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

Abstract

The embodiment of the invention discloses a track filtering method and device based on measurement noise variance under a spherical coordinate system. A specific embodiment of the method comprises the following steps: determining a centroid dynamic model of an aircraft under 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 carrying out track filtering and characteristic parameter identification on the track data according to the expanded Kalman filter by adopting a sliding window mode. According to the embodiment, the position, speed and acceleration estimation precision of the aircraft is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of information fusion processing, and more particularly to a track filtering method and apparatus based on measurement noise variance in a spherical coordinate system. Background Art

[0002] Traditional track filtering algorithms are mainly designed based on rectangular coordinate systems, which require coordinate transformation, obtaining noise statistical characteristics, and finally using Kalman filtering for track smoothing.

[0003] However, the traditional track filtering algorithm has the following technical problems:

[0004] (1) Most of them are designed based on rectangular coordinate systems, but in actual applications, frequent coordinate transformations are required, which significantly increases the algorithm complexity and computational complexity, and the recognition accuracy of acceleration 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 directly used due to the lack of variance information.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0008] Some embodiments of the present disclosure propose a track filtering method and apparatus based on measurement noise variance in a spherical coordinate system to solve the technical problems mentioned in the above background technology section.

[0009] In a first aspect, some embodiments of the present disclosure provide a track filtering method based on measurement noise variance in a spherical coordinate system, the method comprising: determining the center of mass dynamics model of the aircraft in a velocity coordinate system; determining a state vector for the aircraft based on the above-mentioned center of mass dynamics model; constructing a measurement equation corresponding to the measurement data and the above-mentioned state vector; and performing track filtering and characteristic parameter identification on the track data based on an extended Kalman filter using a sliding window method, wherein the extended Kalman filter is constructed in combination with the center of mass dynamics model and the measurement equation, and is constructed through real-time estimation of the measurement noise variance.

[0010] In the second aspect, some embodiments of the present disclosure provide a track filtering device based on measurement noise variance in a spherical coordinate system, the device comprising: a first determination unit, configured to determine the center of mass dynamics model of the aircraft in a velocity coordinate system; a second determination unit, configured to determine the state vector for the aircraft based on the above-mentioned center of mass dynamics model; a construction unit, configured to construct a measurement equation corresponding to the measurement data and the above-mentioned state vector; a track filtering and characteristic parameter identification unit, configured to perform track filtering and characteristic parameter identification on the track data according to an extended Kalman filter in a sliding window manner, wherein the extended Kalman filter is constructed in combination with the center of mass dynamics model and the measurement equation, and is constructed by real-time estimation of the measurement noise variance.

[0011] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0012] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0013] The above-described various embodiments of the present disclosure have the following beneficial effects: Through the track filtering method based on measurement noise variance in a spherical coordinate system, some embodiments of the present disclosure first establish the aircraft's center of mass dynamics equation in a velocity coordinate system based on the aircraft's motion characteristics and range, thereby obtaining a simplified parametric dynamic model in the spherical coordinate system. Next, a state prediction equation and a measurement equation are established, and a sliding window-based real-time estimation method for measurement noise variance is used to obtain statistical properties such as noise variance. Finally, an extended Kalman filter is introduced to perform track filtering and smoothing on ADS-B (Automatic Dependent Surveillance-Broadcast, an air traffic surveillance application for transmitting flight parameters) (raw) track data. By designing a track filtering algorithm based on a spherical coordinate system and utilizing a sliding window-based real-time estimation method for measurement noise variance to obtain statistical properties such as noise variance, the present disclosure significantly reduces the computational complexity and complexity of algorithms in traditional rectangular coordinate systems. This method enables real-time estimation of the noise variance of track measurement data, effectively improving the accuracy of aircraft position, velocity, and acceleration estimates. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0015] Figure 1 is a flow chart of some embodiments of a track filtering method based on measurement noise variance in a spherical coordinate system according to the present disclosure;

[0016] Figure 2 This is an example visualization of track data;

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

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

[0019] Figure 5 It is a visual comparison chart of the theoretical value and track error at different times;

[0020] Figure 6 It is a schematic diagram of the moving process 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 It is a schematic diagram of the process of the standard deviation of accuracy changing over time;

[0023] Figure 9 1 is a schematic structural diagram of some embodiments of a track filtering device based on measurement noise variance in a spherical coordinate system according to the present disclosure;

[0024] Figure 10 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0025] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0026] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

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

[0028] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

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

[0030] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0031] refer to Figure 1 , shows a process 100 of some embodiments of the track filtering method based on measurement noise variance in a spherical coordinate system according to the present disclosure. The track filtering method based on measurement noise variance in a spherical coordinate system includes the following steps:

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

[0033] In some embodiments, an executing entity (eg, a computing device) of the track filtering method based on measurement noise variance in a spherical coordinate system may determine a center of mass dynamics model of the aircraft in a velocity coordinate system.

[0034] In practice, based on the aircraft's motion characteristics and range, a spherical model is used. Taking into account the Earth's rotation, comprehensive computational efficiency, and accuracy, the center-of-mass dynamics model of the aircraft in the velocity coordinate system can be obtained. The center-of-mass dynamics model can be represented by the following formula:

[0035] in, is the geocentric radius, is the flight speed, is the angle between the ground plane and the velocity axis, is the geocentric longitude, is the heading angle, is the geocentric latitude, The aircraft is subjected to engine thrust. is the angle between the speed axis and the body axis, is the mass of the aircraft, For resistance, is the gravitational acceleration, is the Earth's rotation angular rate, For lift, In the velocity coordinate system The angle from the axis to the total lift, 、 、 、 、 、 All are intermediate variables.

[0036] However, if the accurate overall aircraft parameters are unavailable, it is impossible to combine the above equations to accurately establish a thrust and aerodynamic model. Therefore, the present disclosure combines the forces, that is, uniformly updates the above equations using the three components of the resultant acceleration. The updated center of mass dynamic model is represented by the following equation:

[0037] in, is the geocentric radius, is the flight speed, is the angle between the ground plane and the velocity axis, is the geocentric longitude, is the heading angle, is the geocentric latitude, The aircraft is subjected to engine thrust. is the angle between the speed axis and the body axis, is the mass of the aircraft, For resistance, is the gravitational acceleration, is the Earth's rotation angular rate, For lift, In the velocity coordinate system The angle from the axis to the total lift, is the longitudinal acceleration in the velocity coordinate system, which controls the acceleration and deceleration in the horizontal direction by the resultant acceleration. is the vertical acceleration in the velocity coordinate system, where the resultant acceleration controls the vertical acceleration of the vehicle. is the lateral acceleration in the velocity coordinate system, the resultant acceleration in the lateral direction controls the left and right steering, 、 、 、 、 、 All are intermediate variables.

[0038] It should be noted that the computing device described above 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 in the hardware devices listed above. It can be implemented as multiple software or software modules, for example, to provide distributed services, or as a single software or software module. No specific limitations are given here.

[0039] Step 102: Determine a state vector for the aircraft based on a center of mass dynamics model.

[0040] In some embodiments, the execution entity may determine a state vector for the aircraft based on a center of mass dynamics model.

[0041] Among them, the state vector can be represented by the following formula:

[0042]

[0043] in, represents the state vector, is the geocentric radius, is the flight speed, is the angle between the ground plane and the velocity axis, is the geocentric longitude, is the heading angle, is the geocentric latitude, is the longitudinal acceleration in the velocity coordinate system, which controls the acceleration and deceleration in the horizontal direction by the resultant acceleration. is the vertical acceleration in the velocity coordinate system, where the resultant acceleration controls the vertical acceleration of the vehicle. is the lateral acceleration in the velocity coordinate system, the resultant acceleration in the lateral direction controls the left and right steering, Represents the transposed matrix.

[0044] Among them, the derivative equations corresponding to the state vector can be represented by the following formula:

[0045]

[0046] in, represents the derivative equations corresponding to the state vector, 、 、 、 、 、 are intermediate variables. Indicates the velocity coordinate system along Axis noise, Indicates the velocity coordinate system along Axis noise, Indicates the velocity coordinate system along Axis noise.

[0047] Among them, the derivative equations corresponding to the state vector can also be expanded into the following form:

[0048] In particular, when the detection interval When it is shorter, that is, the time interval of the measurement data at two consecutive moments (detection interval ) is shorter, the state vector can also be discretized into the following form:

[0049]

[0050] in, Represents the discretized The state vector corresponding to the moment, Represents the discretized The state vector corresponding to the moment, Indicates the The derivative equations corresponding to the state vector at each moment.

[0051] Step 103: construct a measurement equation corresponding to the measurement data and the state vector.

[0052] In some embodiments, the execution entity may construct a measurement equation corresponding to the measurement data and the state vector.

[0053] The measurement equation is represented by the following formula:

[0054]

[0055] in, Represents measurement data, represents the measurement equation, represents the state vector, where , is the geocentric longitude, is the geocentric latitude, is the heading angle, is the earth's height, is the ground speed, is the climb rate, is the average radius of the Earth.

[0056] The measurement equation can also be represented by the following formula:

[0057]

[0058] in, is the geocentric radius, is the flight speed, is the angle between the ground plane and the velocity axis, is the geocentric longitude, is the heading angle, is the geocentric latitude.

[0059] Step 104 , using a sliding window method, performs track filtering and characteristic parameter identification on the track data according to the extended Kalman filter.

[0060] In some embodiments, the execution entity may employ a sliding window approach to perform track filtering and feature parameter identification on the track data using an extended Kalman filter, wherein the extended Kalman filter is constructed by combining a center of mass dynamics model and a measurement equation, and by real-time estimation of measurement noise variance.

[0061] In some optional implementations of some embodiments, the execution entity uses a sliding window method to perform track filtering and feature parameter identification on the track data according to the extended Kalman filter, including:

[0062] Step S1: Determine the window size corresponding to the sliding window .

[0063] For example, see Figure 2 The visualization example of the track data shown in the figure, where the track data can be represented as ,in, Indicates the serial number, Indicates the A moment, Indicates the The measurement value in the track data corresponding to the moment. Indicates the data length of the track data.

[0064] As yet another example, see Figure 3 The schematic diagram of the position relationship between the sliding window and the track data shown in FIG. Figure 3 The selected Sliding window size The value of is 4. In particular, in the present disclosure, the sliding window size The value of is 20.

[0065] Step S2: Determine the polynomial fitting order .

[0066] Among them, the polynomial fitting order The value of is 3.

[0067] Step S3: According to the polynomial fitting order , construct the polynomial.

[0068] Among them, the polynomial can be represented by the following formula:

[0069]

[0070] in, Indicates time The corresponding theoretical value is represents the polynomial coefficients, Indicates the sequence number, especially when the polynomial fitting order When the value of is 3, it means that the polynomial expansion is a third-order expansion, that is, there are 4 polynomial coefficients, which are 、 、 、 .

[0071] Step S4: Construct error function .

[0072] Among them, the error function It can be represented by the following formula:

[0073]

[0074] Step S5: According to the error function Solve the polynomial coefficients of the polynomial to obtain the polynomial coefficients.

[0075] In practice, to minimize the error function The function value of can be derived from the polynomial and set to 0. It can be represented by the following formula:

[0076]

[0077] Then the specific values ​​of the polynomial coefficients are obtained.

[0078] For example, see Figure 4 A visual comparison of theoretical and measured values ​​at different times is shown.

[0079] Step S6: Determine the theoretical value corresponding to the track data in the sliding window according to the polynomial coefficients.

[0080] In practice, after knowing the specific values ​​of the polynomial coefficients, we can solve the time The corresponding theoretical value The specific value of . Thus the theoretical value corresponding to the track data in the sliding window is determined.

[0081] Step S7: Determine the track error based on the theoretical value corresponding to the track data in the sliding window and the track data in the sliding window.

[0082] After obtaining the specific value of the theoretical value corresponding to the track data in the sliding window, the difference between the measured value and the corresponding theoretical value is determined as the track error, where the track error can be determined by the following formula:

[0083]

[0084] in, Indicates the Track error at each moment, Indicates the The measurement value in the track data corresponding to the moment is represented by Indicates time The corresponding theoretical value.

[0085] For example, see Figure 5 The visual comparison diagram of the theoretical value and track error at different times is shown.

[0086] Step S8: Determine the error statistical variance and error standard deviation corresponding to the track error.

[0087] In practice, the error statistical variance can be determined by the following formula:

[0088]

[0089] in, represents the error statistical variance, Indicates the Track error at each moment, is the mean error, where .

[0090] The error standard deviation can be determined by the following formula:

[0091]

[0092] in, represents the standard deviation of error.

[0093] In addition, the sliding window can be controlled to move backward to control Increment by 1 until the sliding window slides to the last data in the track data.

[0094] As an example, Figure 6 This is a schematic diagram of the sliding window movement process. Specifically, Figure 6 The figure shows the process of moving the "ith window" sliding window to the "i+1th window" sliding window.

[0095] Step S9: Determine the measurement data covariance matrix corresponding to the extended Kalman filter based on the error statistical variance.

[0096] In practice, the noise variance of the measurement data is obtained by solving the statistical variance of the error.

[0097] Optionally, the following steps may also be included:

[0098] Step S10: When the Measurement data at the moment, judgment Whether the measured data at the moment is bad pixel data.

[0099] In the first step, let the state estimate be , the covariance estimate is .

[0100] in, ,express for The first 6 rows of data in . ,express for The 6×6 groups of data in .

[0101] The second step is to predict the state of the dynamic model, which can be represented by the following formula:

[0102]

[0103] in, , indicating the detection interval , is a 3rd order unit array, is a 3-row, 3-column zero matrix.

[0104] The third step is to perform covariance prediction.

[0105] Among them, the covariance prediction process can be characterized by the following formula:

[0106]

[0107] in, express The transposed matrix of .

[0108] The fourth step is to make measurement predictions.

[0109] The measurement prediction process can be characterized by the following formula:

[0110]

[0111] in, Represents the measurement equation.

[0112] Step 5: Define the measurement matrix .

[0113] in, .in, express Time for The partial differential of .

[0114] Step 6: Calculate new interest .

[0115] in, .

[0116] Step 7: Combine new information , measurement matrix , determine the innovation covariance matrix .

[0117] in, .

[0118] Step 8: If the Mahalanobis distance of the new information is greater than the given threshold , then it is believed that The measured data at the moment is bad point data. At this time, the center of mass dynamics model and the discretized state vector are combined to predict The status update value corresponding to the moment.

[0119] Among them, the Mahalanobis distance of the new information and the given threshold The comparison can be represented by the following formula:

[0120]

[0121] Among them, combining the center of mass dynamics model and the discretized state vector, the prediction The process of updating the state value corresponding to the moment can be represented by the following formula:

[0122]

[0123] in, is a 3-row 6-column zero matrix, is a 6-row, 3-column zero matrix.

[0124] Step 9: If the Mahalanobis distance of the new information is less than or equal to the given threshold , then it is believed that The measurement data at the moment is not bad pixel data and can be filtered and smoothed.

[0125] Among them, filtering and smoothing specifically include:

[0126] In the first sub-step, the state is predicted using the following formula:

[0127]

[0128] Among them, the equation corresponding to the discretized state vector is:

[0129]

[0130] The second sub-step is to perform covariance prediction using the following formula:

[0131]

[0132] in, is the state transition matrix, is the process noise covariance matrix.

[0133] in, .

[0134] in, is the maneuvering time constant, is the target maximum acceleration, is a 6-row 6-column zero matrix, is the detection interval.

[0135] The third sub-step is to calculate the gain matrix .

[0136] in, .in, is the measurement data covariance matrix, which is obtained in the above step S9.

[0137] The fourth sub-step is to update the state estimate using the following formula:

[0138] .

[0139] The fifth sub-step is to update the covariance using the following formula:

[0140] .

[0141] The above-described various embodiments of the present disclosure have the following beneficial effects: Through the track filtering method based on measurement noise variance in a spherical coordinate system, some embodiments of the present disclosure first establish the center-of-mass dynamic equation of the aircraft in a velocity coordinate system based on the aircraft's motion characteristics and range, thereby obtaining a simplified parametric dynamic model in the spherical coordinate system. Next, a state prediction equation and a measurement equation are established, and a sliding-window method for real-time estimation of measurement noise variance is used to obtain statistical properties such as noise variance. Finally, an extended Kalman filter is introduced to perform track filtering and smoothing on the raw track data of Automatic Dependent Surveillance-Broadcast (ADS-B), an air traffic surveillance application for transmitting flight parameters. By designing a track filtering algorithm based on a spherical coordinate system and utilizing a sliding-window method for real-time estimation of measurement noise variance to obtain statistical properties such as noise variance, the present disclosure significantly reduces the computational complexity and computational complexity of algorithms in traditional rectangular coordinate systems. This method enables real-time estimation of noise variance in track measurement data, effectively improving the accuracy of aircraft position, velocity, and acceleration estimates.

[0142] The simulation experiment analysis results are as follows:

[0143] (1) Data source

[0144] The data used in the experiment is publicly available at https: / / globe.adsbexchange.com / . The following simulation experiment was conducted using flight data from a certain fighter jet on July 16, 2024, using MATLAB R2023a.

[0145] (2) Comparison of algorithms in different coordinate systems

[0146] The algorithm was validated using raw track data processed in different coordinate systems. Specifically, the algorithm's computation time in the rectangular coordinate system was 1.005136 seconds. In the spherical coordinate system, the computation time was 0.764280 seconds. This indicates that processing track data in the spherical coordinate system is significantly faster than in the rectangular coordinate system, significantly reducing the algorithm's complexity and computational effort, which is consistent with the algorithm's expectations.

[0147] (3) Comparison of acceleration estimation accuracy in different coordinate systems

[0148] Among them, using the (original) track data, the extended Kalman filter is used to perform track filtering and acceleration identification under the algorithm in different coordinate systems. The identification results are as follows Figure 7 (Comparison of acceleration identification results in rectangular coordinate system and spherical coordinate system) Figure 7 It can be seen that compared with the rectangular coordinate system, the acceleration identification estimation accuracy obtained in the spherical coordinate system is higher.

[0149] (4) Analysis of simulation results of real-time estimation of measurement data variance

[0150] Among them, based on the (original) track data, the visualization results of the longitude standard deviation obtained by the sliding window measurement noise variance adaptive estimation algorithm are as follows: Figure 8 (Schematic diagram of the process of precision standard deviation changing with time) is shown, where the analysis Figure 8 It can be seen that, through the present disclosure, the standard deviation estimation of the track measurement data noise is obtained in real time, and the extended Kalman filter can be directly used to perform track filtering and identification subsequently.

[0151] In summary, first, the disclosed track filtering method based on measurement noise variance in a spherical coordinate system provides a real-time estimate of the noise variance of track measurement data, enabling direct use of an extended Kalman filter for track filtering and smoothing. Second, processing track data in a spherical coordinate system takes less time than processing in a rectangular coordinate system, significantly reducing algorithm complexity and computational effort. Finally, compared to a rectangular coordinate system, the acceleration identification and estimation accuracy obtained in a spherical coordinate system is higher.

[0152] Further references Figure 9 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a track filtering device based on measurement noise variance in a spherical coordinate system. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the track filtering device based on measurement noise variance in a spherical coordinate system can be specifically applied to various electronic devices.

[0153] like Figure 9 As shown, in some embodiments, a track filtering device 900 based on measurement noise variance in a spherical coordinate system includes: a first determining unit 901, a second determining unit 902, a constructing unit 903, and a track filtering and characteristic parameter identification unit 904. The first determining unit 901 is configured to determine a center of mass dynamics model of an aircraft in a velocity coordinate system; the second determining unit 902 is configured to determine a state vector for the aircraft based on the center of mass dynamics model; the constructing unit 903 is configured to construct a measurement equation corresponding to the measurement data and the state vector; and the track filtering and characteristic parameter identification unit 904 is configured to perform track filtering and characteristic parameter identification on the track data using an extended Kalman filter using a sliding window method. The extended Kalman filter is constructed by combining the center of mass dynamics model and the measurement equation, and by real-time estimation of the measurement noise variance.

[0154] It can be understood that the various units recorded in the track filtering device 900 based on the measurement noise variance in the spherical coordinate system are the same as those in the reference Figure 1Therefore, the operations, features and beneficial effects described above for the method are also applicable to the track filtering device 900 based on the measurement noise variance in the spherical coordinate system and the units included therein, and will not be described in detail here.

[0155] Reference below Figure 10 , which shows a structural schematic diagram of an electronic device (eg, a computing device) suitable for implementing some embodiments of the present disclosure. Figure 10 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure. Figure 10 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions, which, when executed, may enable the processor to execute any of the above methods. The processor is used to provide computing and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium, which, when executed by the processor, may enable the processor to execute any of the above methods. The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0156] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0157] In one embodiment, the processor is used to run a computer program stored in a memory to implement the following steps: determining the center of mass dynamics model of the aircraft in a velocity coordinate system; determining a state vector for the aircraft based on the center of mass dynamics model; constructing a measurement equation corresponding to the measurement data and the state vector; and performing track filtering and characteristic parameter identification on the track data based on an extended Kalman filter using a sliding window method, wherein the extended Kalman filter is constructed by combining the center of mass dynamics model and the measurement equation, and by real-time estimation of the measurement noise variance.

[0158] An embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the method described above in the present disclosure.

[0159] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., provided on the computer device.

[0160] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0161] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A track filtering method based on measurement noise variance in spherical coordinates, characterized in that: include: Determine the center of mass dynamic model of the aircraft in the velocity coordinate system; determining a state vector for the aircraft based on the center of mass dynamics model; Constructing a measurement equation corresponding to the measurement data and the state vector; A sliding window approach is used to perform track filtering and characteristic parameter identification on the track data based on an extended Kalman filter, wherein the extended Kalman filter is constructed by combining the center of mass dynamics model and the measurement equation, as well as by real-time estimation of the measurement noise variance.

2. The method according to claim 1, characterized in that The center of mass dynamics model is characterized by the following equation: in, is the geocentric radius, is the flight speed, is the angle between the ground plane and the velocity axis, is the geocentric longitude, is the heading angle, is the geocentric latitude, The aircraft is subjected to engine thrust. is the angle between the speed axis and the body axis, is the mass of the aircraft, For resistance, is the gravitational acceleration, is the Earth's rotation angular rate, For lift, In the velocity coordinate system The angle from the axis to the total lift, is the longitudinal acceleration in the velocity coordinate system, which controls the acceleration and deceleration in the horizontal direction by the resultant acceleration. is the vertical acceleration in the velocity coordinate system, where the resultant acceleration controls the vertical acceleration of the vehicle. is the lateral acceleration in the velocity coordinate system, the resultant acceleration in the lateral direction controls the left and right steering, 、 、 、 、 、 All are intermediate variables.

3. The method according to claim 2, characterized in that The state vector can be represented by the following formula: in, represents the state vector, is the geocentric radius, is the flight speed, is the angle between the ground plane and the velocity axis, is the geocentric longitude, is the heading angle, is the geocentric latitude, is the longitudinal acceleration in the velocity coordinate system, which controls the acceleration and deceleration in the horizontal direction by the resultant acceleration. is the vertical acceleration in the velocity coordinate system, where the resultant acceleration controls the vertical acceleration of the vehicle. is the lateral acceleration in the velocity coordinate system, the resultant acceleration in the lateral direction controls the left and right steering, represents the transposed matrix; Among them, the derivative equations corresponding to the state vector can be represented by the following formula: in, represents the derivative equations corresponding to the state vector, 、 、 、 、 、 are intermediate variables. Indicates the velocity coordinate system along Axis noise, Indicates the velocity coordinate system along Axis noise, Indicates the velocity coordinate system along Axis noise.

4. The method according to claim 3, characterized in that The measurement equation is characterized by the following formula: in, Represents measurement data, represents the measurement equation, represents the state vector, where , is the geocentric longitude, is the geocentric latitude, is the heading angle, is the earth's height, is the ground speed, is the climb rate, is the average radius of the Earth.

5. The method according to claim 4, characterized in that The sliding window method is used to perform track filtering and feature parameter identification on the track data according to the extended Kalman filter, including: Determine the window size corresponding to the sliding window ; Determine the polynomial fit order ; According to the polynomial fitting order , construct polynomials; Constructing the error function ; According to the error function Solve the polynomial coefficients of the polynomial to obtain the polynomial coefficients; According to the polynomial coefficients, the theoretical value corresponding to the track data in the sliding window is determined; Determine the track error based on the theoretical value corresponding to the track data in the sliding window and the track data in the sliding window; Determine the error statistical variance and error standard deviation corresponding to the track error; According to the error statistical variance, the covariance matrix of the measurement data corresponding to the extended Kalman filter is determined.

6. A track filtering device based on measurement noise variance in spherical coordinates, characterized in that: include: A first determining unit is configured to determine a center of mass dynamics model of the aircraft in a velocity coordinate system; a second determining unit configured to determine a state vector for the aircraft based on the center of mass dynamics model; a construction unit configured to construct a measurement equation corresponding to the measurement data and the state vector; The track filtering and characteristic parameter identification unit is configured to use a sliding window method to perform track filtering and characteristic parameter identification on the track data according to an extended Kalman filter, wherein the extended Kalman filter is constructed in combination with the center of mass dynamics model and the measurement equation, and through real-time estimation of the measurement noise variance.

7. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable medium, characterized in that A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images