Adaptive nonlinear filtering based aircraft multi-source data attitude evaluation system

Abstract

The application discloses a kind of based on adaptive nonlinear filtering's aircraft multi-source data attitude evaluation system, it is related to chromatic aberration detection technical field, including multi-source data acquisition end, adaptive filtering fusion end, noise characteristic estimation end and attitude solution evaluation end, the multi-source data acquisition end, adaptive filtering fusion end, noise characteristic estimation end and attitude solution evaluation end pass through on-board bus communication protocol and carry out two-way data interaction between, and each end is electrically connected with central logic processing matrix.This based on adaptive nonlinear filtering's aircraft multi-source data attitude evaluation system, the present application is aimed at atmospheric data and inertial navigation data this kind of multi-source heterogeneous data, establishes the unified fusion framework based on unscented kalman filter, and introduces multiple fading factor matrix, solves the problem of insufficient tracking ability of traditional method under strong nonlinear and large maneuvering flight conditions, significantly improves the precision of attitude estimation.

Description

Technical Field

[0001] This invention relates to the field of data fusion technology, and in particular to an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering. Background Technology

[0002] Aircraft flight control systems place extremely high demands on the accuracy, real-time performance, and reliability of attitude information, which directly impacts flight safety and mission effectiveness. Modern aircraft typically integrate multiple sensors, such as air data systems (including pitot tubes, barometric horns, angle-of-attack / sideslip angle sensors, etc.) and inertial navigation systems (including gyroscopes, accelerometers, etc.). By fusing data from these multi-source heterogeneous sensors, the limitations of single sensors, such as susceptibility to environmental interference and measurement noise, can be effectively overcome, resulting in higher-precision aircraft attitude information.

[0003] Currently, nonlinear filtering methods such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cumulative Kalman Filter (CKF) have been widely applied in aircraft attitude estimation. International research mainly focuses on theoretical improvements and performance comparisons of these fundamental algorithms, such as improving accuracy by optimizing the state update process. Domestic scholars, on the other hand, emphasize the robustness and adaptability of the algorithms and attempt to combine them with artificial intelligence technology to address complex flight environments and sensor malfunctions.

[0004] However, existing technologies still have the following shortcomings when handling the fusion of multi-source heterogeneous data in complex flight environments: First, there is a lack of systematic and high-precision fusion strategies for data with significant differences in physical characteristics, sampling frequencies, and noise models, such as atmospheric data (e.g., airspeed, angle of attack) and inertial navigation data (e.g., angular velocity, acceleration). Second, existing adaptive filtering algorithms still need to improve their robustness and adaptability when facing atmospheric turbulence, time-varying sensor noise, or sudden failures, making it difficult to simultaneously meet the stringent requirements of accuracy and stability. Third, the balance between computational efficiency and accuracy of algorithms has not been effectively resolved; high-precision algorithms are often computationally complex and difficult to meet the real-time requirements of flight control.

[0005] Therefore, how to design an aircraft attitude assessment system that can adaptively process multi-source heterogeneous data, maintain high accuracy and robustness in complex environments, and meet real-time requirements is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The main objective of this invention is to provide an aircraft attitude assessment system based on adaptive nonlinear filtering, in order to solve the problems mentioned in the background above.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering, comprising a multi-source data acquisition end, an adaptive filtering fusion end, a noise characteristic estimation end, and an attitude calculation and evaluation end. The multi-source data acquisition end, the adaptive filtering fusion end, the noise characteristic estimation end, and the attitude calculation and evaluation end interact bidirectionally through an airborne bus communication protocol, and each end is electrically connected to a central logic processing matrix.

[0008] The multi-source data acquisition terminal is used to simultaneously acquire atmospheric data and inertial navigation data during aircraft flight. The atmospheric data includes at least airspeed, angle of attack, and sideslip angle, and the inertial navigation data includes at least three-axis angular velocity and three-axis acceleration. The multi-source data acquisition terminal also performs spatiotemporal alignment and outlier removal preprocessing on the raw data.

[0009] The adaptive filtering fusion terminal is used to construct a core fusion model based on the unscented Kalman filter framework, and introduces the idea of ​​a strong tracking filter to dynamically calculate multiple fading factors to adjust the filter gain matrix online. The adaptive filtering fusion terminal establishes a state transition model based on the aircraft kinematic equations, and uses atmospheric data and inertial navigation data as measurement inputs to output high-precision aircraft attitude state estimates.

[0010] The noise characteristic estimation end is used to estimate the system process noise covariance matrix and measurement noise covariance matrix in real time using the information sequence within the sliding window, and adaptively adjust the noise parameters in the adaptive filtering and fusion end according to the estimation results.

[0011] The attitude calculation and evaluation terminal is used to calculate the real-time attitude angles of the aircraft, including pitch angle, roll angle and yaw angle, based on the state estimation value output by the adaptive filtering and fusion terminal, and to evaluate the confidence of the attitude estimation, and generate a structured attitude data stream to be pushed to the flight control computer.

[0012] Furthermore, the multi-source data acquisition terminal includes an atmospheric data acquisition module, an inertial navigation data acquisition module, and a data preprocessing module.

[0013] The atmospheric data acquisition module is connected to the airspeed tube, static pressure orifice, angle of attack sensor, sideslip angle sensor, and total temperature sensor via an interface; the inertial navigation data acquisition module is connected to the optical gyroscope and accelerometer via an interface.

[0014] The data preprocessing module includes a timestamp synchronization unit, a coordinate system alignment unit, and an outlier detection unit. The timestamp synchronization unit utilizes a distributed clock synchronization protocol to align sensor data from different sampling frequencies to the same time reference. The coordinate system alignment unit transforms the airflow coordinate system containing the atmospheric data to the body coordinate system of the inertial navigation system. The outlier detection unit is based on… Criteria or median filtering methods are used to identify and remove outliers and isolated points in sensor data.

[0015] Furthermore, the adaptive filtering fusion terminal includes a model building module, a UKF core filtering module, and a multiple fading factor calculation module.

[0016] The model building module constructs a nonlinear state transition function with the aircraft's attitude angle, angular velocity, position, and velocity as state variables based on the aircraft's six-degree-of-freedom rigid body kinematic equations. And establish a nonlinear measurement function that correlates atmospheric data and inertial navigation data to state variables. The UKF core filtering module is based on the principle of unscented transform, and selects a set of... The points are used to approximate the nonlinear distribution, and the state prediction and measurement update steps are performed.

[0017] The multiple fading factor calculation module draws on the design principles of strong tracking filters to calculate a time-varying multiple fading factor matrix in real time. This is then embedded into the UKF state prediction covariance update formula, with the specific calculation formula as follows:

[0018] in, The state prediction covariance matrix, For the i-th point, This is the predicted state value. Let be the process noise covariance matrix, and n be the dimension of the state vector. The weights used to calculate the covariance; the multiple fading factor matrix By dynamically determining the diagonal elements of the information sequences from different channels to keep them orthogonal, the filter is forced to track rapidly changing states.

[0019] Furthermore, the noise characteristic estimation module includes an information sequence caching module, an online noise covariance estimation module, and an adaptive adjustment module.

[0020] The innovation sequence caching module maintains a fixed-length sliding window to store the innovation vectors generated by the UKF core filtering module within consecutive time steps. ,in , These are actual measured values. For predicting measurement values.

[0021] The online noise covariance estimation module estimates the measurement noise covariance matrix at the current time in real time based on the sample covariance of the innovation sequence within the sliding window, using the following set of equations. process noise covariance matrix :

[0022]

[0023]

[0024] in, For the estimated new information covariance matrix, The length of the sliding window. Let Jacobian matrix be the measurement function. The state prediction covariance matrix, This is the Kalman gain matrix.

[0025] The adaptive adjustment module will estimate the... and The feedback is sent to the UKF core filtering module to replace the original fixed noise parameters, thereby achieving online adaptive tracking of noise statistical characteristics.

[0026] The central logic processing matrix is ​​used to perform cross-end data association and logical arbitration. When the multi-source data acquisition terminal detects that a sensor signal is lost or exceeds a reasonable range, the central logic processing matrix, in conjunction with the noise characteristic estimation terminal, suspends the online estimation of the measurement noise corresponding to that sensor and adjusts its noise covariance matrix. The value of the corresponding channel is temporarily set to a large preset value to reduce the weight of this unreliable measurement information in the fusion process.

[0027] Furthermore, the attitude calculation and evaluation terminal includes an attitude angle calculation module, a confidence evaluation module, and a data encapsulation and push module.

[0028] The attitude angle calculation module calculates the aircraft's pitch angle using trigonometric functions based on the quaternion or Euler angle state variables output by the adaptive filtering and fusion terminal. Roll angle and heading angle .

[0029] The confidence assessment module estimates the noise covariance in real time based on the noise characteristic estimation output. and filtered information The amplitude is used to calculate the overall confidence index of the current attitude estimate. The calculation formula is as follows:

[0030] in, Let be the current information vector. The new information covariance matrix, For the estimated measurement noise covariance matrix, The reference measurement noise covariance matrix, This indicates finding the trace of a matrix.

[0031] The data encapsulation and push module packages the calculated attitude angles and corresponding confidence indices into structured data frames according to avionics standard bus protocols such as ARINC429 or AFDX, and pushes them to the main flight control computer and the backup flight control computer through redundant communication links.

[0032] The present invention has the following beneficial effects: 1. In this invention, by setting up a multi-source data acquisition end and an adaptive filtering fusion end, a unified fusion framework based on unscented Kalman filtering is established for multi-source heterogeneous data such as atmospheric data and inertial navigation data. Multiple fading factor matrices are introduced to solve the problem of insufficient tracking capability of traditional methods under strong nonlinear and high-maneuver flight conditions, and significantly improve the accuracy of attitude estimation.

[0033] 2. In this invention, by setting a noise characteristic estimation end, the statistical characteristics of process noise and measurement noise are estimated in real time online using the information sequence within the sliding window. This enables the system to adapt to noise changes caused by complex flight environments, overcoming the poor robustness of traditional fixed-parameter filtering algorithms.

[0034] 3. In this invention, intelligent fault-tolerant processing is achieved in the event of momentary sensor failure or signal loss by setting cross-end linkage logic in the central logic processing matrix. By dynamically adjusting the noise covariance matrix, the fusion weight of unreliable data is adaptively reduced, effectively preventing faulty sensors from contaminating the final attitude estimation result and improving the system's reliability and fault tolerance.

[0035] 4. In this invention, by setting up an attitude calculation and evaluation terminal, not only is a high-precision attitude angle output, but also a confidence index based on innovation and noise estimation is output simultaneously. This provides key uncertainty measurement information for the decision-making layer of the flight control system, supports higher-level robust control and health management applications, and provides more comprehensive technical protection for flight safety. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the overall system architecture of an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering according to the present invention. Figure 2 This is a schematic diagram of the architecture of the multi-source data acquisition terminal of an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering according to the present invention. Figure 3This is a schematic diagram of the adaptive filtering fusion terminal of an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering according to the present invention. Figure 4 This is a schematic diagram of the noise characteristic estimation end of an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering according to the present invention. Figure 5 This is a schematic diagram of the attitude calculation and evaluation terminal of an aircraft multi-source data attitude evaluation system based on adaptive nonlinear filtering according to the present invention. Detailed Implementation

[0037] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0038] Example 1, please refer to Figures 1 to 3 As shown: An aircraft multi-source data attitude assessment system based on adaptive nonlinear filtering includes a multi-source data acquisition end, an adaptive filtering fusion end, a noise characteristic estimation end, and an attitude calculation and assessment end. Each end interacts bidirectionally with the other end via an airborne bus and is electrically connected to a central logic processing matrix. The central logic processing matrix serves as the coordination core of the system and is used to perform cross-end data association and logic arbitration.

[0039] The multi-source data acquisition terminal is used to acquire the raw signals output by the aircraft's air data system and inertial navigation system, and to perform necessary preprocessing; (Reference) Figure 2 Specifically, it includes an atmospheric data acquisition module, an inertial navigation data acquisition module, and a data preprocessing module; the atmospheric data acquisition module connects to the pitot tube, static pressure orifice, angle of attack / sideslip angle sensor, and total temperature sensor to collect dynamic pressure, static pressure, and angle of attack. Sideslip angle Information such as total atmospheric temperature; the inertial navigation data acquisition module connects to the optical gyroscope and accelerometer to collect three-axis angular velocities. and triaxial acceleration The timestamp synchronization unit in the data preprocessing module uses a distributed clock synchronization protocol to unify data streams with different sampling rates to the system time base; the coordinate system alignment unit transforms atmospheric data from the airflow coordinate system to the body coordinate system; the outlier detection unit uses... The criteria identify and eliminate outlier values ​​caused by electromagnetic interference or instantaneous impacts to ensure the quality of data entering the fusion process.

[0040] The adaptive filtering fusion terminal is the core of this system; Reference Figure 3 It includes a model building module, a UKF core filtering module, and a multiple fading factor calculation module; the model building module first establishes a nonlinear state transition function based on the aircraft rigid body kinematics and dynamic equations. State variables Usually selected as This refers to the aircraft's attitude angles, angular velocity, velocity, angle of attack, sideslip angle, and three-dimensional position; measurement functions. This defines the nonlinear relationship between measurements such as airspeed, angle of attack, and acceleration and state variables.

[0041] The UKF core filtering module executes the standard UKF algorithm; it first selects the filter through an unscented transform. Point set, then through state transition function spread Point obtains state prediction value and its covariance Then, through the measurement function Obtain the measurement prediction value And calculate the Kalman gain. Finally, the state update is completed, and the optimal state estimate after fusion is obtained. .

[0042] When an aircraft performs high-maneuver flight or encounters strong turbulence, the tracking capability of the standard UKF will decrease; therefore, the multiple fading factor calculation module calculates the multiple fading factor matrix in real time. Its basic principle is to force the innovation sequences at different times to remain orthogonal, and to solve for the fading factor of each channel through analytical or iterative methods. When the system state undergoes a sudden change, the covariance of the new information sequence will increase sharply, and correspondingly... It automatically increases, thereby dynamically amplifying the state prediction covariance matrix. The corresponding channel components; this is equivalent to reducing the weight of historical data on the current estimate, thus placing more trust in the measurement information at the current moment, thereby significantly improving the filter's ability to track rapidly changing states and its robustness.

[0043] Example 2, please refer to Figure 4 As shown: Based on Embodiment 1, another innovation of this invention lies in the noise characteristic estimation end; in many prior art, process noise covariance... Measurement noise covariance It is preset to a fixed constant; however, the actual flight environment of an aircraft is complex and variable. For example, when passing through turbulent airflow areas, atmospheric noise increases significantly; and the internal noise characteristics of the sensor also drift with changes in the sensor's operating temperature. , The matrix cannot adapt to this change, leading to a decrease in filtering accuracy or even divergence.

[0044] The noise characteristic estimation module proposed in this invention includes an innovation sequence buffer module, an online noise covariance estimation module, and an adaptive adjustment module, aiming to estimate noise characteristics online in real time. and The information sequence cache module maintains a cache of length . ,For example A sliding window stores the new information vector generated in each computation cycle in real time. The online noise covariance estimation module first calculates the sample covariance of the innovation sequence within the window. In an ideal linear system, the theoretical value of the innovation covariance is... Based on this relationship, the measurement noise covariance matrix at the current moment can be solved by transformation. Similarly, using relationships The process noise covariance matrix can be estimated. .

[0045] The system operates as follows: First, the multi-source data acquisition unit collects inertial navigation data at a frequency of 100Hz and atmospheric data at a frequency of 50Hz. After time-stamping synchronization, the data is sent to the adaptive filtering fusion unit. Simultaneously, the adaptive filtering fusion unit performs UKF calculation and calculates the innovation sequence. The noise characteristic estimation unit continuously monitors the innovation sequence, triggering online estimation every 10 filtering cycles and updating the system using the 50 most recent innovation vectors. and The adaptive adjustment module then replaces the original parameters in the UKF core filtering module with the newly estimated noise matrix.

[0046] For example, when an aircraft enters a turbulent region from steady cruise, the airspeed measurement noise in the atmospheric data increases significantly; the noise characteristic estimation end calculates the new information covariance. An abnormal increase will be observed, which will allow for the estimation of the measurement noise covariance matrix. The corresponding atmospheric data channel value also increased synchronously; the UKF core filtering module received the updated value. Subsequently, the weight of atmospheric data measurements in the fusion will be automatically reduced, and more trust will be placed on the relatively more stable inertial navigation data in this environment; this dynamic adjustment mechanism effectively suppresses noise interference and ensures the stability and accuracy of attitude estimation.

[0047] Example 3, please refer to Figure 1 and Figure 5As shown: This embodiment further illustrates the cross-end collaborative logic of the central logic processing matrix and the functions of the attitude calculation and evaluation end; the central logic processing matrix is ​​electrically connected to all other ends and is responsible for processing the high-level logic across ends; a typical application scenario is sensor fault management; when the outlier detection unit in the multi-source data acquisition end continuously monitors data anomalies from the left pitot tube (such as the dynamic pressure value suddenly changing to zero, which is physically impossible), it determines that the left pitot tube may have failed due to icing; at this time, the central logic processing matrix receives this fault flag and immediately activates the noise characteristic estimation end; the specific instruction is: suspend the measurement noise covariance matrix. The online estimation corresponds to the "left airspeed" channel, and the value of this channel is forcibly set to a preset number that is much larger than the normal value (e.g., Simultaneously, the central logic processing matrix sends a "single-sided airspeed failure" status flag to the adaptive filtering fusion end. Upon receiving the new flag... Subsequently, the weight of the measurement information related to the faulty airspeed is automatically reduced to near zero, thereby effectively isolating the negative impact of the faulty sensor on attitude estimation. The system continues to work only by relying on the normal airspeed tube on the right and the inertial navigation data, achieving fault-tolerant operation without degradation.

[0048] The attitude calculation and evaluation unit is responsible for converting the fused optimal state estimate into attitude angles that can be directly used for flight control, and evaluating its reliability; (Reference) Figure 5 The attitude angle calculation module calculates the state vector. Extracting quaternions The pitch angle is calculated using the standard formula. Roll angle wait.

[0049] The confidence assessment module then measures the noise covariance in real time based on the noise characteristic estimation output. and filtered information Calculate the comprehensive confidence index The index range is (0,1]; when the system is running well, the sensor noise level is normal, and the model matching degree is high, the Mahalanobis distance of the innovation is small, and and near, It approaches 1; conversely, when encountering unmodeled interference or sensor failure, the innovation increases sharply or Significant deviation from the benchmark value It will drop rapidly; the data encapsulation and push module, according to the ARINC429 specification, will convert the attitude angle... and the corresponding confidence level The data is packaged into a 32-bit tag data word and sent to the flight control computer via two independent physical buses; the flight control computer can then... The magnitude of the value determines whether to accept the attitude data or trigger higher-level system reconfiguration logic.

[0050] Through the collaborative work of the above-mentioned embodiments, the present invention provides a high-precision, high-robustness, and high-reliability aircraft attitude assessment system, which effectively solves the technical problem of multi-source heterogeneous data fusion in complex environments and provides key technical support for improving flight safety.

[0051] All contents not described in detail in the specification are existing technologies known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited; conventional equipment can be used. Electrical control components not mentioned in this technical solution are not shown in the figures because they are existing technologies, and will not be described here.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An aircraft attitude evaluation system based on adaptive nonlinear filtering, characterized in that, It includes a multi-source data acquisition end, an adaptive filtering and fusion end, a noise characteristic estimation end, and an attitude calculation and evaluation end. The multi-source data acquisition end, the adaptive filtering and fusion end, the noise characteristic estimation end, and the attitude calculation and evaluation end communicate bidirectionally via an airborne bus communication protocol, and each end is electrically connected to the central logic processing matrix. The multi-source data acquisition terminal is used to simultaneously acquire atmospheric data and inertial navigation data during aircraft flight, and to preprocess the raw data. The adaptive filtering fusion terminal is used to construct a core fusion model based on the unscented Kalman filter framework, and introduces the idea of ​​a strong tracking filter to dynamically calculate multiple fading factors to adjust the filter gain matrix online and output high-precision aircraft attitude state estimates. The noise characteristic estimation end is used to estimate the system process noise covariance matrix and the measurement noise covariance matrix in real time using the information sequence within the sliding window, and adaptively adjust the noise parameters in the adaptive filtering and fusion end according to the estimation results; The attitude calculation and evaluation terminal is used to calculate the real-time attitude angle of the aircraft based on the state estimate output by the adaptive filtering and fusion terminal, evaluate the confidence level of the attitude estimate, and generate a structured attitude data stream.

2. The system according to claim 1, characterized in that, The multi-source data acquisition terminal includes an atmospheric data acquisition module, an inertial navigation data acquisition module, and a data preprocessing module; The atmospheric data acquisition module is connected to the airspeed tube, static pressure orifice, angle of attack sensor and total temperature sensor via an interface; The inertial navigation data acquisition module is connected to the optical gyroscope and accelerometer via an interface; The data preprocessing module includes a timestamp synchronization unit, a coordinate system alignment unit, and an outlier detection unit. The timestamp synchronization unit is used to align sensor data with different sampling frequencies to the same time reference. The coordinate system alignment unit is used to transform the airflow coordinate system where the atmospheric data is located to the body coordinate system where the inertial navigation system is located. The outlier detection unit is used to identify and remove outliers and dead points in the sensor data.

3. The system according to claim 1, characterized in that, The adaptive filtering fusion terminal includes a model building module, a UKF core filtering module, and a multiple fading factor calculation module; The model building module constructs a nonlinear state transition function based on the aircraft's kinematic equations. and nonlinear measurement function The multiple fading factor calculation module is used to calculate a time-varying multiple fading factor matrix in real time. This is then embedded into the UKF state prediction covariance update formula, with the specific calculation formula as follows: in, The state prediction covariance matrix, for point, This is the predicted state value. The process noise covariance matrix is... It is a matrix of multiple fading factors.

4. The system according to claim 1, characterized in that, The noise characteristic estimation module includes an innovation sequence caching module, an online noise covariance estimation module, and an adaptive adjustment module. The innovation sequence caching module is used to store the innovation vectors generated within consecutive time steps. ; The online noise covariance estimation module estimates the current measurement noise covariance matrix in real time based on the covariance of the innovation sequence samples within the sliding window. process noise covariance matrix .

5. The system according to claim 4, characterized in that, The online noise covariance estimation module estimates using the following set of equations. and : ； ； ;in, For the estimated new information covariance matrix, The length of the sliding window. Let Jacobian matrix be the measurement function. The state prediction covariance matrix, Here is the Kalman gain matrix.

6. The system according to claim 5, characterized in that, The adaptive adjustment module will estimate the... and The feedback is sent to the UKF core filtering module to replace the original fixed noise parameters, thereby achieving online adaptive tracking of noise statistical characteristics.

7. The system according to claim 1, characterized in that, The central logic processing matrix is ​​used to execute cross-end data association logic. When the multi-source data acquisition terminal detects an abnormality in a certain sensor signal, the central logic processing matrix, in conjunction with the noise characteristic estimation terminal, suspends the online estimation of the corresponding measurement noise of the sensor and temporarily sets the value of the corresponding channel in its noise covariance matrix to a larger preset value to reduce the fusion weight of the measurement information of that channel.

8. The system according to claim 1, characterized in that, The attitude calculation and evaluation terminal includes an attitude angle calculation module, a confidence evaluation module, and a data encapsulation and push module; The confidence assessment module is used to calculate the comprehensive confidence index of the current attitude estimate. The calculation formula is as follows: in, Let be the current information vector. The new information covariance matrix, For the estimated measurement noise covariance matrix, The reference measurement noise covariance matrix, This indicates finding the trace of a matrix.

9. The system according to claim 8, characterized in that, The data encapsulation and push module packages the calculated attitude angles and corresponding confidence indices into structured data frames according to the avionics standard bus protocol, and pushes them to the main flight control computer and the backup flight control computer through redundant communication links.

10. The system according to claim 9, characterized in that, The atmospheric data includes at least airspeed, angle of attack, and sideslip angle; the inertial navigation data includes at least three-axis angular velocity and three-axis acceleration; and the real-time attitude angles include at least pitch angle, roll angle, and heading angle.

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