Ground Target Location Method Based on Time Accumulation

Abstract

The present invention relates to the technical field of ground target positioning by unmanned aerial vehicles, and particularly relates to a ground target positioning method based on time accumulation. It includes: the unmanned aerial vehicle flies along a predetermined straight flight path, and the airborne optoelectronic turret on the unmanned aerial vehicle aligns with and continuously tracks the ground target; collects target position observation data at different angles and stores the data in the airborne computer of the unmanned aerial vehicle; performs real-time processing and analysis on the collected data through a specific algorithm to identify random error patterns; eliminates errors and improves the accuracy of the positioning result by filtering and fusing the data. The advantages are: using time-accumulated data to eliminate random errors, so it has stronger resistance to environmental changes and sensor noise; improves positioning accuracy, enhances robustness, optimizes data processing, has real-time performance, strong adaptability, is easy to integrate, enhances application value and strengthens decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of ground target positioning by unmanned aerial vehicles, and particularly to a ground target positioning method based on time accumulation. Background Art

[0002] When an unmanned aerial vehicle (UAV) performs a monitoring task, the positioning accuracy of ground targets is increasingly stringent. The airborne optoelectronic turret of a UAV, as an important tool for target tracking and positioning, directly affects the efficiency and quality of the monitoring task. However, traditional UAV target positioning methods have some inherent limitations.

[0003] Although modern UAV monitoring technologies have made significant progress, the accuracy of target positioning remains a challenge. Traditional positioning methods often rely on instantaneous measurements, which may be affected by sensor errors, environmental interference, or the dynamic changes of the aircraft. As the flight time extends, these errors tend to accumulate, resulting in a decrease in positioning accuracy over time.

[0004] To address this challenge, the industry has started researching positioning methods based on time accumulation. The core idea of these methods is to continuously observe the target over a long period of time and use the data accumulated over time to gradually eliminate positioning errors. Compared with traditional methods, the positioning methods based on time accumulation can make more effective use of the large amount of data collected during flight and improve the accuracy and reliability of positioning through statistical analysis and signal processing techniques. Summary of the Invention

[0005] The present invention provides a ground target positioning method based on time accumulation to solve the above problems.

[0006] The object of the present invention is to provide a ground target positioning method based on time accumulation, which specifically includes the following steps:

[0007] S1. The UAV flies along a predetermined straight flight path, and the airborne optoelectronic turret of the UAV aligns with and continuously tracks the ground target;

[0008] S2. Collect the observed data of the target position at different angles and store the data in the airborne computer of the UAV;

[0009] S3. Perform real-time processing and analysis on the collected data to identify random error patterns; eliminate errors and improve the accuracy of the positioning result by filtering and fusing the data.

[0010] Preferably, step S1 specifically includes the following sub-steps:

[0011] S101. The UAV flies along a predetermined straight flight path. When the UAV is at the starting point of the predetermined flight path, the airborne optoelectronic turret starts to conduct initial tracking on the target, and records the azimuth angle of the optoelectronic turret at this time as A 1;

[0012] S102. When the UAV flies to the end point of the predetermined flight path, the airborne optoelectronic turret of the UAV records the azimuth angle of the optoelectronic turret at this time as A 2;

[0013] S103. Calculate the azimuth difference Δ A :

[0014] Δ A = A 2 - A 1.

[0015] Preferably, the azimuth difference Δ A is greater than or equal to 10°.

[0016] Preferably, the target position observation data includes but is not limited to the latitude and longitude coordinate data of the target, altitude information data, and relative position data with the UAV; the target position observation data is expressed as:

[0017] ;

[0018] where i represents the observation sequence, represents the observed data of the target latitude, represents the observed data of the target longitude, represents the observed data of the target altitude.

[0019] Preferably, the processing process of step S3 specifically includes the following sub-steps;

[0020] S301. Establish an observation model: Establish an observation model to associate the true position and the observed position ;

[0021] ;

[0022] where, H is the observation model matrix, is the true position of the target, n i is the noise vector of the i-th observation, is the target position observation data of the i-th observation;

[0023] Assume that the observation model matrix H is invertible, then the true position of the target can be expressed as: ;

[0024] S302. Establish an error model: Assume that the noise vector n i is a Gaussian distribution with zero mean, and its covariance matrix is Cn i ;

[0025] S303. Use the weighted least squares method to define a cost function to minimize the observation error. By taking the derivative of with respect to and setting the derivative to zero, the normal equation is obtained;

[0026] The cost function has the following expression:

[0027] ;

[0028] ;

[0029] The normal equation is as follows:

[0030] ;

[0031] S304. Solve the normal equation to obtain the optimal estimate :

[0032] ;

[0033] S305. In a dynamic environment, use the Kalman filter to fuse a series of observation data to further improve the estimation of the target position; the target position prediction formula of the Kalman filter is:

[0034] ;

[0035] Among them, is the optimal estimate at the current moment, is the optimal estimate based on the observations at the previous moment, K k is the Kalman gain, is the observation data at the current moment, H k is the observation model at the current moment.

[0036] Preferably, the calculation formula of the Kalman gain K k is as follows:

[0037] ;

[0038] Among them, is the covariance matrix of the current prediction error, is the observation model at the current time H k is the transpose of, is the observation noise covariance matrix.

[0039] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0040] 1. Improve positioning accuracy: The present invention continuously tracks the target on a straight flight path and gradually eliminates the positioning error by using the data accumulated over time, significantly improving the accuracy of target positioning. This method is particularly suitable for long-term monitoring tasks and can ensure that the accuracy of the positioning result is continuously improved over time.

[0041] 2. Enhance robustness: Since the present invention uses the data accumulated over time to eliminate random errors, it has stronger resistance to environmental changes and sensor noise. This enables the unmanned aerial vehicle to maintain stable positioning performance in complex or changing environments.

[0042] 3. Optimize data processing: The data processing algorithm of the present invention is specifically designed to process the data accumulated over a long time, can effectively screen and integrate key information, reduce noise interference, and thus improve the efficiency and accuracy of data processing.

[0043] 4. Real-time performance: Although the amount of data processed by the present invention is large, through the optimized algorithm design, the real-time performance of data processing is ensured, meeting the requirements of the unmanned aerial vehicle for real-time tracking and positioning of the target during flight.

[0044] 5. Strong adaptability: The method of the present invention does not depend on specific environmental conditions or target characteristics, has good universality, and can adapt to different application scenarios and target types.

[0045] 6. Easy to integrate: The technical solution of the present invention can be easily integrated into the existing unmanned aerial vehicle monitoring system, providing an upgrade of high-precision positioning ability for the existing system without large-scale hardware modification or replacement.

[0046] 7. Enhance application value: By improving the positioning accuracy and reliability of the unmanned aerial vehicle, the present invention can significantly enhance the application value of the unmanned aerial vehicle in the fields of military reconnaissance, environmental monitoring, traffic management, agricultural mapping, emergency rescue, etc., providing more accurate and reliable services for users.

[0047] 8. Enhance decision-making support: The accurate target positioning information provides strong support for command decision-making, enabling decision-makers to make rapid responses based on reliable data, especially in situations such as emergency rescue and disaster response. Description of the Drawings

[0048] Figure 1 It is a flowchart of a ground target positioning method based on time accumulation provided by an embodiment of the present invention. Specific implementation manners

[0049] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are denoted by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, their detailed descriptions will not be repeated.

[0050] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention.

[0051] See Figure 1 , the present invention provides a ground target positioning method based on time accumulation, which specifically includes the following steps:

[0052] S1. The unmanned aerial vehicle (UAV) flies along a predetermined straight flight path, and the airborne optoelectronic turret on the UAV aligns with and continuously tracks the ground target; specifically, it includes the following sub-steps:

[0053] S101. The UAV flies along a predetermined straight flight path. When the UAV is at the starting point of the predetermined flight path, the airborne optoelectronic turret starts to perform initial tracking on the target, and records the azimuth angle of the optoelectronic turret at this time as A 1;

[0054] S102. When the UAV flies to the end point of the predetermined flight path, the airborne optoelectronic turret on the UAV records the azimuth angle of the optoelectronic turret at this time as A 2;

[0055] S103. Calculate the difference Δ A in the azimuth angles of the optoelectronic turret at the starting point and the end point of the flight path, that is:

[0056] Δ A = A 2 - A 1;

[0057] According to the requirements of the present invention, the azimuth angle difference Δ A should be greater than or equal to 10°, which means that the optoelectronic turret observes the target within a relatively large angular range, ensuring that the continuous tracking of the target on the straight flight path covers a sufficient angular change, and collecting sufficient positioning data to increase the redundancy of the observation, which helps to improve the positioning accuracy through data fusion technology.

[0058] S2. Collect the observation data of the target position at different angles and store the data in the airborne computer of the UAV;

[0059] The target position observation data includes, but is not limited to, the latitude and longitude coordinate data of the target, altitude information data, and relative position data with the UAV; the target position observation data is expressed as:

[0060] ;

[0061] where i represents the observation sequence, represents the observation data of the target latitude, represents the observation data of the target longitude, represents the observation data of the target altitude.

[0062] S3. Perform real-time processing and analysis on the collected data to identify random error patterns; eliminate errors and improve the accuracy of the positioning result by filtering and fusing the data; the algorithms in this step include establishing an observation model, establishing an error model, weighted least squares method, Kalman filter, and data filtering and fusion algorithms; these algorithms work together to identify and eliminate random errors, thereby improving the accuracy of ground target positioning. The processing process specifically includes the following sub-steps;

[0063] S301. Establish an observation model: Establish an observation model to associate the true position and the observation position ;

[0064] ;

[0065] where, H is the observation model matrix, is the true position of the target, n i is the noise vector of the i-th observation, is the target position observation data of the i-th observation;

[0066] In order to recover the true position of the target from the observation data, the inverse operation of the observation model is required; assuming that the observation model matrix H is invertible, then the true position of the target can be expressed as:

[0067] ;

[0068] S302. Establish an error model: Assume that the noise vector n i is a Gaussian distribution with zero mean, then its covariance matrix is Cn i ;

[0069] S303. Define the cost function using the weighted least squares method to minimize the observation error by with respect to taking the derivative and setting it to zero to obtain the normal equation;

[0070] The cost function has the following expression:

[0071] ;

[0072] ;

[0073] The normal equation is as follows:

[0074] ;

[0075] S304. Solve the normal equation to obtain the optimal estimate :

[0076] ;

[0077] S305. In a dynamic environment, use the Kalman filter to fuse a series of observation data to further improve the estimation of the target position; the target position prediction formula of the Kalman filter is:

[0078] ;

[0079] where is the optimal estimate at the current time, is the optimal estimate based on the observations at the previous time, K k is the Kalman gain, is the observation data at the current time, H k is the observation model at the current time, that is, the final target positioning result after improving the positioning accuracy at the current time;

[0080] The Kalman gain K k has the following calculation formula:

[0081] ;

[0082] where is the covariance matrix of the current prediction error, is the transpose of the observation model H k at the current time, is the observation noise covariance matrix.

[0083] In the present invention, the unmanned aerial vehicle (UAV) flies along a predetermined straight flight path, and the airborne optoelectronic turret aligns with and continuously tracks a ground target. Positioning data of the target is collected at different angles, including longitude and latitude coordinates, altitude information, and the relative position with respect to the UAV. This data is stored in the airborne computer of the UAV and is processed and analyzed in real time through specific algorithms to identify and eliminate random error patterns. The key technology lies in calculating the difference in the azimuth angles of the optoelectronic turret at the starting and ending points of the flight path to ensure that the azimuth angle difference should be greater than or equal to 10°, so as to cover sufficient changes in the positioning data. In addition, the present invention defines a cost function using the weighted least squares method, solves for the optimal estimate through the normal equation, and applies a Kalman filter to fuse the observation data to further improve the stability and reliability of the positioning result.

[0084] The technical solution of the present invention has beneficial effects such as improving positioning accuracy, enhancing robustness, optimizing data processing, real-time performance, strong adaptability, easy integration, enhancing application value, and strengthening decision support. These advantages enable the present invention to have a wide range of application prospects in the fields of military reconnaissance, environmental monitoring, traffic management, agricultural mapping, emergency rescue, etc.

[0085] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the disclosure of the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present invention can be achieved. No limitations are imposed herein.

[0086] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A ground target positioning method based on time accumulation, characterized in that: The specific steps include: S1. The UAV flies along a predetermined straight line, and the UAV's onboard optoelectronic turret is aimed at and continuously tracks the ground target; S2. Collect the target position observation data at different angles and store the data in the onboard computer of the drone; the target position observation data includes but is not limited to the target's latitude and longitude coordinate data, altitude information data and relative position data with the drone; the target position observation data It is expressed as: ； Among them, i represents the observation sequence, represents the observation data of the target latitude, represents the observation data of the target longitude, Observation data representing the target height; S3. Process and analyze the collected data in real time to identify random error patterns; eliminate errors and improve the accuracy of positioning results by filtering and fusing the data; the algorithm includes establishing an observation model, establishing an error model, weighted least squares method, Kalman filter, and data filtering and fusion algorithm; the processing process specifically includes the following sub-steps; S301. Establishing an observation model: Establishing an observation model to associate with the real position and observation location ; ； in, H is the observation model matrix, is the target's true position, n i is the noise vector of the ith observation, is the target position observation data of the i-th observation; Assume that the observation model matrix H is reversible, then the true position of the target can be expressed as: ; S302. Establish error model: Assume that the noise vector n i is a Gaussian distribution with zero mean, then its covariance matrix is Cn i ; S303. Define the cost function using weighted least squares method To minimize the observation error, about Taking the derivative and setting it equal to zero, we obtain the normal equation; Cost Function The expression is as follows: ； ； The normal equation is as follows: ； S304. Solve the normal equation to get the optimal estimate : ； S305. Use the Kalman filter to fuse a series of observation data in a dynamic environment to further improve the estimation of the target position; the target position prediction formula of the Kalman filter is: ； in, is the best estimate at the current moment, is the optimal estimate based on the observation at the previous moment, K k is the Kalman gain, is the observation data at the current moment, H k is the observation model at the current moment.

2. The method for locating a ground target based on time accumulation according to claim 1, characterized in that: The step S1 specifically includes the following sub-steps: S101. The UAV flies along the predetermined straight line. When the UAV is at the starting point of the predetermined line, the onboard optoelectronic turret begins to initially track the target and records the azimuth of the optoelectronic turret at this time as A 1; S102. When the UAV flies to the end of the predetermined route, the UAV's onboard optoelectronic turret records the azimuth of the optoelectronic turret at this time as A 2; S103. Calculate the azimuth difference Δ between the photoelectric turret at the route start point and the route end point A : D A = A 2- A 1.

3. A ground target positioning method based on time accumulation according to claim 2, characterized in that: The azimuth difference Δ A Greater than or equal to 10°.

4. The method for locating a ground target based on time accumulation according to claim 1, characterized in that: Kalman Gain K k The calculation formula is as follows: ； in, is the covariance matrix of the current forecast error, is the observation model at the current moment H k The transpose of is the observation noise covariance matrix.

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