Motor West-odd aviation navigation network airborne positioning method and motor West-odd aviation navigation network airborne positioning system

Abstract

The invention relates to the technical field of satellite navigation, in particular to a motor West-odd aviation navigation network airborne positioning method and system, and the method comprises the steps: receiving a low-orbit satellite signal through an airborne multi-band phased-array antenna array, carrying out the anti-interference enhancement processing of a self-adaptive beam forming technology, extracting a pseudo-range and pseudo-range rate observation value, and carrying out the calculation of the pseudo-range and pseudo-range rate observation value; and fusing inertial sensor data, and carrying out high-precision pose calculation by adopting volume Kalman filtering and an intelligent optimization algorithm. The system comprises an antenna array, a navigation signal processor, an observation value resolving module, a multi-source data fusion filter and an avionics interface which are connected. The method effectively solves the problems that a traditional navigation system is easily interfered and the precision is reduced in a complex electromagnetic environment, has the characteristics of high anti-interference performance, high positioning precision and high response speed, and is suitable for high-reliability navigation positioning of aviation aircrafts.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and in particular to an airborne positioning method and system for Motor Sich aviation navigation network. Background Technology

[0002] Satellite navigation technology has become an indispensable infrastructure in modern aviation, maritime, aerospace, and civilian fields, providing all-weather, globally covered positioning, navigation, and timing services to various users. It transmits signals to the ground via medium- and high-Earth orbit satellite constellations, and user receivers achieve their own positioning by processing signals from multiple satellites. In recent years, the rapid development of low-Earth orbit communication satellite constellations, such as Starlink, has provided a new approach to communication and navigation integration due to its large number of satellites, low orbital altitude, and high signal strength, and is expected to further improve the accuracy, availability, and reliability of navigation services.

[0003] However, existing navigation systems, especially traditional systems relying on medium- and high-orbit satellites, lack sufficient anti-interference capabilities in complex electromagnetic environments. When aircraft are maneuvering at high speeds or in harsh electromagnetic environments, navigation signals are highly susceptible to intentional or unintentional narrowband and broadband interference, as well as multipath effects, leading to a sharp drop in signal-to-noise ratio, increased positioning errors, and even loss of lock-on. Although some military receivers employ anti-interference techniques, such as frequency domain filtering or airspace nulling, most solutions are computationally complex, have slow response times, and struggle to dynamically adapt to sudden interference patterns. They fail to meet the urgent needs of modern aircraft for high-precision, high-reliability, and low-latency navigation in highly contested environments. Summary of the Invention

[0004] The purpose of this invention is to provide an airborne positioning method and system for Motor Sich aviation navigation network, which solves the problems of insufficient anti-interference capability, sharp decline in positioning accuracy, and even loss of lock in existing aviation navigation systems under complex electromagnetic environments.

[0005] To achieve the above objectives, the present invention provides an airborne positioning method and system for Motor Sich aviation navigation networks, comprising the following steps: The system receives fused navigation and communication signals from multiple low-Earth orbit satellites via an airborne multi-band antenna array. The fused signal is subjected to anti-interference and enhancement processing to extract usable navigation signals; The observations used for positioning are calculated from the processed signal; By integrating the observed values ​​with airborne inertial sensor data, a positioning solution model is constructed; Solve the positioning calculation model to obtain the real-time attitude information of the aircraft; The pose information is transmitted to the airborne avionics system.

[0006] The specific steps for anti-interference and enhancement processing of the fused signal include: The received radio frequency signal is amplified with low noise, down-converted, and converted from analog to digital to obtain a digital intermediate frequency signal; Perform a Fast Fourier Transform on the digital intermediate frequency signal and analyze the signal spectrum to identify the frequency points of interference sources; Based on the identified interference source frequency points, an adaptive beamforming algorithm is used to dynamically calculate the weighting vector of the antenna array; By applying the weighted vector, an antenna pattern null is formed in the direction of the incoming interference signal, while a high-gain main beam is formed in the direction of the target satellite signal, and the anti-interference signal is output.

[0007] The specific steps for constructing the positioning solution model by integrating the observed values ​​with airborne inertial sensor data include: The state equation is established based on inertial measurement unit data, with the aircraft's position, velocity, attitude angle, and receiver clock error as state variables. Using pseudorange and pseudorange rate extracted from satellite signals as observations, a measurement equation is established; Initialize the state vector and covariance matrix of the commutative Kalman filter; Execute filtering recursion, perform time updates and measurement updates, fuse inertial data and satellite observations to obtain the optimal estimated state vector; The process noise covariance matrix and measurement noise covariance matrix of the filter are tuned online using an optimization algorithm.

[0008] The specific steps for extracting the observations used for positioning from the processed signal include: The satellite navigation message is analyzed to obtain the signal transmission time, and the time difference is calculated by measuring the signal arrival time, thereby obtaining the pseudorange and pseudorange rate observation values.

[0009] After obtaining pseudorange observations, broadcast ephemeris or differential correction signals are used to correct ionospheric delay, tropospheric delay, and satellite clock errors.

[0010] Before receiving navigation and communication fusion signals from multiple low-Earth orbit satellites via an airborne multi-band antenna array, the process also includes the following steps: The system monitors the signal quality of each operating frequency band in real time. When interference is detected in the current frequency band, the system controls the antenna and RF front-end to automatically switch to the pre-configured backup frequency band.

[0011] The optimization algorithm is the binary whale optimization algorithm.

[0012] On the other hand, the present invention also includes an airborne positioning system for the Motor Sich air navigation network, comprising an airborne multi-band phased array antenna array, a navigation signal processor, an observation calculation module, a multi-source data fusion filter, and an avionics system interface. The airborne multi-band phased array antenna array is connected to the navigation signal processor, the navigation signal processor is connected to the observation calculation module, the observation calculation module is connected to the multi-source data fusion filter, the multi-source data fusion filter is connected to the avionics system interface, and the avionics system interface is connected to the avionics system of the aircraft. An airborne multi-band phased array antenna is used to receive navigation and communication fusion signals from multiple low-orbit satellites; A navigation signal processor, connected to the antenna array, is used to perform anti-interference and enhancement processing on the received signals; The observation calculation module is used to calculate pseudorange and pseudorange rate observations from the processed signal; A multi-source data fusion filter is used to fuse the observed values ​​and airborne inertial sensor data to construct and solve the positioning solution model; The avionics system interface is used to output the calculated attitude information to the aircraft's avionics system.

[0013] This invention discloses an airborne positioning method and system for the Motor Sich aviation navigation network. By employing adaptive beamforming technology, it can identify interference sources in real time and dynamically adjust the antenna pattern, forming nulls in the direction of interference and high-gain beams in the direction of satellite signals. This effectively suppresses various intentional and unintentional interferences, ensuring reliable reception of navigation signals in complex electromagnetic environments. Positioning accuracy and reliability are significantly improved: by fusing low-Earth orbit satellite navigation signals and airborne inertial sensor data, and using commutative Kalman filtering combined with a binary whale optimization algorithm, the filtering parameters are intelligently tuned online. This effectively overcomes the error limitations of a single navigation source, achieving high-precision and high-reliability positioning at the centimeter to meter level, effectively avoiding positioning interruptions caused by signal loss. Real-time monitoring of signal quality and automatic switching to clean frequency bands when interference occurs ensures the continuity and robustness of the navigation link. The entire system has a fast response speed, high computing efficiency, and low power consumption, which can meet the stringent requirements of modern aircraft for high-speed maneuverability and high real-time performance. In summary, this invention effectively solves the industry problem of the rapid deterioration of navigation performance in high dynamic and strong interference environments, and provides aircraft with an all-weather, high-precision, and highly reliable navigation and positioning solution. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0015] Figure 1This is a flowchart of the airborne positioning method for the Motor Sich aviation navigation network of the present invention.

[0016] Figure 2 This is a schematic diagram of the structure of the Motor Sich aviation navigation network airborne positioning system of the present invention.

[0017] In the diagram: 101-Airborne multi-band phased array antenna array, 102-Navigation signal processor, 103-Observation calculation module, 104-Multi-source data fusion filter, 105-Avionics system interface. Detailed Implementation

[0018] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0019] Please see Figure 1 ,in Figure 1 This is a flowchart of the airborne positioning method for the Motor Sich aviation navigation network.

[0020] This invention provides an airborne positioning method and system for Motor Sich aviation navigation networks, comprising the following steps: S1: Receives navigation and communication fusion signals from multiple low-orbit satellites via an airborne multi-band antenna array.

[0021] Specifically, before receiving navigation and communication fusion signals from multiple low-orbit satellites via the airborne multi-band antenna array, the signal quality of each working frequency band is monitored in real time; when it is determined that the current frequency band is interfered with, the control antenna and radio frequency front-end are automatically switched to the pre-configured backup frequency band.

[0022] In this embodiment, the real-time monitoring is implemented in parallel by the spectrum analysis unit of the signal processor. This unit continuously monitors and evaluates the carrier-to-noise ratio (CNR) and bit error rate (BER) of signals in each frequency band. The determination step is based on a preset dual-threshold mechanism: when the CNR of a certain frequency band is continuously lower than the first threshold and the BER is continuously higher than the second threshold for a set period of time, it is determined that the frequency band is under interference. The switching command is issued by the main control chip, which controls the radio frequency switch to reconstruct the receiving link to the pre-stored backup frequency band parameters within milliseconds. The backup frequency band is pre-stored in non-volatile memory and contains information such as frequency, bandwidth, and corresponding satellite identifiers to ensure that satellite signals can be quickly reacquired and tracked after the switch, thus ensuring the continuity and reliability of navigation services.

[0023] S2: Perform anti-interference and enhancement processing on the fused signal to extract usable navigation signals.

[0024] Specifically, S21: The received radio frequency signal is amplified with low noise, down-converted, and converted from analog to digital to obtain a digital intermediate frequency signal; S22: Perform a fast Fourier transform on the digital intermediate frequency signal and analyze the signal spectrum to identify the frequency points of the interference source; S23: Based on the identified interference source frequency points, an adaptive beamforming algorithm is used to dynamically calculate the weighting vector of the antenna array; S24: Apply the weighted vector to form an antenna pattern null in the direction of the incoming interference signal. At the same time, S25: Form a high-gain main beam in the direction of the target satellite signal and output the anti-interference signal.

[0025] In this embodiment, the low-noise amplification employs a GaAs pHEMT low-noise amplifier (LNA) with a noise figure below 0.8 dB, operating in the L, S, and C bands. Down-conversion is achieved through a two-stage mixing structure, with a first intermediate frequency (IF) of 950 MHz and a second IF of 70 MHz. The final analog-to-digital conversion is completed using a 16-bit precision ADC with a sampling rate of 100 MSPS. The Fast Fourier Transform (FFT) is implemented in parallel within an FPGA, using a 2048-point FFT processor to generate the signal spectrum in real time. Interference source identification is achieved through a peak power detection algorithm. When the power at a certain frequency exceeds an adaptive threshold (typically 6-8 times the average signal power), it is marked as a potential interference source. The adaptive beamforming algorithm uses the Normalized Least Mean Square (NLMS) algorithm, calculating the optimal weighted vector in real time using digital beamforming (DBF). The weighted vector update period is less than 100 microseconds, ensuring rapid tracking of interference source movement. The antenna pattern null depth can reach -35°. With a gain of over dB, the null width can be dynamically adjusted according to the interference bandwidth (usually 1.2-1.5 times the interference bandwidth), while forming a directional beam with a gain of over 20 dBi in the direction of the main beam, and the beam pointing accuracy is better than 0.5 degrees; the entire anti-interference processing is completed on a dedicated signal processing chip (ASIC), with a processing delay of less than 1 millisecond, effectively ensuring the real-time performance and reliability of the navigation signal.

[0026] S3: Solve for the observations used for positioning from the processed signal.

[0027] Specifically, the satellite navigation message is analyzed to obtain the signal transmission time, and the time difference is calculated by measuring the signal arrival time, thereby obtaining the pseudorange and pseudorange rate observation values.

[0028] After obtaining pseudorange observations, broadcast ephemeris or differential correction signals are used to correct ionospheric delay, tropospheric delay, and satellite clock errors.

[0029] In this embodiment, the error correction process is completed by the error processing unit. For ionospheric delay, the influence of first-order ionospheric delay is directly eliminated by combining dual-frequency observations without geometric distance. For single-frequency receivers, the Klobuchar model is used in conjunction with ionospheric correction parameters provided in the broadcast ephemeris for correction. The correction of tropospheric delay uses the Saastamoinen model, which comprehensively considers the influence of meteorological parameters such as temperature, air pressure, and humidity, and further improves the correction accuracy of low-elevation-angle satellites by introducing an elevation angle mapping function (such as the Niell mapping function). Satellite clock bias is directly corrected using the satellite clock bias parameters broadcast in the navigation message. For high-precision application scenarios, it can be further refined by receiving precise clock bias products released by ground augmentation stations. To further improve the correction accuracy, when a differential correction signal is received from a ground reference station or satellite-based augmentation system, the system automatically uses carrier phase smoothing pseudorange technology to generate high-precision observations after differential correction. All corrected observations are accompanied by accuracy evaluation indicators, providing a reliability weight basis for subsequent multi-source data fusion. The calculation delay of the entire error correction process is less than 2 milliseconds, ensuring the real-time performance and high accuracy of navigation solutions.

[0030] S4: Integrate the observed values ​​with the airborne inertial sensor data to construct a positioning solution model.

[0031] Specifically, S41: Using the aircraft's position, velocity, attitude angle, and receiver clock error as state variables, establish state equations based on inertial measurement unit data; S42: Using pseudorange and pseudorange rate extracted from satellite signals as observations, establish measurement equations; S43: Initialize the state vector and covariance matrix of the capacitive Kalman filter; S44: Perform filtering recursion, update time and measurement, fuse inertial data and satellite observations to obtain the optimal estimated state vector; S45: The process noise covariance matrix and measurement noise covariance matrix of the filter are tuned online using an optimization algorithm.

[0032] The optimization algorithm is the binary whale optimization algorithm.

[0033] In this embodiment, the capacitive Kalman filter (CKF) employs a third-order spherical radial volume criterion for nonlinear filtering. Its state vector includes 11 state variables: three-dimensional position, three-dimensional velocity, three-dimensional attitude angles (roll, pitch, yaw), and receiver clock error and clock drift. The state equation is based on the angular and velocity increment information output by the inertial measurement unit (IMU), and attitude is updated using the quaternion method. The Runge-Kutta method is used for numerical integration to ensure the accuracy of state prediction in high-dynamic environments. The measurement equation is established as a nonlinear function of pseudorange and pseudorange rate with respect to the state vector. The pseudorange observation equation considers the geometric relationship between the satellite orbital position and the receiver position, while the pseudorange rate observation equation introduces the mapping relationship between Doppler frequency shift and relative velocity. During filter initialization, the initial value of the state vector is obtained from the satellite single-point positioning result and the initial alignment of the IMU. The data is provided jointly, and the covariance matrix is ​​diagonalized according to the initial accuracy of each sensor. During the filtering recursion, the time update step uses IMU data to predict the state vector and covariance matrix; the measurement update step uses satellite observations to correct the predicted state. The binary whale optimization (BWOA) algorithm performs online optimization of the diagonal elements of the process noise covariance matrix Q and the measurement noise covariance matrix R by discretizing the search space, so as to adaptively adjust the estimation weights of the filter for IMU error and observation noise, avoiding filter divergence caused by model mismatch. The optimization objective function is set as the root mean square error of the innovation sequence to ensure that the filter is always in the optimal estimation state. The entire fusion solution process is implemented on an embedded multi-core DSP, adopts a parallel pipeline architecture, and the single filtering cycle is less than 10 milliseconds, which significantly improves the accuracy and robustness of the system.

[0034] S5: Solve the positioning solution model to obtain the real-time attitude information of the aircraft.

[0035] In this embodiment, the solution process is implemented through an embedded navigation computer. The computer adopts a multi-core ARM Cortex-A77 architecture and integrates an FPGA coprocessor. After receiving the optimal estimated state vector from the CKF filter, the navigation computer first performs observability analysis and singularity detection: by calculating the condition number of the observation matrix (cond(H) < 10^6), it is confirmed that the system is in a good observation state; then, physical constraint verification is performed. The position solution result must conform to the aircraft dynamic envelope (acceleration ≤ 12g, jerk ≤ 200m / s³), and the attitude angle change must meet the Euler angle continuity and angular rate limits (roll ± 180°, pitch ± 90°, yaw 0-360°, angular rate ≤ 400° / s); the clock error parameter fluctuation range is constrained within ± 1ms; the handling of outliers adopts the Robust Kalman Smoother algorithm based on a sliding window, combined with historical data of 10 cycles for smooth correction. The final output pose information includes: 1. Three-dimensional geographic coordinates (longitude, latitude, altitude), 2. Three-dimensional velocity vectors (east, north, and sky), 3. Three-axis attitude angles (roll, pitch, and yaw), 4. Precise timestamp with a synchronization error of <100ns with UTC; all output data are output at 100Hz via ARINC-429 bus and Ethernet AVB dual-channel output.

[0036] S6: Transmit the pose information to the airborne avionics system.

[0037] In this embodiment, the pose data packets output by the navigation computer are first encapsulated in a format that conforms to the ARINC429 specification. The data words contain 32-bit data fields (where latitude and longitude are encoded using 32-bit fixed-point numbers, altitude is encoded using 24-bit two's complement, and attitude angles are encoded with a precision of 0.001 degrees / bit). At the same time, the Ethernet AVB channel adopts the IEEE 1722 standard transmission protocol, and a 16-bit CRC checksum is added to the data packets. The transmission process adopts a dual-channel hot backup mechanism. When the primary channel (preferably ARINC429) fails, the system automatically switches to the backup channel within 50 microseconds. The data receiving end (flight control computer, display system, mission management system, etc.) parses the data through the hardware interface chip and performs packet loss detection and retransmission requests based on the sequence number in the data packet header. The system supports broadcasting attitude data to up to 8 avionics systems simultaneously, with transmission delay jitter of less than 100 microseconds and a data update interval strictly maintained at 10 milliseconds. This meets the stringent requirements of high-dynamic flight control for the real-time performance and reliability of navigation data. All transmission links comply with the DO-160G avionics environmental condition standard, ensuring stable operation in extreme temperature, vibration, and electromagnetic interference environments.

[0038] The airborne positioning method for the Motor Sich air navigation network of this invention receives low-Earth orbit satellite signals through an airborne multi-band antenna array and achieves seamless switching in interference environments through dynamic frequency band management technology. Adaptive beamforming technology is used for anti-interference processing of the signals, and fast Fourier transform and NLMS algorithms are used to achieve interference suppression and signal enhancement. Dual-frequency observation and multi-model fusion algorithms are used for error correction. A capacitive Kalman filter is used to fuse satellite observations and inertial sensor data, and a binary whale optimization algorithm is used to fine-tune the filter parameters in real time. Finally, a high-precision solution algorithm outputs pose information, which is sent to the avionics system through a multi-redundant transmission architecture. This method achieves centimeter-level positioning accuracy (0.05m horizontally, 0.08m vertically) in complex electromagnetic environments, with a processing delay of less than 20 milliseconds, interference suppression capability of over -35dB, and autonomous navigation capability of over 30 seconds, comprehensively improving the reliability, accuracy, and real-time performance of the air navigation system.

[0039] On the other hand, please see Figure 2 , Figure 2 This is a schematic diagram of the structure of the Motor Sich aviation navigation network airborne positioning system of the present invention.

[0040] The present invention also includes an airborne positioning system for the Motor Sich air navigation network, comprising an airborne multi-band phased array antenna array 101, a navigation signal processor 102, an observation value calculation module 103, a multi-source data fusion filter 104, and an avionics system interface 105. The airborne multi-band phased array antenna array 101 is connected to the navigation signal processor 102, the navigation signal processor 102 is connected to the observation value calculation module 103, the observation value calculation module 103 is connected to the multi-source data fusion filter 104, the multi-source data fusion filter 104 is connected to the avionics system interface 105, and the avionics system interface 105 is connected to the avionics system of the aircraft. Airborne multi-band phased array antenna array 101 is used to receive navigation and communication fusion signals from multiple low-orbit satellites; The navigation signal processor 102 is connected to the antenna array and is used to perform anti-interference and enhancement processing on the received signals. The observation calculation module 103 is used to calculate the pseudorange and pseudorange rate observations from the processed signal; A multi-source data fusion filter 104 is used to fuse the observed values ​​and airborne inertial sensor data to construct and solve the positioning solution model; The avionics system interface 105 is used to output the calculated attitude information to the aircraft's avionics system.

[0041] In this embodiment, the navigation signal processor 102 first performs digital down-conversion processing on the received raw sampled data, shifting the 70MHz intermediate frequency signal to the baseband. Then, it performs decimation filtering through a polyphase filter bank, reducing the data rate to 5MSPS. The anti-interference processing unit adopts a block processing approach, with each 256 sampling points forming a processing unit. Weighted vector calculations are performed through matrix operations, with processing delays consistently below 0.8ms. The observation value calculation module 103 employs a dual-buffer design: a front-end buffer receives the anti-interference I / Q data, while the back-end performs parallel correlation processing. Each satellite channel is independently equipped with a carrier loop discriminator and a code loop discriminator, using a third-order phase-locked loop structure with a loop bandwidth of 18Hz. The pseudorange observations are obtained through code phase measurement and carrier phase smoothing algorithm. The smoothing window length is 30 seconds and the observation output frequency is 100Hz. The multi-source data fusion filter 104 adopts a fixed delay smoothing filter architecture. The data processing is divided into three levels: the raw observation layer processes satellite pseudorange and IMU raw data; the state estimation layer runs a capacitive Kalman filter; and the optimization layer executes the binary whale optimization algorithm. The filter maintains a sliding window of length 20 to perform online estimation of the process noise covariance matrix Q and the measurement noise covariance matrix R, with an optimization period of 1 second. The system data stream adopts a timestamp synchronization mechanism, and each module maintains clock synchronization through the IEEE 1588 precise time protocol, with a time deviation of less than 1 μs. Data communication adopts a standardized message format, defining the following data structures: navigation message (128 bytes, including position, velocity, attitude, and timestamp), observation message (64 bytes, including satellite number, pseudorange, pseudorange rate, and signal-to-noise ratio), and status message (256 bytes, including state vector, covariance matrix, and health status). Multiple integrity monitoring is implemented during data processing: carrier-to-noise ratio detection and multipath detection are performed at the signal level.

[0042] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. An airborne positioning method for a Motor Sich aviation navigation network, characterized in that, Includes the following steps: The system receives fused navigation and communication signals from multiple low-Earth orbit satellites via an airborne multi-band antenna array. The fused signal is subjected to anti-interference and enhancement processing to extract usable navigation signals; The observations used for positioning are calculated from the processed signal; By integrating the observed values ​​with airborne inertial sensor data, a positioning solution model is constructed; Solve the positioning calculation model to obtain the real-time attitude information of the aircraft; The pose information is transmitted to the airborne avionics system.

2. The airborne positioning method for Motor Sich aviation navigation network as described in claim 1, characterized in that, The specific steps for anti-interference and enhancement processing of the fused signal include: The received radio frequency signal is amplified with low noise, down-converted, and converted from analog to digital to obtain a digital intermediate frequency signal; Perform a Fast Fourier Transform on the digital intermediate frequency signal and analyze the signal spectrum to identify the frequency points of interference sources; Based on the identified interference source frequency points, an adaptive beamforming algorithm is used to dynamically calculate the weighting vector of the antenna array; By applying the weighted vector, an antenna pattern null is formed in the direction of the incoming interference signal, while a high-gain main beam is formed in the direction of the target satellite signal, and the anti-interference signal is output.

3. The airborne positioning method for Motor Sich aviation navigation network as described in claim 1, characterized in that, The specific steps for constructing a positioning solution model by integrating the observed values ​​with airborne inertial sensor data include: The state equation is established based on inertial measurement unit data, with the aircraft's position, velocity, attitude angle, and receiver clock error as state variables. Using pseudorange and pseudorange rate extracted from satellite signals as observations, a measurement equation is established; the state vector and covariance matrix of the capacitive Kalman filter are initialized. Execute filtering recursion, perform time updates and measurement updates, fuse inertial data and satellite observations to obtain the optimal estimated state vector; The process noise covariance matrix and measurement noise covariance matrix of the filter are tuned online using an optimization algorithm.

4. The airborne positioning method for Motor Sich aviation navigation network as described in claim 1, characterized in that, The specific steps for extracting the observations used for positioning from the processed signal include: The satellite navigation message is analyzed to obtain the signal transmission time, and the time difference is calculated by measuring the signal arrival time, thereby obtaining the pseudorange and pseudorange rate observation values.

5. The airborne positioning method for Motor Sich aviation navigation network as described in claim 4, characterized in that, After obtaining pseudorange observations, broadcast ephemeris or differential correction signals are used to correct ionospheric delay, tropospheric delay, and satellite clock errors.

6. The airborne positioning method for Motor Sich aviation navigation network as described in claim 1, characterized in that, Before receiving fused navigation and communication signals from multiple low-Earth orbit satellites via an airborne multi-band antenna array, the following steps are also included: The system monitors the signal quality of each operating frequency band in real time. When interference is detected in the current frequency band, the system controls the antenna and RF front-end to automatically switch to the pre-configured backup frequency band.

7. The airborne positioning method for Motor Sich aviation navigation network as described in claim 3, characterized in that, The optimization algorithm is the binary whale optimization algorithm.

8. A Motor Sich airborne positioning system for an aviation navigation network, used to implement the method as described in any one of claims 1-7, characterized in that, The system includes an airborne multi-band phased array antenna array, a navigation signal processor, an observation calculation module, a multi-source data fusion filter, and an avionics system interface. The airborne multi-band phased array antenna array is connected to the navigation signal processor, the navigation signal processor is connected to the observation calculation module, the observation calculation module is connected to the multi-source data fusion filter, the multi-source data fusion filter is connected to the avionics system interface, and the avionics system interface is connected to the aircraft's avionics system. An airborne multi-band phased array antenna is used to receive navigation and communication fusion signals from multiple low-orbit satellites; A navigation signal processor, connected to the antenna array, is used to perform anti-interference and enhancement processing on the received signals; The observation calculation module is used to calculate pseudorange and pseudorange rate observations from the processed signal; A multi-source data fusion filter is used to fuse the observed values ​​and airborne inertial sensor data to construct and solve the positioning solution model; The avionics system interface is used to output the calculated attitude information to the aircraft's avionics system.

Images