Track filtering method and device based on measurement noise variance in spherical coordinate system

Abstract

Embodiments of the present disclosure disclose a track filtering method and device based on measurement noise variance in a spherical coordinate system. A specific embodiment of the method comprises: determining a mass center dynamic model of an aircraft in a speed coordinate system; determining a state vector for the aircraft according to the mass center dynamic model; constructing a measurement equation corresponding to the measurement data and the state vector; and performing track filtering and characteristic parameter identification on track data according to an extended Kalman filter in a sliding window manner. The embodiment improves the estimation accuracy of the position, speed and acceleration of the aircraft.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of information fusion processing, and specifically to a method and apparatus for trajectory filtering based on the measurement noise variance in spherical coordinates. Background Technology

[0002] Traditional track filtering algorithms are mainly designed based on Cartesian coordinates, requiring coordinate transformation, obtaining noise statistical characteristics, and finally using Kalman filtering for track smoothing.

[0003] However, traditional track filtering algorithms have the following technical problems:

[0004] (1) Most of them are designed based on the rectangular coordinate system, but in practical applications, frequent coordinate transformations are required, which significantly increases the complexity and computation of the algorithm, and the accuracy of acceleration identification is not high.

[0005] (2) A large amount of track data contains noise and usually does not provide statistical characteristics such as variance. When these data are used as measurement data for Kalman filtering, they cannot be used directly due to the lack of variance information.

[0006] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0007] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0008] Some embodiments of this disclosure propose a trajectory filtering method and apparatus based on measuring noise variance in spherical coordinates to solve the technical problems mentioned in the background section above.

[0009] In a first aspect, some embodiments of this disclosure provide a trajectory filtering method based on the measurement noise variance in a spherical coordinate system. The method includes: determining a centroid dynamic model of the aircraft in a velocity coordinate system; determining a state vector for the aircraft based on the centroid dynamic model; constructing a measurement equation corresponding to the measurement data and the state vector; and using a sliding window approach to perform trajectory filtering and feature parameter identification on the trajectory data based on an extended Kalman filter. The extended Kalman filter is constructed by combining the centroid dynamic model and the measurement equation, as well as by real-time estimation of the measurement noise variance.

[0010] Secondly, some embodiments of this disclosure provide a trajectory filtering device based on the measurement noise variance in a spherical coordinate system. The device includes: a first determining unit configured to determine a centroid dynamic model of an aircraft in a velocity coordinate system; a second determining unit configured to determine a state vector for the aircraft based on the centroid dynamic model; a construction unit configured to construct a measurement equation corresponding to the measurement data and the state vector; and a trajectory filtering and feature parameter identification unit configured to perform trajectory filtering and feature parameter identification on the trajectory data using a sliding window method based on an extended Kalman filter, wherein the extended Kalman filter is constructed by combining the centroid dynamic model and the measurement equation, and by real-time estimation of the measurement noise variance.

[0011] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0012] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0013] The various embodiments of this disclosure have the following beneficial effects: The trajectory filtering method based on measurement noise variance in a spherical coordinate system, as described in some embodiments of this disclosure, firstly, establishes the centroid dynamic equation of the aircraft in the velocity coordinate system based on the aircraft's motion characteristics and range, thereby obtaining a simplified parametric dynamic model in the spherical coordinate system. Then, state prediction equations and measurement equations are established, and a sliding window-type real-time estimation method for measurement noise variance is used to obtain the statistical characteristics such as noise variance. Finally, an extended Kalman filter is introduced to perform trajectory filtering and smoothing on the (raw) trajectory data of ADS-B (Automatic Dependent Surveillance-Broadcast, an air traffic surveillance application used to transmit flight parameters). This disclosure, through a trajectory filtering algorithm designed based on a spherical coordinate system and a sliding window-type real-time estimation method for measurement noise variance to obtain the statistical characteristics such as noise variance, significantly reduces the computational load and complexity of algorithms in traditional rectangular coordinate systems. It can obtain real-time noise variance estimation of trajectory measurement data, effectively improving the accuracy of aircraft position, velocity, and acceleration estimation. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0015] Figure 1 This is a flowchart of some embodiments of the trajectory filtering method based on measuring noise variance in spherical coordinates according to the present disclosure;

[0016] Figure 2 This is a visual example of flight track data;

[0017] Figure 3 This is a schematic diagram showing the positional relationship between the sliding window and the track data;

[0018] Figure 4 It is a visual comparison chart of theoretical and measured values ​​at different times;

[0019] Figure 5 It is a visual comparison chart of theoretical values ​​and track errors at different times;

[0020] Figure 6 This is a schematic diagram illustrating the movement of the sliding window;

[0021] Figure 7 This is a comparison chart of acceleration identification results in rectangular coordinate system and spherical coordinate system;

[0022] Figure 8 This is a schematic diagram illustrating how the standard deviation of precision changes over time.

[0023] Figure 9 This is a schematic diagram of the structure of some embodiments of a trajectory filtering device for measuring noise variance in spherical coordinates according to the present disclosure;

[0024] Figure 10 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0025] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0026] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0027] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0028] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0029] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0030] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] refer to Figure 1 The flowchart 100 illustrates some embodiments of a trajectory filtering method based on measuring noise variance in a spherical coordinate system according to the present disclosure. This trajectory filtering method based on measuring noise variance in a spherical coordinate system includes the following steps:

[0032] Step 101: Determine the center-of-mass dynamic model of the aircraft in the velocity coordinate system.

[0033] In some embodiments, the implementer (e.g., a computing device) of a trajectory filtering method based on measuring noise variance in spherical coordinates can determine the centroid dynamic model of the aircraft in the velocity coordinate system.

[0034] In practice, based on the aircraft's motion characteristics and range, a spherical model is adopted. Considering the Earth's rotation, overall computational efficiency, and accuracy, a center-of-mass dynamic model of the aircraft in the velocity coordinate system can be obtained. This center-of-mass dynamic model can be characterized by the following formula:

[0035]

[0036] in, For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, The latitude of the Earth's core. For the aircraft to be thrust by the engine, The angle between the velocity axis and the body axis. For the quality of the aircraft, As resistance, For gravitational acceleration, This is the Earth's rotational angular rate. For lift, In the velocity coordinate system The angle between the shaft and the total lift. , , , , , All of these are intermediate variables.

[0037] However, in cases where accurate overall aircraft parameters are unavailable, it is impossible to accurately establish a thrust and aerodynamic model by combining the above equations. Therefore, this disclosure merges the forces, that is, updates the above equations using the three components of the resultant acceleration. The updated center-of-mass dynamics model is characterized by the following equations:

[0038]

[0039] in, For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, The latitude of the Earth's core. For the aircraft to be thrust by the engine, The angle between the velocity axis and the body axis. For the quality of the aircraft, As resistance, For gravitational acceleration, This is the Earth's rotational angular rate. For lift, In the velocity coordinate system The angle between the shaft and the total lift. In the velocity coordinate system, the longitudinal acceleration, controlled by the resultant force acceleration in the horizontal direction, governs acceleration and deceleration. In the velocity coordinate system, the resultant force acceleration controls the vertical acceleration in the vertical direction, which controls the upward and downward movement. In the velocity coordinate system, the resultant force acceleration controls the left and right steering in the lateral direction. , , , , , All of these are intermediate variables.

[0040] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0041] Step 102: Determine the state vector for the aircraft based on the center of mass dynamics model.

[0042] In some embodiments, the aforementioned executing entity may determine the state vector for the aircraft based on the center-of-mass dynamics model.

[0043] The state vector can be characterized by the following formula:

[0044]

[0045] in, Represents the state vector. For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, The latitude of the Earth's core. In the velocity coordinate system, the longitudinal acceleration, controlled by the resultant force acceleration in the horizontal direction, governs acceleration and deceleration. In the velocity coordinate system, the resultant force acceleration controls the vertical acceleration in the vertical direction, which controls the upward and downward movement. In the velocity coordinate system, the resultant force acceleration controls the left and right steering in the lateral direction. This represents the transpose of the matrix.

[0046] The system of derivative equations corresponding to the state vector can be characterized by the following formula:

[0047]

[0048] in, This represents the system of derivative equations corresponding to the state vector. , , , , , All are intermediate variables. Represents the position along the velocity coordinate system. Axial noise, Represents the position along the velocity coordinate system. Axial noise, Represents the position along the velocity coordinate system. Noise in the axial direction.

[0049] The derivative equations corresponding to the state vector can also be expanded into the following form:

[0050]

[0051] In particular, when the detection interval When it is short, that is, the time interval between measurement data at two consecutive moments (detection interval) When the length is relatively short, the state vector can also be discretized into the following form:

[0052]

[0053] in, Represents the discrete first... The state vector corresponding to each time point Represents the discrete first... The state vector corresponding to each time point Indicates the first The system of derivative equations corresponding to the state vector at each time step.

[0054] Step 103: Construct the measurement equations corresponding to the measurement data and the state vector.

[0055] In some embodiments, the aforementioned execution entity may construct measurement equations corresponding to measurement data and state vectors.

[0056] The measurement equation is characterized by the following formula:

[0057]

[0058] in, Represents measurement data, Representing the measurement equation, Let represent the state vector, where , Longitude of the Earth's core The latitude of the Earth's core. For heading angle, The height of the earth. For ground speed, For the climb rate, is the average radius of the Earth.

[0059] The measurement equation can also be characterized by the following formula:

[0060]

[0061] in, For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, It is the geocentric latitude.

[0062] Step 104: Using a sliding window approach, track filtering and feature parameter identification are performed on the track data based on the extended Kalman filter.

[0063] In some embodiments, the aforementioned execution entity may employ a sliding window approach to perform trajectory filtering and feature parameter identification on the trajectory data based on an extended Kalman filter. The extended Kalman filter is constructed by combining a centroid dynamics model and measurement equations, and by real-time estimation of measurement noise variance.

[0064] In some optional implementations of certain embodiments, the aforementioned execution entity employs a sliding window approach to perform track filtering and feature parameter identification on the track data based on an extended Kalman filter, including:

[0065] Step S1: Determine the window size corresponding to the sliding window .

[0066] As an example, see Figure 2 The example visualization of the trajectory data shown is as follows, where the trajectory data can be represented as... ,in, Indicates the serial number. Indicates the first At that moment, Indicates the first The measured values ​​in the track data corresponding to each time point. Indicates the length of the track data.

[0067] As another example, see Figure 3 The diagram shown illustrates the positional relationship between the sliding window and the track data. Figure 3 The selected first The size of a sliding window (ith window) The value of is 4. In particular, in this disclosure, the sliding window size... The value is 20.

[0068] Step S2: Determine the order of the polynomial fitting .

[0069] Among them, the order of polynomial fitting The value of is 3.

[0070] Step S3: Fit the order of the polynomial Construct a polynomial.

[0071] The polynomial can be characterized by the following formula:

[0072]

[0073] in, Indicates time The corresponding theoretical value, Represents the polynomial coefficients. Indicates the sequence number, especially when the order of the polynomial fitting is... When the value of is 3, it indicates that the polynomial expansion is a third-order expansion, meaning that there are 4 polynomial coefficients, namely _____. , , , .

[0074] Step S4: Construct the error function .

[0075] Wherein, error function It can be represented by the following formula:

[0076]

[0077] Step S5: Based on the error function The polynomial coefficients are obtained by solving for the polynomial coefficients.

[0078] In practice, in order to minimize the error function The function value can be represented by differentiating the polynomial separately and setting its value to 0. Specifically, it can be characterized by the following formula:

[0079]

[0080] Then, the specific values ​​of the polynomial coefficients can be obtained by solving the problem.

[0081] As an example, see Figure 4 The image shows a visual comparison of the theoretical and measured values ​​at different times.

[0082] Step S6: Determine the theoretical values ​​corresponding to the track data within the sliding window based on the polynomial coefficients.

[0083] In practice, after knowing the specific values ​​of the polynomial coefficients, the time step can be obtained by solving the polynomial described above. Corresponding theoretical value The specific values ​​are then determined, thereby establishing the theoretical values ​​corresponding to the track data within the sliding window.

[0084] Step S7: Determine the track error based on the theoretical value corresponding to the track data within the sliding window and the track data within the sliding window.

[0085] After obtaining the specific values ​​of the theoretical values ​​corresponding to the track data within the sliding window, the difference between the measured value and the corresponding theoretical value is determined as the track error. The track error can be determined by the following formula:

[0086]

[0087] in, Indicates the first Track error at a given moment Indicates the first The measured values ​​in the track data corresponding to each time point represent Indicates time The corresponding theoretical value.

[0088] As an example, see Figure 5 The image shows a visual comparison of the theoretical values ​​and track errors at different times.

[0089] Step S8: Determine the error statistical variance and error standard deviation corresponding to the track error.

[0090] In practice, the statistical variance of the error can be determined using the following formula:

[0091]

[0092] in, This represents the statistical variance of the error. Indicates the first Track error at a given moment Let be the mean error, where .

[0093] The standard deviation of the error can be determined using the following formula:

[0094]

[0095] in, It represents the standard deviation of the error.

[0096] In addition, the sliding window can be controlled to move backward to control... Increment by 1 until the sliding window reaches the last data point in the track data.

[0097] As an example, Figure 6 This is a diagram illustrating the movement of a sliding window. Specifically, Figure 6This illustrates the process of moving the "ith window" slider to the "i+1th window" slider.

[0098] Step S9: Determine the measurement data covariance matrix corresponding to the extended Kalman filter based on the error statistical variance.

[0099] In practice, the noise variance of the measurement data is obtained by solving the statistical variance of the error.

[0100] Optionally, the following steps may also be included:

[0101] Step S10: When obtained Time measurement data, to determine Is the measurement data at any given time a bad pixel?

[0102] First step, let the state estimate be... The covariance is estimated as .

[0103] in, ,express for The first 6 rows of data. ,express for The data consists of 6×6 sets.

[0104] The second step is to predict the state of the dynamic model, which can be represented by the following formula:

[0105]

[0106] in, , indicating the detection interval , It is a 3rd order identity matrix. It is a 3x3 zero matrix.

[0107] The third step is to predict the covariance.

[0108] The covariance prediction process can be characterized by the following formula:

[0109]

[0110] in, express The transpose of .

[0111] The fourth step is to conduct measurement and prediction.

[0112] The measurement and prediction process can be characterized by the following formula:

[0113]

[0114] in, This represents the measurement equation.

[0115] Step 5: Define the measurement matrix .

[0116] in, .in, express time for The partial differential of .

[0117] Step 6: Calculate the new information .

[0118] in, .

[0119] Step 7, combine new information Measurement matrix Determine the new information covariance matrix .

[0120] in, .

[0121] Step 8: If the Mahalanobis distance of the new information is greater than a given threshold Then it is believed The measurement data at time point is defective. Therefore, by combining the centroid dynamics model and the discretized state vector, prediction is needed. The state update value corresponding to each moment.

[0122] Wherein, the Mahalanobis distance of the new information is related to a given threshold. The comparison can be represented by the following formula:

[0123]

[0124] Among them, combining the centroid dynamics model and the discretized state vector, prediction The process of updating the state value at time t can be represented by the following formula:

[0125]

[0126] in, It is a 3x6 zero matrix. It is a 6-row, 3-column zero matrix.

[0127] Step 9: If the Mahalanobis distance of the new information is less than or equal to a given threshold Then it is believed The measurement data at any given time are not bad data, so they can be filtered and smoothed.

[0128] Specifically, filtering and smoothing include:

[0129] The first sub-step involves state prediction using the following formula:

[0130]

[0131] The equation corresponding to the discretized state vector is:

[0132]

[0133] The second sub-step involves predicting the covariance using the following formula:

[0134]

[0135] in, Here is the state transition matrix. Let be the process noise covariance matrix.

[0136] in, .

[0137] in, For the maneuver time constant, For the target maximum acceleration, It is a 6x6 zero matrix. The detection interval.

[0138] The third sub-step involves calculating the gain matrix. .

[0139] in, .in, The covariance matrix of the measurement data is obtained from step S9 above.

[0140] The fourth sub-step involves updating the state estimate using the following formula:

[0141] .

[0142] The fifth sub-step involves updating the covariance using the following formula:

[0143] .

[0144] The various embodiments of this disclosure have the following beneficial effects: The trajectory filtering method based on measurement noise variance in a spherical coordinate system, as described in some embodiments of this disclosure, firstly, establishes the centroid dynamic equation of the aircraft in the velocity coordinate system based on the aircraft's motion characteristics and range, thereby obtaining a simplified parametric dynamic model in the spherical coordinate system. Then, state prediction equations and measurement equations are established, and a sliding window-type real-time estimation method for measurement noise variance is used to obtain the statistical characteristics such as noise variance. Finally, an extended Kalman filter is introduced to perform trajectory filtering and smoothing on the raw ADS-B (Automatic Dependent Surveillance-Broadcast, an air traffic surveillance application used to transmit flight parameters) trajectory data. This disclosure, through a trajectory filtering algorithm designed based on a spherical coordinate system and a sliding window-type real-time estimation method for measurement noise variance to obtain the statistical characteristics such as noise variance, significantly reduces the computational load and complexity of algorithms in traditional rectangular coordinate systems. It can obtain real-time noise variance estimation of trajectory measurement data, effectively improving the accuracy of aircraft position, velocity, and acceleration estimation.

[0145] The simulation results are as follows:

[0146] (1) Data source

[0147] Among them, using flight track data of a certain type of fighter jet on July 16, 2024, the following simulation experiment was conducted based on MATLAB R2023a.

[0148] (2) Comparison of algorithms in different coordinate systems

[0149] The effectiveness of the algorithm was verified by processing (raw) track data in different coordinate systems. Specifically, the algorithm's computation time was 1.005136 seconds in Cartesian coordinates and 0.764280 seconds in spherical coordinates. This demonstrates that processing track data in spherical coordinates is faster than in Cartesian coordinates, significantly reducing algorithm complexity and computational load, which aligns with the algorithm's expectations.

[0150] (3) Comparison of acceleration estimation accuracy in different coordinate systems

[0151] Among them, using (raw) track data, and employing algorithms in different coordinate systems, an extended Kalman filter is used for track filtering and acceleration identification. The identification results are as follows: Figure 7 (As shown in the comparison diagram of acceleration identification results in rectangular coordinate system and spherical coordinate system), the analysis... Figure 7 It is evident that the acceleration identification and estimation obtained in the spherical coordinate system is more accurate than that in the rectangular coordinate system.

[0152] (4) Analysis of simulation results for real-time estimation of variance of measurement data

[0153] Among them, the visualization results of the longitude standard deviation obtained by using a sliding window-based adaptive estimation algorithm for measurement noise variance based on (raw) track data are as follows: Figure 8 (A schematic diagram illustrating the change in the standard deviation of precision over time is shown, in which the analysis...) Figure 8 As can be seen, the noise standard deviation estimate of the track measurement data is obtained in real time through this disclosure, and the extended Kalman filter can be directly used for track filtering and identification.

[0154] In summary, firstly, the track filtering method based on measurement noise variance in spherical coordinates disclosed in this paper obtains real-time noise variance estimates of track measurement data, allowing for direct smoothing of the track using an extended Kalman filter. Secondly, processing track data in spherical coordinates is faster than in rectangular coordinates, significantly reducing algorithm complexity and computational load. Finally, the acceleration identification estimation obtained in spherical coordinates has higher accuracy compared to rectangular coordinates.

[0155] Further reference Figure 9 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a trajectory filtering device based on measuring noise variance in a spherical coordinate system. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this track filtering device based on measuring noise variance in spherical coordinates can be specifically applied to various electronic devices.

[0156] like Figure 9 As shown, a trajectory filtering device 900 based on measurement noise variance in spherical coordinates in some embodiments includes: a first determining unit 901, a second determining unit 902, a construction unit 903, and a trajectory filtering and feature parameter identification unit 904. The first determining unit 901 is configured to determine the centroid dynamic model of the aircraft in the velocity coordinate system; the second determining unit 902 is configured to determine the state vector for the aircraft based on the centroid dynamic model; the construction unit 903 is configured to construct measurement equations corresponding to the measurement data and the state vectors; the trajectory filtering and feature parameter identification unit 904 is configured to perform trajectory filtering and feature parameter identification on the trajectory data using a sliding window method based on an extended Kalman filter, wherein the extended Kalman filter is constructed by combining the centroid dynamic model and the measurement equations, and by real-time estimation of the measurement noise variance.

[0157] It is understandable that the units described in the track filtering device 900, which measures noise variance in spherical coordinates, and the reference... Figure 1The steps described in the method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the track filtering device 900 and its constituent units based on measuring noise variance in spherical coordinates, and will not be repeated here.

[0158] The following is for reference. Figure 10 It illustrates a schematic diagram of the structure of an electronic device (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. Figure 10 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of this disclosure. Figure 10 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods described above. The processor provides computational and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium; when executed by the processor, the computer program causes the processor to perform any of the methods described above. The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0159] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0160] In one embodiment, the processor is used to run a computer program stored in a memory to perform the following steps: determining the centroid dynamic model of the aircraft in the velocity coordinate system; determining the state vector of the aircraft based on the centroid dynamic model; constructing the measurement equation corresponding to the measurement data and the state vector; and using a sliding window approach to perform track filtering and feature parameter identification on the track data based on an extended Kalman filter, wherein the extended Kalman filter is constructed by combining the centroid dynamic model and the measurement equation, and by real-time estimation of the measurement noise variance.

[0161] This disclosure also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the method implemented when the program instructions are executed can be referred to the various embodiments of the methods described above.

[0162] The aforementioned computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. Alternatively, the aforementioned computer-readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0163] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0164] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A trajectory filtering method based on measuring noise variance in spherical coordinates, characterized in that, include: Determine the center-of-mass dynamic model of the aircraft in the velocity coordinate system; Based on the aforementioned center-of-mass dynamics model, determine the state vector for the aircraft; Construct measurement equations corresponding to the measurement data and the state vector; A sliding window approach is used to perform track filtering and feature parameter identification on track data based on an extended Kalman filter. The extended Kalman filter is constructed by combining the centroid dynamics model and measurement equations, as well as by estimating the variance of measurement noise in real time. The centroid dynamic model is characterized by the following equation: ， in, For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, The latitude of the Earth's core. For the aircraft to be thrust by the engine, The angle between the velocity axis and the body axis. For the quality of the aircraft, As resistance, For gravitational acceleration, This is the Earth's rotational angular rate. For lift, In the velocity coordinate system The angle between the shaft and the total lift. In the velocity coordinate system, the longitudinal acceleration, controlled by the resultant force acceleration in the horizontal direction, governs acceleration and deceleration. In the velocity coordinate system, the resultant force acceleration controls the vertical acceleration in the vertical direction, which controls the upward and downward movement. In the velocity coordinate system, the resultant force acceleration controls the left and right steering in the lateral direction. , , , , , All are intermediate variables; The method of using a sliding window to perform track filtering and feature parameter identification on track data based on an extended Kalman filter includes: Determine the window size corresponding to the sliding window ; Determine the order of the polynomial fitting ; Based on the order of polynomial fitting Construct a polynomial; Constructing the error function ; According to the error function Solve for the polynomial coefficients to obtain the polynomial coefficients; Based on the polynomial coefficients, determine the theoretical values ​​corresponding to the track data within the sliding window; The trajectory error is determined based on the theoretical value corresponding to the trajectory data within the sliding window and the trajectory data within the sliding window. Determine the error statistical variance and error standard deviation corresponding to the track error; Based on the error statistical variance, determine the measurement data covariance matrix corresponding to the extended Kalman filter.

2. The method according to claim 1, characterized in that, The state vector is characterized by the following formula: in, Represents the state vector. For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, The latitude of the Earth's core. In the velocity coordinate system, the longitudinal acceleration, controlled by the resultant force acceleration in the horizontal direction, governs acceleration and deceleration. In the velocity coordinate system, the resultant force acceleration controls the vertical acceleration in the vertical direction, which controls the upward and downward movement. In the velocity coordinate system, the resultant force acceleration controls the left and right steering in the lateral direction. Represents the transpose matrix; The derivative equations corresponding to the state vector are characterized by the following formulas: in, This represents the system of derivative equations corresponding to the state vector. , , , , , All are intermediate variables. Represents the position along the velocity coordinate system. Axial noise, Represents the position along the velocity coordinate system. Axial noise, Represents the position along the velocity coordinate system. Noise in the axial direction.

3. The method according to claim 2, characterized in that, The measurement equation is characterized by the following formula: in, Represents measurement data, Representing the measurement equation, Let represent the state vector, where , Longitude of the Earth's core The latitude of the Earth's core. For heading angle, The height of the earth. For ground speed, For the climb rate, is the average radius of the Earth.

4. A trajectory filtering device based on measuring noise variance in spherical coordinates, characterized in that, include: The first determining unit is configured to determine the center-of-mass dynamic model of the aircraft in the velocity coordinate system, wherein the center-of-mass dynamic model is characterized by the following equations: ， in, For the geocentric radius, For flight speed, The angle between the ground plane and the velocity axis. Longitude of the Earth's core For heading angle, The latitude of the Earth's core. For the aircraft to be thrust by the engine, The angle between the velocity axis and the body axis. For the quality of the aircraft, As resistance, For gravitational acceleration, This is the Earth's rotational angular rate. For lift, In the velocity coordinate system The angle between the shaft and the total lift. In the velocity coordinate system, the longitudinal acceleration, controlled by the resultant force acceleration in the horizontal direction, governs acceleration and deceleration. In the velocity coordinate system, the resultant force acceleration controls the vertical acceleration in the vertical direction, which controls the upward and downward movement. In the velocity coordinate system, the resultant force acceleration controls the left and right steering in the lateral direction. , , , , , All are intermediate variables; The second determining unit is configured to determine the state vector for the aircraft based on the center-of-mass dynamics model. The construction unit is configured to construct a measurement equation corresponding to the measurement data and the state vector; The track filtering and feature parameter identification unit is configured to use a sliding window approach to perform track filtering and feature parameter identification on the track data based on an extended Kalman filter. The extended Kalman filter is constructed by combining the centroid dynamics model and measurement equations, and by real-time estimation of measurement noise variance. The track filtering and feature parameter identification unit is further configured as follows: Determine the window size corresponding to the sliding window ; Determine the order of the polynomial fitting ; Based on the order of polynomial fitting Construct a polynomial; Constructing the error function ; According to the error function Solve for the polynomial coefficients to obtain the polynomial coefficients; Based on the polynomial coefficients, determine the theoretical values ​​corresponding to the track data within the sliding window; The trajectory error is determined based on the theoretical value corresponding to the trajectory data within the sliding window and the trajectory data within the sliding window. Determine the error statistical variance and error standard deviation corresponding to the track error; Based on the error statistical variance, determine the measurement data covariance matrix corresponding to the extended Kalman filter.

5. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 3.

6. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 3.

Images