Ground target positioning method based on time accumulation

Abstract

The invention relates to the technical field of ground target positioning by an unmanned aerial vehicle, in particular to a ground target positioning method based on time accumulation. Comprising the steps that the unmanned aerial vehicle flies according to a preset linear route, and an airborne photoelectric turret of the unmanned aerial vehicle aligns to and continuously tracks a ground target; collecting target position observation data at different angles, and storing the data in an airborne computer of the unmanned aerial vehicle; performing real-time processing and analysis on the collected data through a specific algorithm to identify a random error mode; by filtering and fusing the data, errors are eliminated, and the precision of a positioning result is improved. The method has the advantages that random errors are eliminated by adopting time accumulation data, so that the method has higher resistance to environmental change and sensor noise; the method improves positioning precision, enhances robustness, optimizes data processing and real-time performance, is high in adaptability, is easy to integrate, improves application value, and enhances decision support.

Description

Technical Field

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

[0002] When UAVs perform monitoring tasks, the positioning accuracy requirements for ground targets are becoming increasingly stringent. As an important target tracking and positioning tool, the performance of the UAV-mounted optoelectronic turret directly affects the efficiency and quality of the monitoring task. However, traditional UAV target positioning methods have some inherent limitations.

[0003] Although modern drone surveillance technology has made significant progress, the accuracy of target positioning remains a challenge. Traditional positioning methods often rely on instantaneous measurements that may be affected by sensor errors, environmental interference, or dynamic changes in the aircraft. These errors tend to accumulate as the flight time increases, causing the positioning accuracy to degrade over time.

[0004] To meet this challenge, the industry has begun to study positioning methods based on time accumulation. The core idea of ​​these methods is to gradually eliminate positioning errors by continuously observing the target over a long period of time and using the accumulated data. Compared with traditional methods, positioning methods based on time accumulation can more effectively utilize the large amount of data collected during the flight, and improve the accuracy and reliability of positioning through statistical analysis and signal processing technology. Summary of the invention

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

[0006] The present invention aims to provide a ground target positioning method based on time accumulation, which specifically comprises the following steps: 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 observation data of the target position at different angles and store the data in the onboard computer of the UAV; 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.

[0007] Preferably, 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 : Δ A = A 2- A 1.

[0008] Preferably, the azimuth difference Δ A Greater than or equal to 10°.

[0009] Preferably, 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 respect to the drone; the target position observation data It is expressed as: ; Where 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.

[0010] Preferably, the processing process of step S3 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.

[0011] Preferably, the 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.

[0012] Compared with the prior art, the present invention can achieve the following beneficial effects: 1. Improve positioning accuracy: This invention significantly improves the accuracy of target positioning by continuously tracking the target on a straight line and gradually eliminating positioning errors using data accumulated over time. This method is particularly suitable for long-term monitoring tasks and can ensure that the accuracy of positioning results is continuously improved over time.

[0013] 2. Enhanced robustness: Since the present invention uses time-accumulated data to eliminate random errors, it has stronger resistance to environmental changes and sensor noise. This allows the drone to maintain stable positioning performance in complex or changing environments.

[0014] 3. Optimize data processing: The data processing algorithm of the present invention is specially designed to process data accumulated over a long period of time, and can effectively screen and integrate key information, reduce noise interference, and thus improve the efficiency and accuracy of data processing.

[0015] 4. Real-time performance: Although the present invention processes a large amount of data, the real-time performance of data processing is ensured through optimized algorithm design, meeting the needs of real-time tracking and positioning of targets by UAVs during flight.

[0016] 5. Strong adaptability: The method of the present invention does not rely on specific environmental conditions or target characteristics, has good universality, and can adapt to different application scenarios and target types.

[0017] 6. Easy to integrate: The technical solution of the present invention can be easily integrated into the existing UAV monitoring system, providing an upgrade of the high-precision positioning capability for the existing system without large-scale hardware modification or replacement.

[0018] 7. Improve application value: By improving the positioning accuracy and reliability of drones, the present invention can significantly improve the application value of drones in military reconnaissance, environmental monitoring, traffic management, agricultural surveying and mapping, emergency rescue and other fields, and provide users with more accurate and reliable services.

[0019] 8. Enhanced decision support: Accurate target positioning information provides strong support for command decisions, enabling decision makers to respond quickly based on reliable data, especially in situations such as emergency rescue and disaster response. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention provides a flow chart of a method for ground target positioning based on time accumulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are represented by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, the detailed description thereof will not be repeated.

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0023] See also Figure 1 The present invention provides a ground target positioning method based on time accumulation, which specifically includes the following steps: 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; specifically, the following sub-steps are included: 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 difference Δ between the azimuth angles of the photoelectric turret at the route start point and the route end point A ,Right now: Δ A = A 2- A 1; According to the requirements of the present invention, the azimuth difference Δ A It should be greater than or equal to 10°, which means that the optoelectronic turret observes the target within a larger angle range, ensuring continuous tracking of the target on a straight route, covering sufficient viewing angle changes, and collecting sufficient positioning data to increase the redundancy of observation, which helps to improve positioning accuracy through data fusion technology.

[0024] S2. Collect observation data of the target position at different angles and store the data in the onboard computer of the UAV; Target location 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; Target location 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.

[0025] 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 in this step includes establishing an observation model, establishing an error model, weighted least squares method, Kalman filter, and data filtering and fusion algorithm; 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; 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; In order to recover the true position of the target from the observation data, it is necessary to inverse the observation model; assuming 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, that is, the final target positioning result after improving the positioning accuracy at the current moment; 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.

[0026] In the present invention, the UAV flies along a predetermined straight route, and the onboard optoelectronic turret is aligned with and continuously tracks the ground target. The positioning data of the target is collected at different angles, including latitude and longitude coordinates, altitude information, and relative position to the UAV. These data are stored in the onboard computer of the UAV and are processed and analyzed in real time through a specific algorithm to identify and eliminate random error patterns. The key technology is to ensure that the azimuth difference should be greater than or equal to 10° by calculating the difference in the azimuth of the optoelectronic turret at the starting point and the end point of the route, thereby covering sufficient positioning data changes. In addition, the present invention uses the weighted least squares method to define the cost function, solves the optimal estimate through the normal equation, and applies the Kalman filter to fuse the observation data, so as to further improve the stability and reliability of the positioning results.

[0027] The technical solution of the present invention has the beneficial effects of improving positioning accuracy, enhancing robustness, optimizing data processing, real-time performance, strong adaptability, easy integration, improving application value and enhancing decision support. These advantages make the present invention have broad application prospects in the fields of military reconnaissance, environmental monitoring, traffic management, agricultural mapping, emergency rescue, etc.

[0028] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0029] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in 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 observation data of the target position at different angles and store the data in the onboard computer of the UAV; 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.

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: 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.

5. The method for locating a ground target based on time accumulation according to claim 1, characterized in that: The algorithm in step S3 includes establishing an observation model, establishing an error model, weighted least squares method, Kalman filter, and data filtering and fusion algorithm.

6. A ground target positioning method based on time accumulation according to claim 5, characterized in that: The processing process of step S3 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.

7. A ground target positioning method based on time accumulation according to claim 6, 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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