Low-altitude aircraft dynamic trajectory data compression transmission and prediction method

Abstract

The invention discloses a low-altitude aircraft dynamic trajectory data compression transmission and prediction method, relates to the technical field of low-altitude aircraft wireless communication, and aims to solve the problems that in the prior art, it is difficult to efficiently compress low-altitude aircraft dynamic trajectory data, reliably transmit the data and accurately predict the trajectory. The method comprises the following steps: S1, performing feature extraction and compression processing on dynamic trajectory data of a low-altitude aircraft, wherein the compression processing adopts a compression method based on spatial-temporal correlation to remove redundant data; s2, transmitting compressed data to the command and dispatch center by using a Beidou satellite system through a relay mode that the command and dispatch center configures a Beidou command type user machine and the low-altitude aircraft configures a subordinate Beidou user machine; and S3, combining a precise time service function of a Beidou system. The method has the advantages that the transmission efficiency is improved, and the future trajectory of the aircraft is accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication for low-altitude aircraft, and more particularly to a method for compressing, transmitting and predicting dynamic trajectory data of low-altitude aircraft. Background Art

[0002] With the popularization of low-altitude aircraft applications, in addition to the further expansion of traditional application areas, the demand for private and official low-altitude aircraft continues to grow. At the same time, the application of low-altitude aircraft in fire rescue, maritime first aid, anti-smuggling and anti-drug, firefighting, medical assistance and other official services is also rapidly expanding.

[0003] Currently, low-altitude aircraft usually communicate with control centers using a combination of very high frequency (VHF), high frequency (HF) and wireless network communications. VHF communication is too short-range and greatly affected by flight altitude; HF communication has poor communication quality due to unstable shortwave signals and mutual interference between radio stations; and wireless networks are restricted by geographical conditions and cannot be used in forests, mountainous areas and other areas where mobile networks cannot cover.

[0004] With the opening of the BeiDou-2 system, although it can ensure the continuity of communication for low-altitude aircraft and provide more direct and effective monitoring and command and dispatch for the using departments, the frequency of civil BeiDou cards is high and the short message capacity is limited, and the amount of transmitted data is severely restricted. In this case, how to efficiently compress the dynamic trajectory data of low-altitude aircraft and transmit it reliably while accurately predicting the trajectory has become an urgent problem to be solved. In view of this, we propose a method for compressing, transmitting and predicting the dynamic trajectory data of low-altitude aircraft. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for compressing, transmitting and predicting the dynamic trajectory data of low-altitude aircraft, aiming to solve the problem that the existing technology is difficult to efficiently compress the dynamic trajectory data of low-altitude aircraft and transmit it reliably, while accurately predicting the trajectory.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft, the method comprising the following steps; S1. Extracting features and compressing the dynamic trajectory data of the low-altitude aircraft, wherein the compression process uses a compression method based on spatiotemporal correlation to remove redundant data; S2. The command and dispatch center is equipped with a Beidou command user terminal, and low-altitude aircraft are equipped with subordinate Beidou user terminals to transmit compressed data to the command and dispatch center using the Beidou satellite system. S3. Then, by combining the precise timing function of the BeiDou system and utilizing real-time collected flight data and historical trajectory data, a linear extrapolation prediction model is used to analyze the aircraft's motion trends and predict future trajectories.

[0007] Preferably, in the above step S1, the feature extraction includes extracting flight altitude, speed, acceleration, and latitude and longitude coordinate information.

[0008] Preferably, in the above step S1, the judgment formula Is it established to determine the spatiotemporal correlation, where For trajectory points and The spatial distance, is the error coefficient, is the average flight speed. When this condition is met, it indicates that the two points have temporal and spatial correlation and can be compressed to reduce data redundancy.

[0009] Preferably, in the above step S2, during the data transmission process, a single machine and multiple cards are rotated for continuous positioning, which reduces the positioning frequency to within 10 seconds, increases the number of positioning points, and reduces the error.

[0010] Preferably, in the above step S2, the Beidou command user terminal can accommodate 100-1000 user terminals, realizing position monitoring, broadcasting, timing, trajectory playback and data management functions.

[0011] Preferably, in the above step S3, the prediction model formula is , where For prediction Time position, is the current location, is the current speed, is the current acceleration, is the prediction time interval.

[0012] Preferably, in the above step S2, the compression method further comprises the following steps: S1.1. Use an autoregressive model to analyze temporally adjacent trajectory point data, using historical trajectory point data to predict the current trajectory point data value. For trajectory data time series with strong autocorrelation, calculate the residual between the predicted value and the actual value and encode and store the residual to reduce redundant information in the time dimension. S1.2. Divide the aircraft's flight area into multiple spatial blocks. When adjacent trajectory points are determined to be within the same spatial block or adjacent spatial blocks and satisfy spatiotemporal correlation conditions, use spatial correlation to further reduce data redundancy. S1.3. Construct a trajectory data feature dictionary and store common trajectory data feature patterns in the dictionary. During the compression process, for the trajectory data parts that match the patterns in the dictionary, directly reference the index in the dictionary to reduce the amount of data storage.

[0013] Preferably, in the above step S1.1, the current trajectory point is , its autoregressive prediction model can be expressed as , where is the autoregressive coefficient, For the past Trajectory point data of time steps, is the prediction residual.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention removes redundant data through a compression method based on spatiotemporal correlation, effectively reducing the amount of data and improving transmission efficiency. At the same time, combined with Beidou's precise timing function and linear extrapolation prediction model, it achieves accurate prediction of the aircraft's future trajectory, meeting the urgent needs of low-altitude aircraft in communication transmission and trajectory prediction.

[0015] 2. In data compression processing, the present invention uses an autoregressive model to analyze the data of temporally adjacent trajectory points. By calculating residuals and encoding and storing them, redundancy in the time dimension is reduced. The flight area is divided into spatial blocks, and spatial correlation is used to further reduce data redundancy. A trajectory data feature dictionary is constructed, and dictionary indexes are referenced to reduce data storage volume. The multi-dimensional compression method significantly improves data compression efficiency.

[0016] 3. During the data transmission process, the present invention adopts a single-machine multi-card rotation continuous positioning method to reduce the positioning frequency to within 10 seconds, increase the number of positioning points, and reduce errors. At the same time, the Beidou command user terminal can accommodate 100-1000 user terminals, realizing functions such as position monitoring, broadcasting, timing, trajectory playback and data management, thereby ensuring the reliability of data transmission and the accuracy of positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Example 1 like Figure 1 As shown, a method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft comprises the following steps: S1. Feature extraction and compression processing are performed on the dynamic trajectory data of low-altitude aircraft. The compression processing uses a compression method based on spatiotemporal correlation to remove redundant data, extract key features, and compress the data using spatiotemporal correlation, reducing the data volume, lowering the transmission bandwidth requirements, and improving data transmission efficiency while retaining key trajectory information. S2. By deploying Beidou command-type user terminals at the command and dispatch center and deploying subordinate Beidou user terminals on low-altitude aircraft, the Beidou satellite system is used to transmit compressed data to the command and dispatch center. By leveraging the Beidou satellite system's relay transmission, stable data transmission is achieved in low-altitude environments, particularly in areas with poor signal coverage, ensuring data transmission reliability. S3. Then, by combining the precise timing function of the Beidou system, using real-time collected flight data and historical trajectory data, a linear extrapolation prediction model is used to analyze the aircraft's motion trends and predict future trajectories. By utilizing the high-precision time reference of Beidou timing, combined with real-time and historical data to predict trajectories, the aircraft's motion trends can be grasped in advance, providing decision support for command and dispatch, and improving the safety and efficiency of low-altitude flight management.

[0020] Among them, in the above step S1, feature extraction includes extracting flight altitude, speed, acceleration, and latitude and longitude coordinate information to extract these key motion parameters, comprehensively characterize the dynamic trajectory characteristics of the aircraft, and provide accurate basic data for subsequent compression processing and trajectory prediction.

[0021] Among them, in the above step S1, by judging the formula Is it established to determine the spatiotemporal correlation, where For trajectory points and The spatial distance, is the error coefficient, is the average flight speed. When this condition is met, it indicates that the two points have temporal and spatial correlation and can be compressed to reduce data redundancy.

[0022] Among them, in the above-mentioned step S2, during the data transmission process, the single-machine multi-card rotation continuous positioning method is adopted to reduce the positioning frequency to within 10 seconds, increase the number of positioning points, and reduce the error. The single-machine multi-card rotation positioning method is used to increase the positioning frequency and increase the number of positioning points, thereby reducing the positioning error and improving the accuracy of the trajectory data, so that the transmitted data can more accurately reflect the actual position of the aircraft.

[0023] Among them, in the above step S2, the Beidou command user terminal can accommodate 100-1000 user terminals, realizing position monitoring, broadcasting, timing, trajectory playback and data management functions. The large-capacity user terminal support capability of the Beidou command user terminal can meet the simultaneous monitoring and management needs of multiple low-altitude aircraft. Its multiple functions provide the command and dispatch center with comprehensive aircraft status information and data management means.

[0024] Among them, in the above step S3, the prediction model formula is , where For prediction Time position, is the current location, is the current speed, is the current acceleration, To predict the time interval, the current position, speed and acceleration are combined with the predicted time interval to simply and quickly predict the future position of the aircraft, providing a basis for real-time monitoring and early warning. It is suitable for scenarios where the aircraft's motion state is relatively stable.

[0025] In the above step S2, the compression method further includes the following steps: S1.1. Use an autoregressive model to analyze temporally adjacent trajectory point data, using historical trajectory point data to predict the current trajectory point data value. For trajectory data time series with strong autocorrelation, calculate the residual between the predicted value and the actual value and encode and store the residual to reduce redundant information in the time dimension. S1.2. Divide the aircraft's flight area into multiple spatial blocks. When adjacent trajectory points are determined to be within the same spatial block or adjacent spatial blocks and satisfy spatiotemporal correlation conditions, use spatial correlation to further reduce data redundancy. S1.3. Construct a trajectory data feature dictionary and store common trajectory data feature patterns in the dictionary. During the compression process, for the trajectory data parts that match the patterns in the dictionary, directly reference the index in the dictionary to reduce the amount of data storage.

[0026] Among them, in the above step S1.1, the current trajectory point is , its autoregressive prediction model can be expressed as , where is the autoregressive coefficient, For the past Trajectory point data of time steps, is the prediction residual.

[0027] The embodiments disclosed in the present invention are preferred embodiments, but are not limited to them. Ordinary technicians in this field can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. As long as they do not deviate from the spirit of the present invention, they are all within the scope of protection of the present invention.

Claims

1. A method for compressing, transmitting and predicting dynamic trajectory data of low-altitude aircraft, characterized in that: The method The following steps are included: S1. Extracting features and compressing the dynamic trajectory data of the low-altitude aircraft, wherein the compression process uses a compression method based on spatiotemporal correlation to remove redundant data; S2. The command and dispatch center is equipped with a Beidou command user terminal, and low-altitude aircraft are equipped with subordinate Beidou user terminals to transmit compressed data to the command and dispatch center using the Beidou satellite system. S3. Then, by combining the precise timing function of the BeiDou system and utilizing real-time collected flight data and historical trajectory data, a linear extrapolation prediction model is used to analyze the aircraft's motion trends and predict future trajectories.

2. A method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 1, characterized in that: In the above step S1, the feature extraction includes extracting flight altitude, speed, acceleration, and latitude and longitude coordinate information.

3. The method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 1, characterized in that: In the above step S1, the formula Is it established to determine the spatiotemporal correlation, where For trajectory points and The spatial distance, is the error coefficient, is the average flight speed. When this condition is met, it indicates that the two points have temporal and spatial correlation and can be compressed to reduce data redundancy.

4. The method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 1, characterized in that: In the above step S2, during the data transmission process, a single machine and multiple cards are rotated for continuous positioning, which reduces the positioning frequency to within 10 seconds, increases the number of positioning points, and reduces the error.

5. The method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 1, characterized in that: In the above step S2, the Beidou command user terminal can accommodate 100-1000 user terminals to realize position monitoring, broadcasting, timing, trajectory playback and data management functions.

6. The method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 1, characterized in that: In the above step S3, the prediction model formula is , where For prediction Time position, is the current location, is the current speed, is the current acceleration, is the prediction time interval.

7. The method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 1, characterized in that: In the above step S2, the compression method further comprises the following steps: S1.

1. Use an autoregressive model to analyze temporally adjacent trajectory point data, using historical trajectory point data to predict the current trajectory point data value. For trajectory data time series with strong autocorrelation, calculate the residual between the predicted value and the actual value and encode and store the residual to reduce redundant information in the time dimension. S1.

2. Divide the aircraft's flight area into multiple spatial blocks. When adjacent trajectory points are determined to be within the same spatial block or adjacent spatial blocks and satisfy spatiotemporal correlation conditions, use spatial correlation to further reduce data redundancy. S1.

3. Construct a trajectory data feature dictionary and store common trajectory data feature patterns in the dictionary. During the compression process, for the trajectory data parts that match the patterns in the dictionary, directly reference the index in the dictionary to reduce the amount of data storage.

8. A method for compressing, transmitting and predicting dynamic trajectory data of a low-altitude aircraft according to claim 7, characterized in that: In the above step S1.1, the current trajectory point is , its autoregressive prediction model can be expressed as , where is the autoregressive coefficient, For the past Trajectory point data of time steps, is the prediction residual.

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