by an embodiment of the present invention;

[0072] The implementation, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0074] An embodiment of the present application provides a flight calibration processing method for an ILSVOR navigation station under wideband large noise conditions. The execution subject of the flight calibration processing method for the ILSVOR navigation station under wideband large noise conditions includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the flight calibration processing method for the ILSVOR navigation station under wideband large noise conditions can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. Embodiment

[0075] Refer to Figure 1As shown in the figure, it is a schematic flowchart of the flight inspection processing method for an ILSVOR navigation station under wide - band and high - noise conditions provided by an embodiment of the present invention. In this embodiment, the flight inspection processing method for an ILSVOR navigation station under wide - band and high - noise conditions includes:

[0076] S1. Determine the aircraft of the ILSVOR navigation station, analyze the sensitivity value of the high - precision receiver corresponding to the aircraft, based on the sensitivity value, construct the multi - altitude flight trajectory of the aircraft, and calculate the coverage coefficient of the multi - altitude flight trajectory for the corresponding inspection area of the ILSVOR navigation station.

[0077] It should be explained that the ILSVOR navigation station refers to an aviation navigation facility that provides precision approach guidance. It guides the aircraft to align with the runway centerline and the correct glide path during the final approach stage by transmitting vertical and horizontal navigation signals (i.e., glide slope and localizer signals). The aircraft refers to the flight vehicle for conducting ILSVOR navigation station navigation tests, such as drones, fixed - wing aircraft, or helicopters. The high - precision receiver refers to a receiver that is used to receive the signals transmitted by the ILSVOR navigation station and can analyze these signals with extremely high precision.

[0078] The present invention analyzes the sensitivity value of the high - precision receiver corresponding to the aircraft to improve the reliability of the collected data.

[0079] Specifically, the analysis of the sensitivity value of the high - precision receiver corresponding to the aircraft includes:

[0080] Construct an interference - free environment for the high - precision receiver;

[0081] Determine the signal source of the high - precision receiver corresponding to the ILSVOR navigation station;

[0082] Define the test intensity gradient of the signal source;

[0083] Analyze the intensity demodulation coefficient of the test intensity gradient;

[0084] Determine the sensitivity value of the high - precision receiver according to the intensity demodulation coefficient.

[0085] Among them, the interference - free environment refers to an electromagnetic environment in which there is no or only negligible electromagnetic interference, which can ensure that the test results are only affected by the performance of the receiver and the signal source itself. The signal source refers to a controllable transmitter used to simulate the signals transmitted by the ILSVOR navigation station. The test intensity gradient refers to a series of predefined signal intensity levels used to test the sensitivity of the receiver. The intensity demodulation coefficient refers to the relationship between the signal intensity and the demodulation performance when the receiver demodulates the signal. The sensitivity value refers to the minimum signal intensity required for the receiver to correctly demodulate the signal.

[0086] Further, determining the sensitivity value of the high-precision receiver according to the intensity demodulation coefficient includes:

[0087] Determining the demodulation threshold of the high-precision receiver;

[0088] Analyzing the minimum detectable signal of the high-precision receiver according to the intensity demodulation coefficient;

[0089] Calculating the sensitivity value of the high-precision receiver according to the demodulation threshold and the minimum detectable signal by using the following formula:

[0090]

[0091] Wherein, represents the sensitivity value of the high-precision receiver, represents the bandwidth of the high-precision receiver, represents the noise generated by the bandwidth of the high-precision receiver, represents the noise factor, represents the demodulation threshold, represents the noise floor of the high-precision receiver, represents the minimum detectable signal.

[0092] Wherein, the demodulation threshold refers to the lowest signal-to-noise ratio at which the high-precision receiver demodulator can operate, the minimum detectable signal refers to the minimum signal power that the high-precision receiver can detect, the bandwidth, the noise generated by the bandwidth refers to the noise power caused by the bandwidth of the receiver, the noise factor refers to the amount of additional noise introduced by the receiver when amplifying the signal, and the noise floor refers to the inherent noise level of the high-precision receiver.

[0093] It should be noted that in this application, calculating the sensitivity value of the high-precision receiver through the above formula can determine the signal strength limit of the ILSVOR navigation station, thereby improving the reliability of the ILSVOR navigation station for navigation. In order for the high-precision receiver to correctly demodulate the signal, the input signal power must be greater than or equal to the minimum detectable signal (MDS) plus the demodulation threshold. The demodulation threshold is the additional signal power that the high-precision receiver needs to increase on the basis of MDS to overcome the losses and noises generated during the internal processing process.

[0094] The multi-altitude flight trajectory of the aircraft constructed by the present invention can effectively test the calibration area of the ILSVOR navigation station.

[0095] Specifically, constructing the multi-altitude flight trajectory of the aircraft includes:

[0096] Obtaining the flight area map of the aircraft;

[0097] Mark the obstacles and dangerous areas on the flight area map;

[0098] Define the test requirements of the aircraft;

[0099] Analyze the multi - altitude layers of the aircraft according to the test requirements;

[0100] Based on the obstacles, the dangerous areas and the multi - altitude layers, determine the flight parameters of the aircraft, where the flight parameters include the take - off point, the turning point, the multi - altitude layer transition point and the multi - altitude layer landing point;

[0101] Determine the multi - altitude layer flight trajectory of the aircraft through the flight parameters.

[0102] Among them, the flight area map refers to a geographical information map showing the area where the aircraft will fly. The obstacle refers to any object in the flight area that may affect the safe flight of the aircraft, such as buildings, towers, trees, etc. The dangerous area refers to an area in the flight area that may pose a threat to the aircraft, such as a high electromagnetic interference area, a military restricted area, a restricted airspace around an airport, etc. The test requirement refers to the specific objectives and technical requirements of the flight mission, including the performance test standards of the receiver, the types of data to be collected, the flight altitude, the flight speed, the flight time and other requirements. The multi - altitude layer refers to different altitudes at which the aircraft needs to fly when performing a mission. The take - off point refers to the location where the aircraft starts to fly. The turning point refers to a predetermined point in the flight trajectory where the aircraft changes direction. The multi - altitude layer transition point refers to a predetermined point where the aircraft transitions from one altitude layer to another. The multi - altitude layer landing point refers to the location where the aircraft lands after completing all the predetermined altitude layer flights. The multi - altitude layer flight trajectory refers to the predetermined flight path of the aircraft during the entire mission, including the paths of take - off, flight at different altitude layers, turning, transition and landing and other all stages.

[0103] Optionally, the obstacles and dangerous areas marked on the flight area map can be marked with different symbols and colors on the map, and annotations can be added to the marked obstacles and dangerous areas to explain their nature, height, radius and other information.

[0104] Calculating the coverage coefficient of the multi - altitude layer flight trajectory of the present invention for the corresponding calibration area of the ILSVOR navigation station can ensure the reliability of the test data.

[0105] Specifically, calculating the coverage coefficient of the multi - altitude layer flight trajectory for the corresponding calibration area of the ILSVOR navigation station includes:

[0106] Determine the navigation station parameters of the ILSVOR navigation station;

[0107] Determine the flight trajectory parameters of the multi-altitude layer flight trajectory;

[0108] Construct a radio propagation model for the multi-altitude layer flight trajectory;

[0109] Based on the navigation station parameters and the flight trajectory parameters, use the radio propagation model to analyze the effective coverage area of the multi-altitude layer flight trajectory;

[0110] Based on the effective coverage area, analyze the coverage coefficient of the multi-altitude layer flight trajectory for the corresponding calibration area of the ILSVOR navigation station.

[0111] Wherein, the navigation station parameters refer to the parameters of the technical specifications related to the performance of the ILSVOR navigation station, such as frequency, transmission power and other parameters, the flight trajectory parameters refer to the data related to the flight path of the aircraft, the radio propagation model refers to the model used to predict the propagation characteristics and coverage range of radio waves in space, the effective coverage area refers to the area where the radio signal can provide sufficient strength to support the navigation service under specific conditions, the calibration area refers to an area set to ensure the normal operation of the navigation station, and the coverage coefficient refers to the parameter used to represent the overlapping degree between the effective coverage area of the flight trajectory and the calibration area of the ILSVOR navigation station.

[0112] Optionally, the radio propagation model for constructing the multi-altitude layer flight trajectory can be trained by using a large amount of historical flight trajectory parameter data through the free space propagation model.

[0113] S2. According to the coverage coefficient, use the high-precision receiver to collect the transmission signal of the ILSVOR navigation station, identify the broadband noise characteristics of the transmission signal, and through the broadband noise characteristics, use the pre-trained noise separation model to separate the noise of the transmission signal to obtain the navigation signal.

[0114] It should be explained that the transmission signal refers to the radio signal emitted by the ILSVOR navigation station for providing navigation information, and the broadband noise characteristics refer to the noise characteristics within the broadband frequency range in the transmission signal of the ILSVOR navigation station collected by the receiver, such as noise power, noise spectrum and other characteristics.

[0115] In the present invention, through the broadband noise characteristics, using the pre-trained noise separation model to separate the noise of the transmission signal to obtain the navigation signal can improve the quality of the navigation signal.

[0116] Specifically, the noise separation of the transmitted signal by using the pre-trained noise separation model to obtain the navigation signal includes:

[0117] Construct a time-frequency diagram of the transmitted signal;

[0118] Normalize the time-frequency diagram to obtain a normalized time-frequency diagram;

[0119] Use the noise separation model to output the noise probability diagram and the signal probability diagram of the normalized time-frequency diagram;

[0120] Perform noise separation on the transmitted signal through the noise probability diagram and the signal probability diagram to obtain the navigation signal.

[0121] Among them, the time-frequency diagram refers to the conversion of a one-dimensional time-domain signal into a two-dimensional time-frequency domain representation through time-frequency analysis (such as STFT, wavelet transform). The normalized time-frequency diagram refers to the time-frequency diagram after standardizing the original time-frequency diagram. The noise probability diagram refers to a two-dimensional matrix in which the time-frequency units in the normalized time-frequency diagram belong to the noise probability. The signal probability diagram refers to a two-dimensional matrix in which the time-frequency units in the normalized time-frequency diagram belong to the navigation signal probability. The navigation signal refers to the pure signal after time-frequency mask processing.

[0122] Optionally, the construction of the time-frequency diagram of the transmitted signal can be converted by short-time Fourier transform.

[0123] S3. Construct an observation matrix of the navigation signal, and reconstruct the navigation signal based on the observation matrix to obtain a reconstructed navigation signal.

[0124] The construction of the observation matrix of the navigation signal in the present invention provides a data basis for the subsequent reconstruction of the navigation signal.

[0125] Specifically, the construction of the observation matrix of the navigation signal includes:

[0126] Analyze the observation matrix index of the navigation signal;

[0127] Determine the sparse basis of the navigation signal according to the observation matrix index;

[0128] Analyze the sparsity of the navigation signal through the sparse basis;

[0129] Construct an observation matrix of the navigation signal based on the sparsity.

[0130] Among them, the observation matrix index refers to some metrics used to evaluate the performance of the observation matrix, the sparse basis refers to a set of basis functions that can represent a signal as a small number of non-zero coefficients (sparse representation), the sparsity refers to the number of non-zero coefficients when the signal is represented under the sparse basis, and the observation matrix refers to a matrix that maps a high-dimensional sparse signal or compressible signal to a low-dimensional space.

[0131] Further, analyzing the sparsity of the navigation signal through the sparse basis includes:

[0132] Converting the navigation signal according to the sparse basis to obtain a sparse navigation signal;

[0133] Identifying the signal length of the sparse navigation signal;

[0134] Defining an indicator function for the sparse navigation signal;

[0135] Based on the signal length and the indicator function, calculating the number of non-zero elements of the sparse navigation signal:

[0136]

[0137] Among them, represents the number of non-zero elements, represents the th element of the sparse navigation signal, represents the signal length of the sparse navigation signal, represents the indicator function;

[0138] Determining the sparsity of the navigation signal according to the number of non-zero elements.

[0139] Among them, the sparse navigation signal refers to the navigation signal after conversion by the sparse basis, the signal length refers to the total number of elements in the sparse navigation signal, the indicator function refers to a function used to identify non-zero elements in the sparse navigation signal, the number of non-zero elements refers to the total number of non-zero elements in the sparse navigation signal, and the sparsity refers to the ratio of the number of non-zero elements to the signal length.

[0140] It should be noted that in this application, calculating the number of non-zero elements of the sparse navigation signal through the above formula can evaluate the quality and integrity of the signal.

[0141] Based on the observation matrix, the present invention reconstructs the navigation signal to obtain a reconstructed navigation signal, which can further improve the signal quality of the navigation signal, thereby improving the accuracy of the subsequent ILSVOR navigation station flight inspection. Among them, the reconstructed navigation signal refers to the original navigation signal recovered from compressed or undersampled observation data through a mathematical algorithm. Specifically, the compressed sampling matching pursuit (CoSaMP) of the navigation signal reconstructs the signal by iteratively selecting and updating the columns of the observation matrix.

[0142] S4. Analyze the filtering parameters of the reconstructed navigation signal using a preset intelligent optimization algorithm, filter the reconstructed navigation signal through the filtering parameters to obtain a filtered navigation signal, and fuse the filtered navigation signal with the pre-collected inertial navigation system data to obtain a high-precision navigation signal.

[0143] The present invention analyzes the filtering parameters of the reconstructed navigation signal using a preset intelligent optimization algorithm to improve the processing quality of the navigation signal and the overall performance of the system.

[0144] Specifically, the analyzing the filtering parameters of the reconstructed navigation signal using a preset intelligent optimization algorithm includes:

[0145] Determine the filtering objective and filter type of the reconstructed navigation signal;

[0146] Identify the initial filtering parameters of the filter type;

[0147] Construct a filtering parameter group of the initial filtering parameters;

[0148] According to the filtering objective, analyze the filtering performance of the filtering parameter group using the intelligent optimization algorithm;

[0149] Based on the filtering performance, determine the filtering parameters of the reconstructed navigation signal.

[0150] Among them, the filtering objective refers to the specific purpose expected to be achieved through the filtering process, such as reducing noise, eliminating multipath effects, improving signal quality, etc. The filter type refers to the mathematical model used to process signals, such as FIR (finite impulse response) filter, IIR (infinite impulse response). The initial filtering parameters refer to a set of preset parameter values of the filter before the start of the optimization process. The filtering parameter group refers to a set of parameter collections used to configure the filter. The filtering performance refers to the effect of the filter in achieving the filtering objective. The filtering parameters refer to the finally determined set of parameters, and the filtering parameters include the cut-off frequency, filter order, and filter coefficients.

[0151] Optionally, the analyzing the filtering performance of the filtering parameter group using the intelligent optimization algorithm can be implemented through a particle swarm optimization (PSO) algorithm.

[0152] It should be noted that the filtered navigation signal refers to the signal obtained by filtering the reconstructed navigation signal through filtering parameters.

[0153] The present invention fuses the filtered navigation signal and the pre-collected inertial navigation system data to obtain a high-precision navigation signal, which can provide more accurate and reliable navigation data. Among them, the inertial navigation system data is a series of information collected by the inertial navigation system, such as acceleration data, angular velocity data, attitude data, etc. The high-precision navigation signal refers to the navigation information obtained by fusing data from different navigation systems (such as GPS, GLONASS, Beidou, etc.). Specifically, the fusion of the filtered navigation signal and the pre-collected inertial navigation system data to obtain a high-precision navigation signal can be performed through an extended Kalman filter fusion algorithm.

[0154] S5. Calculate the signal error of the high-precision navigation signal, and based on the signal error, verify the high-precision navigation signal to obtain a target navigation signal.

[0155] Calculating the signal error of the high-precision navigation signal in the present invention can serve as the data basis for later signal verification, thereby improving the signal quality of the high-precision navigation signal.

[0156] Specifically, the calculation of the signal error of the high-precision navigation signal includes:

[0157] Analyze the position PVT solution of the high-precision navigation signal;

[0158] Define the reference benchmark of the high-precision navigation signal;

[0159] Based on the reference benchmark, analyze and calculate the position error of the position PVT solution;

[0160] According to the position error, determine the signal error of the high-precision navigation signal.

[0161] Among them, the position PVT solution refers to the carrier position calculation result obtained through navigation signal processing, usually including three coordinate values of latitude, longitude, and altitude. The reference benchmark refers to a known and accurate position standard used to compare and evaluate the navigation signal calculation result. The position error refers to the difference between the carrier position obtained through navigation signal processing and the reference benchmark position. The signal error refers to a comprehensive index used to evaluate the overall accuracy and reliability of the navigation signal.

[0162] Finally, based on the signal error, the high-precision navigation signal is verified to obtain a target navigation signal, which can effectively verify the high-precision navigation signal, improve its accuracy and reliability, and finally obtain a navigation signal for operation. The target navigation signal refers to a navigation signal that meets the predetermined accuracy and reliability requirements after a series of error analysis and verification processes. Specifically, the verification of the high-precision navigation signal based on the signal error can be performed by a sequence estimation verification method.

[0163] First, by analyzing the sensitivity value of the high-precision receiver corresponding to the aircraft and constructing a flight trajectory with multiple altitude layers, it can ensure that the flight verification covers all important levels of the verification area corresponding to the navigation station, improve the comprehensiveness and accuracy of the verification, and the calculation of the coverage coefficient makes the planning of the flight trajectory more scientific and reasonable, ensuring the uniformity and effectiveness of signal acquisition. Secondly, by using the high-precision receiver to collect the transmitted signal and identify the wideband noise characteristics, combined with the pre-trained noise separation model, the noise can be effectively separated from the transmitted signal, improving the quality of the navigation signal. This method is particularly effective in a wideband large-noise environment and significantly improves the credibility of the signal. Thirdly, by constructing an observation matrix to reconstruct the navigation signal and analyzing the filtering parameters in combination with the intelligent optimization algorithm, precise filtering of the reconstructed navigation signal can be achieved. This method not only improves the signal processing efficiency but also reduces the signal error and enhances the stability of the navigation system. Finally, the filtered navigation signal is fused with the data of the inertial navigation system to obtain a high-precision navigation signal, and the signal error is calculated for verification to finally obtain the target navigation signal. This process greatly improves the navigation accuracy of the navigation system, provides more reliable and accurate navigation information for the aircraft, and ensures flight safety. Therefore, the present invention can improve the accuracy of the navigation information of the ILSVOR navigation station.

Claims

1. A flight inspection processing method for an ILSVOR navigation station under wide-band large-noise conditions, characterized in that, The method includes: Determining an aircraft of an ILSVOR navigation station, analyzing the sensitivity value of a high-precision receiver corresponding to the aircraft, based on the sensitivity value, constructing a multi-altitude flight trajectory of the aircraft, and calculating a coverage coefficient of the multi-altitude flight trajectory for a calibration area corresponding to the ILSVOR navigation station, wherein calculating the coverage coefficient of the multi-altitude flight trajectory for the calibration area corresponding to the ILSVOR navigation station includes: determining navigation station parameters of the ILSVOR navigation station, determining flight trajectory parameters of the multi-altitude flight trajectory, constructing a radio propagation model of the multi-altitude flight trajectory, based on the navigation station parameters and the flight trajectory parameters, analyzing an effective coverage area of the multi-altitude flight trajectory by using the radio propagation model, and based on the effective coverage area, analyzing the coverage coefficient of the multi-altitude flight trajectory for the calibration area corresponding to the ILSVOR navigation station; According to the coverage coefficient, using the high-precision receiver to collect a transmitted signal of the ILSVOR navigation station, identifying a wideband noise feature of the transmitted signal, and through the wideband noise feature, separating noise from the transmitted signal by using a pre-trained noise separation model to obtain a navigation signal; Constructing an observation matrix of the navigation signal, and reconstructing the navigation signal based on the observation matrix to obtain a reconstructed navigation signal; Using a preset intelligent optimization algorithm to analyze filtering parameters of the reconstructed navigation signal, filtering the reconstructed navigation signal through the filtering parameters to obtain a filtered navigation signal, and fusing the filtered navigation signal with pre-collected inertial navigation system data to obtain a high-precision navigation signal; Calculating a signal error of the high-precision navigation signal, and based on the signal error, calibrating the high-precision navigation signal to obtain a target navigation signal.

2. The flight inspection processing method of the ILSVOR navigation station under wideband and high-noise conditions as described in claim 1, wherein, The analyzing the sensitivity value of the high-precision receiver corresponding to the aircraft includes: Constructing a non-interference environment for the high-precision receiver; Determining a signal source of the high-precision receiver corresponding to the ILSVOR navigation station; Defining a test intensity gradient of the signal source; Analyzing an intensity demodulation coefficient of the test intensity gradient; According to the intensity demodulation coefficient, determining the sensitivity value of the high-precision receiver.

3. The flight inspection processing method for an ILSVOR navigation station under broadband and high-noise conditions as described in claim 2, characterized in that The according to the intensity demodulation coefficient, determining the sensitivity value of the high-precision receiver includes: Determining a demodulation threshold of the high-precision receiver; According to the intensity demodulation coefficient, analyzing a minimum detectable signal of the high-precision receiver; According to the demodulation threshold and the minimum detectable signal, calculating the sensitivity value of the high-precision receiver by using the following formula: Among them, represents the sensitivity value of the high-precision receiver, represents the bandwidth of the high-precision receiver, represents the noise generated by the bandwidth of the high-precision receiver, represents the noise figure, represents the demodulation threshold, represents the noise floor of the high-precision receiver, represents the minimum detectable signal.

4. The flight inspection processing method of the ILSVOR navigation station under wideband and high-noise conditions as described in claim 3, characterized in that, The constructing the multi-altitude flight trajectory of the aircraft includes: Obtaining a flight area map of the aircraft; Marking obstacles and dangerous areas in the flight area map; Defining test requirements of the aircraft; According to the test requirements, analyzing multi-altitudes of the aircraft. Determine the flight parameters of the aircraft based on the obstacle, the dangerous area, and the multi-height layers, where the flight parameters include a takeoff point, a turning point, a multi-height layer transition point, and a multi-height layer landing point; Determine the multi-height layer flight trajectory of the aircraft based on the flight parameters.

5. The flight inspection processing method of the ILSVOR navigation station under wideband and high-noise conditions as described in claim 4, characterized in that The noise separation of the transmitted signal by using the pre-trained noise separation model to obtain a navigation signal includes: Construct a time-frequency diagram of the transmitted signal; Normalize the time-frequency diagram to obtain a normalized time-frequency diagram; Use the noise separation model to output a noise probability diagram and a signal probability diagram of the normalized time-frequency diagram; Perform noise separation on the transmitted signal through the noise probability diagram and the signal probability diagram to obtain a navigation signal.

6. The flight inspection processing method of the ILSVOR navigation station under wideband and high-noise conditions as described in claim 5, characterized in that The construction of the observation matrix of the navigation signal includes: Analyze the observation matrix indexes of the navigation signal; Determine the sparse basis of the navigation signal according to the observation matrix indexes; Analyze the sparsity of the navigation signal through the sparse basis; Construct the observation matrix of the navigation signal based on the sparsity.

7. The flight inspection processing method for an ILSVOR navigation station under wideband and high-noise conditions according to claim 6, characterized in that The analysis of the sparsity of the navigation signal through the sparse basis includes: Convert the navigation signal according to the sparse basis to obtain a sparse navigation signal; Identify the signal length of the sparse navigation signal; Define the indicator function of the sparse navigation signal. Calculate the number of non-zero elements of the sparse navigation signal based on the signal length and the indicator function: where represents the number of non-zero elements, represents the -th element of the sparse navigation signal, represents the signal length of the sparse navigation signal, represents the indicator function; determine the sparsity of the navigation signal according to the number of non-zero elements.

8. The flight inspection processing method for an ILSVOR navigation station under wideband and high-noise conditions according to claim 7, characterized in that The analysis of the filtering parameters of the reconstructed navigation signal by using a preset intelligent optimization algorithm includes: Determine the filtering objective and filter type of the reconstructed navigation signal; Identify the initial filtering parameters of the filter type; Construct a filtering parameter group of the initial filtering parameters; According to the filtering objective, use the intelligent optimization algorithm to analyze the filtering performance of the filtering parameter group; Determine the filtering parameters of the reconstructed navigation signal based on the filtering performance.

9. The flight inspection processing method of the ILSVOR navigation station under wideband and high-noise conditions according to claim 8, wherein, The calculation of the signal error of the high-precision navigation signal includes: Analyze the position PVT solution of the high-precision navigation signal; Define the reference benchmark of the high-precision navigation signal; Analyze and calculate the position error of the position PVT solution based on the reference benchmark; Determine the signal error of the high-precision navigation signal according to the position error.

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